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lIPZ`Y`0_0W`X`@```apa`aPa`apa0b`a5a6a@4a4a FaEaEaEa0ba Va@VapaWaPa`apaaaaaaZb0ebPeb\bdbZb nb ]b\b\bZb nb0nb0b@`ubpxbpaUnknown 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 PjPjPjPjCan'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 position00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899...\\?\\/INVALID_ARGUMENT:, map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.Stacktrace: Cpuai.onnx.mlai.onnx.preview.trainingai.onnx.trainingVIWEF!=D:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.hD:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)CPUExecutionProviderelem_type_ != nullptrTensor sequence must contain only primitive typesD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::Geti < tensors_.size()Trying to get a Tensor, but got: OrtValue::GetIsTensor()OrtValue::GetMutableD:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hIsTensorSequence()Trying to get a TensorSeq, but got: IsSparseTensor()Trying to get a SparseTensor, but got: ORT_LOAD_CONFIG_FROM_MODELInteger overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowsize overflowtried creating tensor with negative value in shapenot enough space: expected , got `anonymous-namespace'::GetDataTransferNot able to find appropriate IDataTransfer to copy sparse dataStrings can only reside in CPU memoryD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shape`anonymous-namespace'::ValidateFillInputArgsOrtApis::FillSparseTensorCsrOrtApis::FillSparseTensorCooOrtApis::FillSparseTensorBlockSparsetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::UseCooIndicesCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::UseBlockSparseIndicesOrtApis::UseCsrIndicesUse GetStringTensor*() API to retrieve stringsthe ort_value must contain a constructed tensorRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyonnxruntime_profile_lengths allocation failedoutput name cannot be emptyOutput buffer allocation failedstring buffer allocation failedelement index is out of boundsinput array doesn't equal tensor sizeSparse Tensor does not contain sparse dataOrtValue should contain a Tensor or a Sparse Tensorshape is invalidThis API supports Tensors or SparseTensorsoffsets buffer is not equal to tensor sizeindex is out of boundsbuffer size is too small for string elementoutput buffer is too small. Use GetStringTensorDataLength.internal errorout of indexInput is not of one of the supported sequence types.index out of rangeinput array is too shortInput is not of type sequence or map.Expecting all elements to be tensors. Got: Input is not of one of the supported map types.OrtCreateValueImplSeqHelperin[idx]->IsTensor()Each element of the sequence should be either tensor or map.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported input typeAt least one element in the sequence is of a type different from others.Either the key tensor or the value tensor has NumDimensions > 1For map type num_values MUST be 2Key type is not supported yet.Key and value tensors have unequal number of elements.opaque(Number of values should be at least 1.Specified domain and type names combination does not refer to a registered opaque type,OrtApis::CreateOpaqueValueml_type != nullptrnon_tensor_base != nullptrOpaque type is not a non_tensor type!!!Specified provider is not supported.OrtApis::GetOpaqueValuethis API does not support stringsinvalid location rangelocation dimensions do not match shape sizearena_extend_strategymax_memmax_dead_bytes_per_chunkinitial_chunk_size_bytesInvalid key found: initial_growth_chunk_size_bytes1.12.0The 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::DataAsSpanvalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelperMap is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::check++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,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 >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::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 > >,class std::allocator,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscript != len >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::DataTypeImpl::GetType() == type_OrtCreateMapMLValueonnxruntime::Tensor::Datanot implementedgraph_optimization_level is not validexecution_mode is not validInput index: contains null pointersProvider options key/value cannot be empty execution provider is not supported in this build. Maximum string length for a provider options key/value is 1024.XNNPACKSNPEUnknown provider name. Currently supported values are 'SNPE' and 'XNNPACK' is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'custom op registered at runtimeD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInputTThere 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.all typesonnxruntime::CreateCustomRegistrytype_id_counter == 1OutputT0D:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggeronnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size (onnxruntime::TensorSeq::AddTensorSeq: tensor to be added has a different data type.IsSameDataType(tensor)Required output at index output_ptr is not present.Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::RequiredOutputD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.honnxruntime::OpKernelContext::OutputD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.ccProvider_SetHostonnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::EnsureGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dllonnxruntime_providers_cuda.dllonnxruntime_providers_rocm.dllonnxruntime::TryGetProviderInfo_CUDAonnxruntime_providers_tensorrt.dllonnxruntime_providers_migraphx.dllonnxruntime::TryGetProviderInfo_ROCMSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryinvalid unordered_map keyMissing Input: input_ptronnxruntime::OpKernelContext::RequiredInputonnxruntime::OpKernelContext::InputRequired input at index Compute_Create_State_Release_State_, expect: invalid node input count: kernel already mapped to existing nodematching node is missingarg_num < static_cast(input_count_)onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsinvalid node output countinvalid arg_num.onnxruntime::standalone::CreateOpstandalone_D:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.ccAttribute type not supported yet.onnxruntime::standalone::InvokeOpProvided allocator is nullOrtMemoryInfo is nullEnv is nullNo requested allocator availableProvided OrtMemoryInfo is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.env_ptr == p_instance_.get()D:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputmapped_feed_names_.size() == feed_names_.size()Size mismatch: index= it.second=Size mismatchonnxruntime::IOBinding::SynchronizeOutputsonnxruntime::IOBinding::SynchronizeInputsonnxruntime::SyncProvidersp_provideronnxruntime::IOBinding::BindOutputImplmapped_output_names_.size() == output_names_.size()bad cast: stoi argument out of rangeinvalid stoi argument]Device:[DeviceType: MemoryType: DeviceId:name: id: OrtMemType: OrtAllocatorType:OrtMemoryInfo:[CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProvider has already been registered.D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider D:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoIsGraphCaptured()D:\a\_work\1\s\onnxruntime\core/session/inference_session.hCached EP instance for graph replay is not set yet before calling ReplayGraph()onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph4Provided type is not an optional tensorProvided type is not an optional sequence tensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensorIsOptionalTensor(type)onnxruntime::utils::GetElementTypeFromOptionalSeqTensorIsOptionalSeqTensor(type)ORTMMemcpyTransformer[json.exception.] /session.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.set_denormal_as_zerosession.use_ort_model_bytes_directlysession.intra_op.allow_spinningsession.inter_op.allow_spinningsession.dynamic_block_baseoptimization.minimal_build_optimizationsmemory.enable_memory_arena_shrinkagesession.strict_shape_type_inferencesession.force_spinning_stopapply value of 'save' is only valid when saving an ORT format model.save%Y-%m-%d_%H-%M-%SThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: Invalid value for ModelProto needs to be parsed to check for ORT config within itD:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configFlush-to-zero and denormal-as-zero are offonis_model_proto_parsedonnxruntime::InferenceSession::ConstructorCommonstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()-intra-op-Creating and using per session threadpools since use_per_session_threads_ is truegraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()to.custom_join_thread_fncustom join thread function not set for intra op thread poolDynamic block base set to session-Using global/env threadpools since use_per_session_threads_ is falseFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALcustom join thread function not set for inter op thread pool-inter-opGiven model could not be parsed while creating inference session. Error message: CastFloat16Transformersession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessiononnxruntime::InferenceSession::~InferenceSessionUnknown error during EndProfiling()onnxruntime::InferenceSession::{dtor}::::operator ()Error during EndProfiling(): Execution providers must be registered before the session is initialized.onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized. Received nullptr for exec providerParallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsParallel 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.onnxruntime::InferenceSession::LoadThis session already contains a loaded model.Exception during loading: onnxruntime::InferenceSession::SaveToOrtFormatModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Encountered unknown exception in Load()Unknown exception in Load()model_loading_arrayFailed to load model because protobuf parsing failed.onnxruntime::InferenceSession::TransformGraphmodel_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.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.onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: onnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelModel was not loadedonnxruntime::InferenceSession::InitializeInitializing session.onnxruntime::InferenceSession::Initialize::::operator ()This session will use the allocator registered with the environment.Adding default CPU execution provider.Session has already been initialized.Model was not loaded. as all the graph nodes have not been partitioned to the CUDA EP.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.loading_ort_format && serialized_session_state != nullptrThis session will use the CUDA Graph feature as requested by the user.This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been partitioned to the CUDA EP.Encountered 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.NchwcTransformer Got: Expected: Please fix either the inputs or the model.session_initialization index: Got invalid dimensions for input: for the following indices Invalid rank for input: )))) , expected: ((Unexpected input data type. Actual: (Invalid Feed Input Name:Size mismatch: feed_names has elements, but feeds has elements.onnxruntime::InferenceSession::ValidateInputstensorInput with name: is not expected to be of type tensor.seq is not expected to be of type tensor sequence.sparse_tensor is not expected to be of type sparse tensor.Output vector incorrectly sized: output_names.size(): p_fetches->size(): At least one output should be requested.Output vector pointer is NULLonnxruntime::InferenceSession::RunReplaying the captured CUDA Graph for this model with tag: Invalid Output Name:Encountered unknown exception in Run()Running with tag: Session not initialized.Session was not initializedonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelMetadataStart the second Run() to capture the graph. The first one is for necessary memory allocation;The second one is for capturing the graph.model_runProfiler is disabled.onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetOverridableInitializersCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingDid 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: Unsupported device specified in the memory arena shrink list: Unable to shrink arena: error message: The registered allocator for device-id combination is not an arena based allocator: onnxruntime::InferenceSession::CreateLoggerForRunrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasonnxruntime::InferenceSession::AddPredefinedTransformersonnxruntime::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 3.10.5.jsonLoad model from failed:model_loading_uri_ failed. Only bytes were able to be read.onnxruntime::LoadOrtModelByteslist too longother_erroronnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0Hjcom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwccom.microsoftorg.pytorch.atenMismatch between source and target type. Source=[ShapeInferenceError] Unsupported Source/Target type= Target=tensor(uint64)tensor(int32)tensor(int64)tensor(float16)unknowntensor(uint32)tensor(uint16)tensor(int8)tensor(int16)seq(tensor(uint8))tensor(float)tensor(double)tensor(bfloat16)tensor(uint8)seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))tensor(complex64)tensor(complex128)seq(tensor(string))seq(tensor(bool))seq(tensor(float))seq(tensor(double))tensor(string)tensor(bool)seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16)) line , but it is already registered from file ) from file version: Schema error: in onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you (domain: Trying to register schema with name known by the checker., but its domain is notin the inclusive range [, but its version is not Only CPU allocators can be shared between multiple sessions for now.An allocator for this device has already been registered for sharing.Only CPU devices are supported for now.Exception caught: intra-opinter-opD:\a\_work\1\s\onnxruntime\core\session\environment.ccReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.No allocator for this device has been registered for sharing.stringYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostMemcpyFromHostinputXoutputvalue'session_optionsUnsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cc, column at line ort_configUnsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeSequential modeParallel modeSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Setting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: Setting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICThe Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoORT config json from the model: onnxruntime::SetEnableProfilingSetting enable_profiling to Json stored in the `ort_config` key cannot be parsed. Error message: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()intra_op_num_threads option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integerexecution_modeThe Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointra_op_num_threadsenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: ' not foundexecution_mode option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingkey 'cannot use at() with cannot use key() for non-object iteratorsbooleanbinarydiscardednumberinvalid map keynullobjectarraycannot get valueout_of_rangetype_errorparse_errorinvalid_iteratorparse errorsyntax error while parsing - invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literaltrue literal; last read: 'unexpected ; expected '[''{'']''}'false literalnull literalstring literalnumber literal'[', '{', or a literalunknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'':'','end of inputinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Avector too longinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 bytecannot compare iterators of different containersobject keyobject separatornumber overflow parsing 'iterator out of rangecannot use erase() with iterator does not fit current valuetype must be number, but is Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplD:\a\_work\1\s\include\onnxruntime\core/graph/graph.h Max:onnxruntime::GraphTransformer::RecursetoRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hInsertedCast_cast node to cast from float16 to float32 on cpuCastonnxruntime::InsertCastTransformer::ApplyImpldtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()ACLExecutionProviderArmNNExecutionProviderROCMExecutionProviderNhwcTransformerQDQFinalCleanupTransformersession.disable_quant_qdqAttentionFusionBiasDropoutFusionBiasGeluFusionsession.enable_quant_qdq_cleanupoptimization.enable_gelu_approximationsession.qdqisint8allowedConvConvBNFusionConvMulFusionDivMulFusionBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationConvAddFusionEmbedLayerNormFusionExpandEliminationExpandFastGeluFusionDivEliminateDropoutDropoutDynamicQuantizeMatMulFusionGemmGemmTransposeFusionEliminateIdentityIdentityGeluApproximationGeluFusionGemmActivationFusionGemmSumFusionMatMulScaleFusionMatmulTransposeFusionNoopEliminationAddLayerNormFusionSimplifiedLayerNormFusionMatMulAddFusionMatMulIntegerToFloatFusionQDQPropagationTransformerQDQS8ToU8TransformerReluQuantRewriteReluNotWhereFusionWhereClipQuantRewriteClipSliceTransposeOptimizerUnsqueezeEliminationUnsqueezeFuseReluClipReshapeFusionSkipLayerNormFusionEliminateSliceD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformeronnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild_RuleBasedTransformerLevelUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRewriteRulesVitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'Memcpyonnxruntime::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::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.ccBad optional accessperm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDims does not match rank onnxruntime::ApiTensor::NumElementssize >= 0No NodeArg found for name onnxruntime::ApiTensor::DataPermutation entry out of bounds for shape Failed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()Failed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializer to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializeronnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrsuccessFailed to find initializer for name: GatherSqueeze provided for op: Transposenew_num_elts == old_num_eltsCannot reshape initializer Added in transpose optimizer_outAn entry for this node should be added in onnx_ops_available_versions and static_kernel_hashes map.opset_import_iter != domain_to_version_map.end()iter != onnx_ops_available_versions.end()Transpose Optimizer is adding an unexpected node: domain == kOnnxDomainTranspose optimizer is expected to add only onnx domain ops. Domain: Onnx domain not found in opset imports.onnxruntime::GetSinceVersionForNewOpFusedConvResizeQLinearGlobalAveragePoolQLinearAveragePoolExisting destination type is not compatible with source type.const_transpose_optimizeronnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Optimization after layout transformation failed: channels_lastonnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccCould not find OrtValue with name 'DequantizeLinearendD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplstartConstantFolding. Can't constant fold fetches.size() == node->OutputDefs().size()Unsupported output type of node 'ShapeCould not find a CPU kernel and hence can't constant fold 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->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 ()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.hReshapeonnxruntime::AttentionFusionHelper::MatchGemmSubgraphFaild to match gemm pathConcatExpected value:Slice ends is less than INT_MAXStart MatchGemmSubgraphSlice parameter is not expected. Input index:CheckSliceParameters return falseGemm bias shape not expectedFaild to match concat node for Gather pathsconcat first input value is not -1Output edge count not expected for nodes in gemm pathInput of reshape_before_gemm is not the input of subgraphGemm bias is not constantGemm does not have 3 inputsunsqueeze_after_gather axes value not expectedaxesgather axis value not expectedaxisconcat_after_gather input 2 does not have expected valueconcat_after_gather does not have expected number of inputs or output edgesOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathGemm bias shape is not expectedGemm bias is not constant initializerGemm weight shape is not expectedGemm weight is not constant initializerPass MatchGemmSubgraphgather input 1 value is not expectedonnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerMask is neither unidirectional nor all onesThis optimizer does not support external data for unidirectional mask right nowStart MatchUnidirMaskSubgraphExpect mask data type is uint8 or floatunidir mask is not constantPass ValidateGemmInitializerunidir mask shape not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskDiv and Shape1 does not have edgeOutput edge count not expected for nodes in path 1 of unidirectional maskCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for last_sliceFaild to match the path (Div-->Where-->Add) for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match path 1 for unidirectional maskSubOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskCheckSliceParameters returns false for slice1ValidateUnidirMask returns false for mask_sliceOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphOutput edge count not expected for SoftmaxFailed to find Softmax nodeCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2Pass MatchInputMaskSubgraphmask_mul const input not matchedOutput edge count not expected for mask nodesFailed to find path for maskmask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3equal const not matched.Failed to find shape pathFailed to find reshape shape path 2Failed to find reshape shape path 1EqualStart MatchInputMaskSubgraphDistilBertwhere const not matched.Failed to find mask pathFailed to find path for present_kFailed to find path for past_kFailed to match v_concatFailed to find path for present_v and past_vPass MatchInputMaskSubgraphDistilBertgather indices not matched.onnxruntime::AttentionFusionHelper::MatchPastSubgraphStart MatchPastSubgraphpast_v_gather indices != 1present_v_unsqueeze axes value not expectedpast_v_gather and past_k_gather does not have same past inputpast_k_gather indices != 0past_k_transpose perm attribute not matchedpermpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3Pass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQqk_div const not matched.q_reshape const not matchedv_reshape initializer value is not expectedhidden_size != num_heads * head_sizereshape initializer value is not expectedPass CheckNodesInPathVk_transpose perm attribute not matchedk_transpose has not perm attributePass CheckNodesInPathKk_reshape const not matchedPass CheckNodesInPathQq_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKFaild to find path to qkv_matmulonnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path v to SplitSplitCast mask from int64 to int32Mask_Int32Start FuseGptAttentionCheckNodesInPathQ returns falseq and v are not from same Split nodeopt_k_transpose perm attribute not matchedUsing transpose optimized patternMatchInputMaskSubgraph returns falseCheckNodesInPathV return falseFailed to find path for qMatchUnidirMaskSubgraph returns NULLAttentionFused Attention subgraphs unidirectionalnum_headsk and v are not from same Split nodeFailed to find path for kMatchPastSubgraph returns falseCheckNodesInPathK returns falseD:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32onnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64 or float32qkv_weightsFused an attention node for GPT.Mask shape is unknown or not 2D, or data type unknownqkv_biasq_matmul and q_add shape not matchedq root should be layer normalizationk_matmul and k_add shape not matchedk root is not layer normshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::FuseSubGraphQKImplTotal fused Attention node count: onnxruntime::FuseSubGraphQKDistilBertFused an attention node.onnxruntime::AttentionFusion::FuseSubGraphFaild to find path vFailed 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 int32Failed in match v_matmul and v_add input shapeOutput edge count not expected for Add or MatMul in path vFailed in match input mask subgraphbn_mean_tensor_protobn_var_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyepsilonBatchNormalizationConvBnFusion_W_ConvBnFusion_BN_B_conv_W_tensor_protoconv_B_tensor_protoqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantonnxruntime::QDQS8ToU8Transformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccAveragePoolLeakyReluArgMax1DQMaxPooldropDQQDQSelectorActionTransformer2DQ*DQGlobalAveragePoolSigmoidȵjD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)onnxruntime::NodesToOptimize::GetNodeactivationFailed to get Clip min/max constants.onnxruntime::`anonymous-namespace'::actions::FuseConvActivation::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccExpected activation node.activation != nullptrTanhHardSigmoidConvActConvAddReluconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"onnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesactivation_paramsExpected Conv then Add.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)betaConvActivationFusionUnsqueezeElimination_D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccUnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyUnexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyminFuseReluClip_D:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantFastGeluTotal Gelu Approximation (FastGelu) node count: D:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationGeluonnxruntime::GeluApproximation::ApplyImplBiasGelujj0jFused reshape node: Total fused reshape node count: D:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccallowzeroonnxruntime::ReshapeFusion::ApplyImplCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraphstartsends_sum_transformedFused Gemm with Sumnew_gemm_output_defs.size() == 1D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.cconnxruntime::GemmSumFusion::Applynew_gemm_input_defs.size() == 3transBtransAother_sum_input != nullptrSumgraph.RemoveNode(sum_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(gemm_node.Index())!sum_input_movedsum_output_edge.src_arg_index == 0gemm_input_edge.src_arg_index < 2onnxruntime::GemmSumFusion::SatisfyCondition and onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndnode != nullptrInvalid node index onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputnode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.hAt least one graph node must be specified in the propagation edge.QuantizeLinearInserting Q/DQ pair between node ("", index: at NodeArg "".D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cconnxruntime::`anonymous-namespace'::InsertQDQPairsrc_node || dst_nodeFailed to set op schema for added DQ node._dqFailed to set op schema for added Q node._qInserted by QDQPropagationTransformer_pre_qonnxruntime::QDQPropagationTransformer::ApplyImplonnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::GetNextPropagationEdgedst_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgesrc_node != nullptrInput data type is not int32 or int64D:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cconnxruntime::CheckInputInput shape is unknown or not 2D, or data type unknown_CastCast Input from int64 to int32_Int32Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.Output edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Output edge count not expected for nodes in path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.The first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.RangeConstantOfShapeNonZerotwo paths share the same shapeThe parent of two shape nodes are expected to be input_ids.position_embeddingsFailed to match position subgraph.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Optional position subgraph nodes number of outputs unexpected.The parent of shape nodes are expected to be input_ids.Failed to match Shape node. Second input of Gather should be a constant with value 1. The third input of Range should be a constant with value 1.Position embedding shape not matched.Failed to get initializer tensor.Input is expected to have dim value in all dimensions.onnxruntime::FuseSubGraphWord embedding shape not expected.EmbedLayerNormalizationfused EmbedLayerNorm subgraphs mask_indexGamma should be of shape (hidden_size). Input_ids and segment id should have the same shape. Segment id is not valid. Input id is not valid. Position embedding shape is not expected.Failed to get position embedding weights.Failed to match position embedding subgraph.Position embedding data type shall be float or float16.onnxruntime::EmbedLayerNormFusion::ApplyImplonnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). DynamicQuantizeMatMulDynamicQuantizeLinearMatMulIntegerToFloatD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplwhich does not equal the specified override of with a fixed dimension size ' The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplFreeDimensionOverrideTransformerInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.onnxruntime::MatMulAddFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add gemmEluSeluSoftplusSoftsignScaledTanhParametricSoftplusThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccwith activation fused Gemm FusedGemmfused activation_Unexpected data type for QuantizeLinear input y_zero_point of onnxruntime::GetQConstantLowerUpperD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ValueMovesConvAddActConvAddActivationFusionFused Gemm with Transpose_transformedtranspose_node.InputDefs().size() == 1onnxruntime::MatmulTransposeFusion::ApplyImpltransBatchAtransBatchBfused MatMul and Transpose MatMul_With_Transposeonnxruntime::GetTransposePermsD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()onnxruntime::UpdateConsumerCountcast != nullptronnxruntime::ReorderCastAndTransposeCreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesdeque too longConvMulFusion_Mul_B_onnxruntime::ConvMulFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_SqrtReduceMeanfused LayerNorm subgraphs Powonnxruntime::SimplifiedLayerNormFusion::ApplyImplstash_typeSimplifiedLayerNormalizationD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImpljj jonnxruntime::SkipLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccSkipLayerNormalizationjj0jReorderInputreorderstridespadsoutput_channels <= nchwc_output_channelsBuffer overflowD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformerImpl::TransformConvreshape_nchwcspatialkernel_shapedilationsNOTSETauto_padSAME_LOWERSAME_UPPERgroupVALIDalign_cornersasymmetricnearest_modehalf_pixelUpsamplefloorGlobalMaxPoolscales_bn_nchwcchannelsReorderOutputnearestmodecoordinate_transformation_modelinearonnxruntime::NchwcTransformer::ApplyImplHjj is not in valid range [-axis >= -tensor_rank && axis <= tensor_rank - 1axis D:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisonnxruntime::BiasSoftmaxFusion::ApplyImplbroadcast_axisD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cc into softmax(input + bias)softmax_axisBiasSoftmaxonnxruntime::CommonSubexpressionElimination::ApplyImplrepresentative.output_index != kInvalidOutputIndexD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cc[] because it's the graph's output.Not eliminating output of node onnxruntime::QDQFinalCleanupTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cc"" and with name "onnxruntime::`anonymous-namespace'::CleanUpNodeSequenceCleaning up back-to-back nodes: NotD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.ccTranspose optimizer failed: onnxruntime::TransposeOptimizer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccQLinearConvonnxruntime::NhwcTransformer::ApplyImplonnxruntime::BiasDropoutFusion::ApplyImplBiasDropoutBitmaskBiasDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBitmaskDropoutfused Add-Dropout-(Add) for onnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.ccMatMulIntegeronnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.ccfused Gelu subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErfonnxruntime::GeluFusion::ApplyImplfused Add and GeluD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGeluFusion::ApplyImplfast_gelu_outputD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImplGPT2Gelufused GPT2Gelu subgraphs jjj0jConvAddFusion_B_add_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_Add_B_FloorCeilAbsReciprocalIsNaNIsInfRoundNegnoop_with_empty_axeskeepdimsLogExpstorage_orderNhwcMaxPoolAtanAcosAsinCoshMaxAtanhAcoshAsinhHardSwishCeluSignShrinkSinhTanCosSinMeanLessOrEqualGreaterOrEqualLessReduceLogSumReduceSumPadCastLikeXorOrAndMinGreaterBitShiftPReluModcom.microsoft.QLinearReduceMeanLogSoftmaxHardmaxTilecom.microsoft.QLinearAddcom.microsoft.QLinearConcatcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearSigmoidReduceProdReduceMinReduceMaxReduceLogSumExpArgMinReduceL2ReduceL1ReduceSumSquareSpaceToDepthcom.microsoft.com.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearMulDepthToSpaceGridSampleLRNUnsupported ONNX opsetonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceonnxruntime::Initializer::Initializerutils::HasDataType(tensor_proto)Initializer must have a datatypecalled_ == 1Axis must be non-negativeonnxruntime::Initializer::divonnxruntime::Initializer::mulonnxruntime::Initializer::subscalers.size() == 1 || scalers.size() == num_blocksInvalid other(scalers) sizeonnxruntime::Initializer::scale_by_axisaxis >= 0Expecting the same data type!model_path.IsEmpty()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.D:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccsize() == other.size()Expecting the same sizeonnxruntime::Initializer::adddata_type() == other.data_type()onnxruntime::Tensor::MutableDataAsSpanmaxD:\a\_work\1\s\onnxruntime\core\optimizer\utils.cconnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()RandomNormalRandomUniformUnexpected data type for Clip input of MultinomialRandomNormalLikeRandomUniformLikePHk@HkHkHkHkonnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerallocator_ptr_q_or_dq_input_defs.size() >= 2onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations for transformer Applying runtime optimization action Missing action onnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsNot enough produced nodes in the runtime optimization record.onnxruntime::RegisterProducedNodesWithGraphToo many produced nodes in the runtime optimization record.onnxruntime::SelectorActionRegistry::RegisterSelectorAndActionD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMatched onnxruntime::MatchAndProcessExisting registration with name inserted_in_name_to_entryNodes to optimize are not valid, skipping action.generated at runtimeQLinearinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aQGemmFailed to set node op schema.Failed to remove node.D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::CreateReplacementNodeonnxruntime::ReplaceWithNew::Runonnxruntime::ReplaceWithNew::RunForSaveonnxruntime::MergeIntoTarget::Runtarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::MoveInputOutputNode index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.Index out of rangeonnxruntime::`anonymous-namespace'::MoveInputOutputImplD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.ccNnapiExecutionProviderNupharExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderXnnpackExecutionProvider`jHj8jjRkRk@RkRkj(RkXRkjjjpRkRkTjonnxruntime::ml::RegisterOnnxMLOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::RegisterOnnxOperatorKernelsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBaseAttribute perm of Transpose has an invalid value. Value is outside range.v >= 0 && static_cast(v) <= std::numeric_limits::max()[ is repeated.perm: does not align with rank of input data: (local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseMismatched data types between input and output Tensors. num_axes > 0Transpose not implemented for empty tensors.Transpose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetuponnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::Transpose::Computeinput_tensor_ptr != nullptr(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::TypedDoTransposeEltWiseboxes_tensorNonMaxSuppressionD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareCompute0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBasecenter_point_boxoutput != nullptronnxruntime::NonMaxSuppression::ComputeThe most inner dimension in boxes must have 4 data.boxes and scores should have same spatial_dimension.onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].boxes must be a 3D tensor.scores_tensorboxes and scores should have same num_batches.scores must be a 3D tensor.Missing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBaseinfo.GetAttr("axis", &axis_).IsOK()Gather Tind type not supported in this build.TindD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::Compute must be within the inclusive range [indices element out of data bounds, idx=onnxruntime::UnsqueezeBase::UnsqueezeBaseinfo.GetAttrs("axes", axes_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueonnxruntime::UnsqueezeBase::PrepareComputeX != nullptronnxruntime::Unsqueeze::Compute'axes' has an out of range axisaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1nullptr != p.output_tensor'axes' has a duplicate axisAxes input is nullD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor != nullptronesidedinversedft_length must be a scalar value.onnxruntime::discrete_fourier_transformDFTSTFTT1T2Unsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::DFT::Computeonnxruntime::STFT::ComputeUnsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.Unsupported input data type of D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.ccdft_length must be greater than zero.ratio input should have a single value.Ensure that the dft size is smaller than the signal.window_size < signal_sizeIf both frame_length and window are set, then the size of the window must be equal to the frame_length.frame_length == window_lengthsignal_components == 1 || signal_components == 2onnxruntime::short_time_fourier_transformD:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.hsignal shape must end in 1 (real) or 2 (real, imaginary).tensor->Shape().Size() == 1onnxruntime::signal::get_scalar_value_from_tensoronnxruntime::bit_reverseUnsupported bit size.@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?periodicoutput_datatypeMelWeightMatrixBlackmanWindowT3HannWindowHammingWindowupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cclower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOoffset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceByonnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::BroadcastIterator::Append is invalid.onnxruntime::BroadcastIterator::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largest by start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocator) are not at boundary of span with size:Broadcast Output range [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) for tensor of length:Invalid start/ending offset [onnxruntime::Mean_6::Computedata_1.Shape() == shapedata_n.Shape() == shapeonnxruntime::Max_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeinputCount >= 1onnxruntime::Min_6::ComputeUnsupported X type: Must have 1 or more inputsD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::Computefmod attribute must be true for floating point typesonnxruntime::mod_internal::CallModImpl::operator ()(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfmodfmod must have value either 0 or 1shape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandonnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.Unsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::Sum_6::Computecur1 == end1input_count >= 1onnxruntime::Sum_6::Computehelper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShift'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'LEFTRIGHTdirectiononnxruntime::BitShift::BitShiftonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()Attempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hbodyinfo.GetAttr("body", &proto).IsOK()info_ == nullptronnxruntime::Scan<9>::SetupSubgraphExecutionInfogsl::narrow_cast(output_axes_.size()) == num_scan_outputsSetupSubgraphExecutionInfo should only be called once for each subgraph.scan_output_axesNumber of entries in 'scan_output_axes' was Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_input_axes but expected scan_input_directionsscan_output_directionsnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::Scan<9>::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cc of Invalid value in scan_input_axes for input Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was ' dimension but input 'Invalid scan input: has length of dimensions or more but input had shape of Expected session_stateonnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::ComputeSubgraph SessionState was not found for 'body' attribute.CreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_Scanonnxruntime::ScanImpl::TransposeOutputVInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::Execute. Output tensor rank was Output OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'No attribute with name:''is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeunknown kernel typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ElementWiseRangedTransform::CreateD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ScaledTanh::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelgammaD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelreductionCPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: last dimension of indices must not be larger than rank of input tensorinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: ScatterNDonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()invalid indice found, indice = onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.ScatterElementsScatterdata type is different from updates typeIndices 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= vs Indices and updates must have the same ranknoneD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::Scatteronnxruntime::GridSample::Computegrid_dims[0] == NGrid batch size does not match input batch size Only 4-D tensor is supportedgrid_dims[3] == 2Last dimension of grid: , expect 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cc" not supported, expect bilinear, nearest or bicubicbicubicpadding_modezerosbilinearpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"padding_mode "" not supported, expect zeros, border or reflectionreflectionborderD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.honnxruntime::GridSample::GridSamplemode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"mode "Invalid mode of value specified. It should be either avg or maxavgThe existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.spatial_scalesampling_ratio_ >= 0Sampling ratio should be >=0, but it was sampling_ratiooutput_widthoutput_heightD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBaseFirst dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly Number of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1Null batch_indices_ptrNull rois_ptrNull input X ptrRoiAlignIBLoop inputs. Found:Graph in 'body' attribute of Loop should have static_cast(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cc but has outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputs Got: Expected: was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output Inconsistent shape in loop output for output. onnxruntime::Loop::Initonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 1 has unknown shape: All scan outputs MUST be tensorslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::Executeinfo == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::Compute' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::InitializeFailed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for If output onnxruntime::IfImpl::ExecuteIf outputs. outputs which doesn't match the subgraph's 'If' node has num_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.ccthen_branchinfo.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::Initelse_branchinfo.GetAttr("else_branch", &proto).IsOK()onnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatoronnxruntime::IdentityOp<1>::ComputetypeOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()onnxruntime::Optional::OptionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.hOOptionalOptionalHasElementOptionalGetElementonnxruntime::PropagateInputOrtValueToFirstOutputD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableonnxruntime::Optional::Computecan only be of type(tensor) or (seq(tensor))The TypeProto attribute in the Optional op Trying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::OptionalGetElement::Computebad allocationInvalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: Uonnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid number of outputs for BN trainingInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: !is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::Computetraining_modemomentumTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormreverseforwardbidirectionalRNND:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::ComputeAffineclipinfo.GetAttr("direction", &direction_).IsOK()layoutD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNactivation_alphaallowed_directions.find(direction_) != allowed_directions.end()activationsactivation_betahidden_sizeinfo.GetAttrs("activations", activations_).IsOK()activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - layout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.'. Must be one of 'forward', 'reverse', or 'bidirectional'.onnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of 'D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0sigmoidinput_forgetactivation_func_names.size() == static_cast(num_directions_) * 3tanhonnxruntime::DeepCpuLstmOp::PrePackLSTMLSTM operator does not support double yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetGRUactivation_func_names.size() == static_cast(num_directions_) * 2Invalid data type for GRU operator of GRU operator does not support double yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::Computeonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::SafeRawPointeronnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= NB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endcur + size <= endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointeri < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:gsl::narrow_cast(input_shape.Size()) == sizesize != 0 && (input_shape.Size() % size) == 0requested_shape[i] >= -1A dimension cannot be less than -1, got D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelperunknown_dim == -1At most one dimension can be -1.shapeTensor->Shape().NumDimensions() == 1A shape tensor must be a vector tensor.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape::Computeonnxruntime::Reshape_1::Reshape_1Attribute shape is not set.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cconnxruntime::Trilu::ComputeInput tensor should have a rank of at least 2upperonnxruntime::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()TriluD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.onnxruntime::SliceSkips::SliceSkipsdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceIteratorBase::InitAxis tensor must be provided to the CumSum opAxis tensor should be of type `int32_t` or `int64_t`Axis tensor should be 0D or 1DCumSumonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeexclusivestarts.size()=dims.size() == starts.size()dims.size()=extents.size()=onnxruntime::WritableSliceIterator<__int64>::Initsteps.size()=dims.size() == extents_.size()onnxruntime::WritableSliceIterator::Initdims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=onnxruntime::Hardmax::Compute. N=Hardmax inputs N, D and N * D must be < info.GetAttrs("scales", scales_).IsOK()extrapolation_valueonnxruntime::UpsampleBase::UpsampleBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.h`tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floor model uses the deprecated attributeyet this opset exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outsidemode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be cubicinfo.GetAttr("mode", &mode).IsOK()'Linear' mode only support: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || (scales.size() == 4 && scales[0] == 1 && scales[3] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)Resize operatorUpsample operatorscales 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)onnxruntime::UpsampleBase::ParseScalesDatatf_crop_and_resize] is not supportted!pytorch_half_pixeltf_half_pixel_for_nnround_prefer_ceilceilcoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeonnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.] is not supported!nearest_mode:[Scale value should be greater than 0.scale > 0scale >= 1onnxruntime::UpsampleBase::ScalesValidationfast_shape.size() == 3onnxruntime::ValidateFastReduceKRKfast_shape[1] == output.Shape().Size()Only works on matrices with three dimensions.Reduction on all axes, output size should be 1.count == 1fast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceRKRAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1onnxruntime::ValidateNoTransposeReduceonnxruntime::ValidateCommonFastReduceCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateKeepDimslast_loop_red_size > 0last_loop_size > 0projected_index.size() > 0onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyD:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccOnly works on matrices with two dimensions.fast_shape.size() == 2must be overloaded.onnxruntime::ValidateMustBeOverloadedfast_shape[0] == output.Shape().Size()onnxruntime::ValidateFastReduceRKonnxruntime::ValidateFastReduceKROutput size mismatch.info.GetAttr("keepdims", &keepdims).IsOK()select_last_indexonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<0>::ReduceKernelBaseedgeInvalid 'mode' attribute valueconstantreflectInvalid 'pads' attribute valueonnxruntime::PadBase::PadBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hValue 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() == 1Unexpected mode of onnxruntime::PadValueFromFloatCannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:pads_tensor.IsDataType()onnxruntime::Pad::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorPads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankPads 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)onnxruntime::PadImpl'pads' has wrong number of valuesInput tensor has no dimensionsdata_rank > 0data_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroGatherNDindicesbatch_dimsonnxruntime::GatherND::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrindices tensor data type not supportedinvalid index found, index = indices tensor must has rank larger than 0onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::ComputeX input is required!failed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccNull input ptronnxruntime::SpaceToDepth::Computeinput count mismatchblocksizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.DepthToSpace requires input depth to be a multiple of (block_size * blok_size)SpaceToDepth requires input width to be a multiple of block_sizeSpaceToDepth requires input height to be a multiple of block_sizeSpaceDepth ops require a 4-D input. Provided rank: onnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCRUnsupported input type in DepthToSpace op: onnxruntime::DepthToSpace::ComputeUnsupported input type in SpaceToDepth op: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccGatherElementsD:\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 outputGatherElements 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 inputD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_implGatherElements op: Unsupported tensor type, size:GatherElements op: Out of range value in index tensoronnxruntime::GetIndexInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyTile 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 dimensionalTile doesn't have an implementation yet for the type: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::Compute!input_tensor.IsDataType()input_shape[i] == 1Dimension of input must be 1 instead of . shape=onnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeInvalid value in 'split' attribute. All values must be > 0splitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::ComputeSplit operator does not support yetD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::Split::ComputeImplsplit_tensor->Shape().NumDimensions() == 1An split tensor must be a vector tensor.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attribute!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes'axes' has an axis outside of the tensor dimension count'step' value cannot be 0'axes' has duplicatesonnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of Ends must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccData type for starts and ends inputs' is not supported in this build. Got Starts and steps shape mismatchStarts and axes shape mismatchStarts and ends shape mismatchonnxruntime::SliceBase::ComputeCannot slice scalarsoutput == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::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.honnxruntime::ConcatBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccnew_axisinput_rank == reference_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Data type mismatchinput != nullptrCannot concatenate scalars got: Ranks of input data are different, cannot concatenate them. expected rank: onnxruntime::ConcatBase::ComputeImplD:\a\_work\1\s\onnxruntime\core/framework/copy.hsrc and dst types must matchdst.DataType() == src.DataType()onnxruntime::DispatchStridedCopylast >= firstonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopycounter.current_offset == lastonnxruntime::StridedCopy::::operator ()src and dst must have same shape and not be rank 0.dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeX 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.hratio_tensor->Shape().Size() == 1onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fseedInput is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationacross_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()onnxruntime::LRN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccX->Shape().NumDimensions() == 4sizeinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRNalpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fbiasD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()onnxruntime::Flatten::FlattenD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeFlattenscale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.cconnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })SizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.honnxruntime::GemmBase::GemmBaseinfo.GetAttr("transB", &temp).IsOK()info.GetAttr("transA", &temp).IsOK() K: N:Gemm: Invalid bias shape for broadcastGEMM: Dimension mismatch, W: onnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hM_ >= 0 && K_ > 0 && N_ >= 0onnxruntime::GemmBroadcastBiasc_shape != nullptrc_shape is required if c_data is providedright operand cannot broadcast on dim left operand cannot broadcast on dim M_ == 1 && N_ == 1 was falsenum_dims_with_pad != num_output_dimsMatMul dimension mismatchD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hleft_num_dims > 2 && left_num_dims == right_num_dimsTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is truenum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsonnxruntime::MatMulComputeHelper::Computeleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computemin should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()min->Shape().IsScalar()onnxruntime::clip_internal::Clip_6Base::Clip_6Basemin_ <= max_D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max should be a scalar.D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().IsScalar()stoull argument out of rangeinvalid stod argumentinvalid stoll argumentinvalid stoull argumentstoll argument out of rangestod argument out of rangeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastAttribute to is not set.onnxruntime::`anonymous-namespace'::CastToStringsnprintf_result > 0snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().INF-INFsnprintf() failed with return value: %.8gNaNinvalid expand shapeonnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()->equationEinsum op: An implementation for the input type D:\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::DeviceComputeEinsumEinsum op: There must be atleast one inputonnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringceil_modecount_include_padD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesGlobalLpPoolNo kernel shape is set.Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.onnxruntime::PoolBase::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hInput dimension cannot be less than 3.info.GetAttr("p", &p_).IsOK()onnxruntime::PoolProcessContext::init`InlinedVector::at(size_type)` failed bounds checkLpPoolDilations dimensions should match kernel shapedilations.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()kernel_shape[dim] > 0Pad should be smaller than kernel.info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()onnxruntime::PoolAttributes::ComputeSizePadDilationsponnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0onnxruntime::Pool::ComputeUnsupported pooling size : onnxruntime::MaxPoolV8::ComputeImplInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsOneHotInvalid argument for depth; it's not a scalar.Depth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::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,float,int>::Computeop_kernel_info.GetAttr("axis", &axis_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopkOpset9ConstructorCommonkop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()TopKop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedonnxruntime::TopkOpset11ConstructorCommonlargest] should not be greater than specified axis dim value [k argument [k tensor should be a 1D tensor of size 1value of k must not be negativeinput count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueoutput count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!onnxruntime::GetSeqIdxSequenceAtSequenceLengthSonnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertonnxruntime::SequenceEmpty::ComputeUnsupported 'dtype' value: SequenceEmptyInvalid sequence index () specified for sequence of size (D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceConstruct::Computenum_inputs >= 1SequenceConstructonnxruntime::SequenceErase::ComputeSequenceEraseonnxruntime::SequenceInsert::ComputeData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (onnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputInvalid data type for split tensor split_size_sum () != split_dim_size (SplitToSequence operator does not support SplitToSequenceViolation of the requirment that all input tensors must have the same data type.onnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0Split should be > 0std::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::ComputeGot nullptr for sequence input.ConcatFromSequenceInvalid input shape: Input channels is not divisible by group.filter number not equal to input channel number. filter_number: num_input_channels: group count is <= 0output_shapeoutput_padding kernel_shape: W: D:\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' attributes group: X num_dims does not match W num_dims. X: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeConvTransposeonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose.honnxruntime::ConvTranspose::ConvTransposeThe existing bug in the padding distribution for auto_pad type SAME_UPPER/SAME_LOWER will be fixed in next ORT 1.13 release and hence the results of ConvTranspose operator using the above auto_pad type(s) will be different.onnxruntime::ConvAttributes::InferPadsAndOutputShapeNot enough elements in pads. Expected: Not enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: 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. C: kernel channels: onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.onnxruntime::Conv::Computeoutput and sum shape must matchD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::MaxUnpool::ComputeMaxUnpoolstrides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()output_shape is smaller than minimum required. output_shape: inferred output shape:Shape must be 1 dimensional as it's tensor data of a shapeIndex tensor shape should be same as that of the input data tensor to unpool.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccDetMatrix dimensions are not equal. Square matrix is expectedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Compressx_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.ccbatch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.hReverseSequence. batch_size=batch_axis != time_axistime_axisinfo.GetAttr("time_axis", &time_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are sequence_lens shape must be {batch_size}. Got:Unknown tensor type of onnxruntime::ReverseSequenceOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cc. Value must be in range [0,Invalid sequence length: detect_negativeFailed to obtain detect_negativedetect_positiveFailed to obtain detect_positiveonnxruntime::IsInf::IsInfD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccT4onnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccprepacked_buffers[0].get() == nullptrQLinearConv : zero point of per-channel filter must be sameW_zero_point_data[i] == W_zero_point_valueonnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::ComputeOffsetIsValidQuantParam(W_scale, M)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeOffsetQLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)onnxruntime::QLinearConv::ComputeOutputScaleQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)ConvIntegerMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)onnxruntime::MatMulInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccMatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)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.onnxruntime::MatMulComputeHelper::Compute::::operator ()QLinearMatMulIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsBQuantParamSupported(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_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 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1*D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cconnxruntime::string_normalizer::Locale::Localeis_case_sensitiveen-USonnxruntime::StringNormalizer::StringNormalizerattribute is_case_sensitive is not setattribute case_change_action is not setcase_change_actionStringNormalizerConversion ErrorConversion ErrorFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesp.secondDuplicate stopwords not allowedwstr != wconv_errorStopword contains invalid utf8 charsInput dimensions are either[C > 0] or [1][C > 0] allowedSingle dimension value must be greater than 0Input contains invalid utf8 charsUPPERLOWERattribute case_change_action has invalid valueNONEstopwordslocale!sw.empty()Empty stopwords not allowedbad conversionResize: input/output value is nullptronnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeResize: input/output value's dimension mismatchUpsample: input/output value is nullptrResize: input shape needs to be at least a single dimensionUpsample: input/output value's dimension mismatchUpsample: input shape needs to be at least a single dimension. Input dim value: Output dim value: Cannot scale 0 by any factor to generate a non-zero value. Dimension: output_dims[i] == 0Input dim is zero but required output dim is non-zero. D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::Computeroi_input_idx_ > 0Invalid roi input index.sizes == nullptrOnly one of scales or sizes must be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Either scales or sizes MUST be provided as input.X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeUpsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.4-D input with innermost scale (usually channel of NHWC) as 1.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.: '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.scales[3] == 1.0fResize: unexpected mode: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.onnxruntime::Upsample::BaseComputeoutput_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::UpsampleNearestonnxruntime::TfIdfVectorizer::TfIdfVectorizermode is requiredTFD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDFmode: is unrecognized, acceptable values are TF,IDF,TFIDFTfIdfVectorizerstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowed must be of equal sizeweightsGot weights of size: but ngram_indexes size: pool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()min_gram_length must be inbounds of ngram_counts: <= max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()ngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()status.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredmax_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: max_skip_count is requiredngram_countsimpl_->max_skip_count_ >= 0status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredmin_gram_lengthimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length is requiredmax_gram_lengthimpl_->min_gram_length_ > 0min_gram_length >= max_gram_length required: >= (items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.!pool_strings.empty()pool_strings must not be empty if specifiednon-empty pool_int64s is required if pool_strings not providedpool_int64s-gramsstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for Duplicate ngram detected, size: id: onnxruntime::ngram_details::PopulateGramsp.first->second->id_ == 0op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()onnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hEyeLike : Input tensor dimension is not 2EyeLikeMust have a single dimension of 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims()[0] == 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeMust have a valid input shape.Unsupported output datatype with size: D:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::PrepareComputet_proto_p->dims_size() == 1Must have a single dimensionutils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoonnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: !utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.Unsupported value attribute datatype: p_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::AllocateOutputTensorsonnxruntime::OrtValueTensorSlicer::Iterator::operator *D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_ but Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::Initialize has batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::ValidateInput did not match batch size of Scan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Initonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsroi_batch_id >= 0roi_batch_id < batch_sizeonnxruntime::RoiPool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccMaxRoiPoolR->Shape()[1] == 5info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0pooled_height_ > 0pooled_width_ > 0pooled_shapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2info.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolLpNormalizationp_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormInvalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: onnxruntime::InstanceNorm::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInstanceNormalizationop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::RandomUniformLike::RandomUniformLikesample_sizelowinfo.GetAttr("low", &low_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of info.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformonnxruntime::RandomNormalLike::RandomNormalLikehighmeaninfo.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::RandomNormal::RandomNormalD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hOutput type not supported in this build: num_samples is < 1Invalid data type of D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::Computebatch_size is < 1num_classes is < 1onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensoronnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterNchwcKernelsSOFTMAX_ZEROLOGISTICSOFTMAXTreeEnsembleRegressorSUMInput shape needs to be at least a single dimension.onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccnodes_nodeidsnodes_modesnodes_truenodeidsnodes_treeidsnodes_featureidsnodes_falsenodeidsnodes_missing_value_tracks_truenodes_hitratestarget_weightstarget_treeidsnodes_values_as_tensortarget_weights_as_tensorpost_transformnodes_valuestarget_nodeidstarget_ids-D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_hitrates_as_tensorbase_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon::Init (truenode).Unable to find node in tree Node (weights).One falsenode is pointing either to itself, either to another tree. (falsenode).base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()base_values.empty() is already there.nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()aggregate_functionUnknown aggregation function in TreeEnsemble.n_targetsbase_valuesonnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::MergePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMin::MergePredictionTreeEnsembleClassifierX dims is empty.onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclass_nodeidsclass_treeidsclass_weightsclasslabels_stringsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeclass_idsclasslabels_int64sclass_weights_as_tensoronnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScorespredictions.size() == 2default_floatvalues_floatskeys_strings_Unusedvalues_int64skeys_int64sLabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()values_stringskeys_floatsInput of tensor(int64) must have output of tensor(string)Input of tensor(string) must have output of tensor(int64)classes_stringsdefault_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()) must have the same length. However, the number of key is and the number of values is num_keys == num_valuesThe attribtues in LabelEncoder (name: 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<__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_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2] != 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.ccInput features_per_batch[onnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.ZipMapPOLYkernel_typeLINEARD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramsSVMRegressorUnexpected mode:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::Computenum_features == feature_count_coefficientsonnxruntime::ml::SVMRegressor::SVMRegressorinfo.GetAttrs("rho", rho_).IsOK()rhoone_class!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()support_vectorsn_supportsonnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccSVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hUnsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0Unexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)ScalerEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetscale_.size() == offset_.size()Scale size: () != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.ccOneHotEncoderUnknown Category and zeros = 0.onnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_int64sonnxruntime::ml::MakeNormalizenorminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerMAXInvalid normalize value of D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of info.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearRegressorinterceptstargetsLinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.ccUnsupported input element type of Found:Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplshape_size == out.length()onnxruntime::ml::CastInputToFloatonnxruntime::ml::ImputerOp::ComputeInvalid typereplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()replaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccImputerimputed_value_floatsimputed_value_int64sEmpty value of imputed values.Empty input dimensions.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::Computeonnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer).inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()DictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOpstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpCategoryMappernum_entries == int_categories.size()Input of int64 must have output of string Input of string must have output of int64onnxruntime::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.h%finvalid stof argumentstof argument out of rangeInvalid PACK_MAP value of Expected DENSE or SPARSEcast_toonnxruntime::ml::MakePackonnxruntime::ml::MakeCastInvalid CAST_TO value of SPARSEDENSETO_STRINGTO_FLOAT Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64Unexpected CAST_TO value of 0.fCastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0Invalid input type of value: Expected std::map or std::mapmax_mapinfo.GetAttr("map_form", &attr).IsOK()max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()onnxruntime::ml::CastMap::CastMapinfo.GetAttr("cast_to", &attr).IsOK()map_formD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.cconnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of Binarizer is NaNthresholdInput data with index: ArrayFeatureExtractorInvalid Y argument: index is out of range: Y[] (Invalid argument: X input has empty dimensions.) >=Invalid Y argument: num_indices = 0iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator () did not.validonnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesInvalid values in ''. 0 == forward. 1 == reverse.D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoNumber of entries in '' was inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputonnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #Unsupported typeInput X must have 3 dimensions only. Actual:Input W must have shape {}. Actual:onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid activation function of alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameA + (M * K) <= A_endD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccQuantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fweights.quant_para_softplussoftsignonnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got scaledtanhthresholdedrelueluhardsigmoidaffineInput initial_h must have shape {leakyrelureluInput B must have shape {Input R must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {Input initial_c must have shape {Input P must have shape {onnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.ccThe override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size() Left shape override: Left shape: onnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1candidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum op: Input shapes do not alignEinsum op: The candidate output cannot be reshaped into the op's outputEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimRanks of pair-wise operands must be equal. left_rank == right_rankright.Shape().Size() == right_shape_override.Size() Right shape: Right shape override: Right shape: Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript provideddim_iter == rank...onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleEinsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input . The shape is: is incompatible in the dimension in the same dimensionAnother operand has a dim value of Found '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: onnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains letters not seen in the inputsFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersrank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal for input shape Cannot parse the diagonal elements along dims onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal The innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()Einsum op: Exception during MatMul operation: Incompatible 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]Only 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::MatMulData types of the inputs must match for MatMulInversebias is expected to have 1 dimension, got Last dimension of bias and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchinput is expected to have 3 dimensions, got skip is expected to have same shape as inputepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormop_kernel_info.GetAttr("axis", &axis_).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::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerinfo.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerDynamicSlice) + scale_[1] () + scale_[0] (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder (Input's height () needs to be greater than or equal to the topBorder () + bottomBorder ( input dimensions insteadAttribute border needs to be specified with four border elements, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::ComputeCropBifurcationDetectorpred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)onnxruntime::contrib::BifurcationDetector::Computesrc_tokens_len >= prev_suffix_match_idx_datamax_ngram_size_ >= min_ngram_size_max_ngram_size_ > 0info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_sizemin_ngram_size_ > 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.honnxruntime::contrib::BifurcationDetector::BifurcationDetectorinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_sizengram_sizeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()input_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computescores_dims.size() == 2scores_dims[0] == batch_sizeTidNGramRepeatBlockonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::ComputeCDistThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:metricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist::CDistNull crop_size_ptrNumber of dimensions for crop size should be exactly 1CropAndResize specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResizeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hTransposeMatMulpositivekeysMurmurHash3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeinput_num_bytes % 4 == 0SparseToDenseMatMulonnxruntime::contrib::SparseToDenseMatMul::ComputeCurrently supporting only 2-D matrices vs inner_B: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccExpecting COO 2-D indices shapeCan not multiply A and B as inner dimension does not match. inner_A: Expecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesCurrently support only COO and CSR(x64) formatsOuter size must be M + 1COO indices must be 2-D, got: is out of bounds of lhs_right: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator () is out of bounds of out_left: COO k index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()COO m index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()Char embedding size does not match conv kernal size 2. Conv kernal size 2 : D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeWordConvEmbeddingembedding_sizechar_embedding_sizeconv_window_size conv filter size: Conv filter size does not match embedding_size attribute. embedding_size attribute: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute .Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setseparators must not be empty!tokenexp.empty()mincharnum is too big for char level tokenezation!separators.empty()Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 charsCan not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedtensor(string) expected as inputTokenizerattribute mark is not setmarkD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute pad_value is not setpad_valueattribute mincharnum is not setmincharnummincharnum_ > 0attribute mincharnum must have a positive valuetokenexpseparatorsonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpstatic_cast(activation_func_names.size()) == num_directions_ * 3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImpl}, actural: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.ccAttention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {], while found!, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, }, Got: Attention memory layer weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{, aw_attn_size}. Got:Attention v weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {onnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::ExpandDims::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.hAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()Axis must be within range [axis <= X_NumDims && axis >= -X_NumDims Axis is ].ExpandDimsonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeBeamSearchparameters_.model_type == IBeamSearchParameters::kModelTypeGpt || parameters_.model_type == IBeamSearchParameters::kModelTypeT5encoderinfo.GetAttr("encoder", &proto).IsOK()onnxruntime::contrib::transformers::BeamSearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.ccgpt_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfodecoderinfo.GetAttr("decoder", &proto).IsOK()Encoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyonnxruntime::contrib::transformers::BeamSearch::Computedecoder_feeds_fetches_manager_Subgraph SessionState was not found for 'decoder' attribute.decoder_session_stateencoder_feeds_fetches_manager_Subgraph SessionState was not found for 'encoder' attribute.encoder_session_stateonnxruntime::contrib::transformers::BeamSearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.h'BeamSearch' input min_length should be a scalar. Got shape of onnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.h'BeamSearch' input num_beams should be a scalar. Got shape of 'BeamSearch' input num_beams is required'BeamSearch' input max_length should be a scalar. Got shape of 'BeamSearch' input max_length is required'BeamSearch' input length_penalty should be a scalar. Got shape of 'BeamSearch' input length_penalty is required'BeamSearch' input num_return_sequences should be a scalar. Got shape of 'BeamSearch' input num_return_sequences is requiredonnxruntime::contrib::transformers::BeamSearchBase::Initializeparameters->sequence_length == 1'num_return_sequences' has to be smaller or equal to 'num_beams'.onnxruntime::contrib::transformers::BeamSearchGpt::ExecuteInput 'input_ids' is expected to have 2 dimensions, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' shape does not match with vocab_size, got Input 'attention_mask' is expected to have same shape as input_idsonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 2 dimensions, got onnxruntime::contrib::transformers::BeamSearchT5::Executeonnxruntime::contrib::transformers::BeamSearchT5::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenAttention cannot have past sequence and extra add qkInput 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got qkv_hidden_sizes attribute should have 3 elementsqkv_hidden_sizes first element should be same as the secondInput 1 dimension 1 should be 3 times of hidden dimensionhidden_size should be divisiable by num_heads.Input '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:qkv_sizes doesn't match the wights dimensionInputs '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 0Inputs '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)Input 'extra_add_qk' is expected to have 4 dimensions, got Input 'extra_add_qk' dimension 0 should be same as batch_size, got Inputs '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 Input 'extra_add_qk' dimension 3 should be same as sequence_length, got Input 'extra_add_qk' dimension 1 should be same as number of heads, got Input 'extra_add_qk' dimension 2 should be same as sequence_length, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hqkv_hidden_sizesinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseonnxruntime::contrib::Attention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.ccExpect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresent4D mask in attention cpu kernel is not supportedonnxruntime::contrib::AttentionCPUBase::ApplyAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.hSampleOpD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.cconnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.hIsScalarOr1ElementVector(a_zp)QGemm : zero point of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsQGemm : scale of input a must be a scalar or 1D tensor of size 1b_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : scale of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or Ny_zp == nullptr || IsScalarOr1ElementVector(y_zp)QGemm : zero point of y must be null or a scalar or 1D tensor of size 1b_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point and scale of input b should have same shape sizeTBTAy_scale == nullptr || IsScalarOr1ElementVector(y_scale)QGemm : scale of y must be null or a scalar or 1D tensor of size 1TYTYZTCSegment embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationBeta zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::QEmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cconnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::NhwcMaxPool::Computeinput_shape.Size() > 0 || N == 0MatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)onnxruntime::contrib::MatMulIntegerToFloat::ComputeInput DynamicQuantizeLSTMRecurrentWeight point must be constant, 4*} for per-channel quantization. Actual:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePackR_zero_pointW_zero_point must have shape {} for per-tensor/layer quantization or shape {DynamicQuantizeLSTM : Weight zero point must be zeroR_scaleW_scaleQAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.cconnxruntime::contrib::QAttention::Computeinput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1QLinearMulQLinearAddD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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 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 giventensor_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 givenQLinearLeakyReluQLinearSigmoidonnxruntime::contrib::QLinearAveragePool::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if giventensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)Input x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)QLinear Pooling unsupported pooling size!input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if giventensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::contrib::QLinearConcat::QLinearConcatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.ccInput scale is not float for input def @tensor_x_scale->IsDataType()Each input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @QLinearConcatWrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()input_count_x3 >= 3 && input_count_x3 % 3 == 0onnxruntime::contrib::QLinearConcat::Computeonnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::Compute(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1channels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseUnsupported mode 'X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccinfo.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPool(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::Computen_dims == 1Attribute 'D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cconnxruntime::ml::GetNumberOfElementsAttrsOrDefault' is specified but is empty.' must be a vector.Attribute:'n_elements > 0' has one dimension but is empty.(for attribute ''.proto.data_type() == proto_typeUnexpected type (onnxruntime::ml::GetVectorAttrsOrDefaultInput 1 dimension 0 should have same length as the last dimension of input 0Input 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got rows[ [seqno=.. row[ is not in (0, D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps not support normalize yet.onnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_onnxruntime::contrib::BahdanauAttention::PrepareMemoryunimplemented activation: activation_params count mismatchInput 0 and 1 shall have same shapeinput_ids and position_ids shall have same shapeword_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeposition_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got beta is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have size of onnxruntime::contrib::BeamSearchCpuDeviceHelper::CreateGptInputsinput_ids_shape.NumDimensions() == 2onnxruntime::contrib::BeamSearchCpuDeviceHelper::CreateEncoderInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_device_helper.ccBeamSearch op: An implementation for the input type onnxruntime::contrib::BeamSearchCpuDeviceHelper::ExpandInputselement_type == DataTypeImpl::GetType()onnxruntime::contrib::BeamSearchCpuDeviceHelper::ExpandBufferonnxruntime::contrib::BeamSearchCpuDeviceHelper::ProcessLogitslogits_shape.NumDimensions() == 3onnxruntime::contrib::BeamSearchCpuDeviceHelper::UpdateDecoderFeedslast_outputs.size() >= static_cast(1 + num_present_tensors)Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZeroonnxruntime::contrib::transformers::BeamSearchScorer::Finalizeoutput_sequences != nullptrnext_beam_scores_.empty()onnxruntime::contrib::transformers::BeamSearchScorer::Initializeonnxruntime::contrib::transformers::BeamHypotheses::OutputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cctop_k <= Size()hypothesis_buffer_offset_ <= hypothesis_buffer_length_sequences != nullptrbeam_hyp.Size() >= gsl::narrow_cast(num_beams_)beam_idx == num_beams_next_scores.size() == next_indices.size()Batch can only be done if all beams have been generatednext_scores.size() == next_tokens.size()onnxruntime::contrib::transformers::BeamSearchScorer::ProcessInvalid hidden_states expension: has_hidden_state_ == falsesession_state_ != nullptronnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesSetup must be called before CreateInitialFeedsdecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesnumber of outputs expected to be 1 + 2 * layers, got:input_idsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.ccnumber of outputs expected to be kFirstPastInputIndex + 4 * layers, got:kFirstPastInputIndex currently only supports 2 or 3onnxruntime::contrib::transformers::T5DecoderSubgraph::Validateencoder_hidden_statesdecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typelogitsdecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 0 shall be named as input_ids, got: encoder_attention_maskonnxruntime::contrib::transformers::T5EncoderSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.ccexpect 3 inputs, got:encoder subgraph input 2 shall be named as decoder_input_ids, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder_input_idsencoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: expect >=6 outputs, got:number of outputs expected to be 2 + 4 * layers, got:encoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph output 2 shall be named as present_key_self_0, got: present_value_self_0encoder subgraph output 1 shall be named encoder_hidden_states, got: present_key_self_0onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedscpu_allocator shouldn't be nullptrencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph outputs 1, 2, ... shall have same data typeprefix.length() == prefix_lengthonnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::ProcessD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.ccsubgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph output present_key_self_0 is expected to have 4 dimension, got subgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state is expected to have 5 dimension, got onnxruntime::contrib::transformers::Subgraph::GetParametersonnxruntime::contrib::transformers::Subgraph::SetupD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.ccmodel_typeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.cconnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsinput_ids shall have 2 dimensions. Got no_repeat_ngram_sizecontext != nullptrpad_token_iddecoder_start_token_idearly_stoppingeos_token_idnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeams) shall be no more than max_length <= kMaxSequenceLengthmax_length (max_length > sequence_lengthdims.size() == 2) shall be greater than input sequence length (repetition_penalty > 0.0fnum_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got ) shall be be no more than num_beams (num_return_sequences (num_return_sequences shall be a positive integer, got num_return_sequences >= 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.cconnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedssubgraph input 2 shall be named as attention_mask, got: attention_masksubgraph input 1 shall be named as position_ids, got: position_idssubgraph input 0 shall be named as input_ids, got: Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).subgraph input 2 (attention_mask) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 0 (input_ids) shall have int32 typesubgraph input 1 shall be named as present_0, got: present_0subgraph output 0 shall be named as logits, got: subgraph input 3 shall be named as past_0, got: past_0subgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 0 (logits) shall be float or float16 data typeQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1 is not supportedTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType.DMLCudaPinnedCudaOpenVINO_GPUSpecified device is not supported.D:\a\_work\1\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShapeArgument is not a tensorthe ort_value must contain a constructed tensor or sparse tensorNot implemented`anonymous-namespace'::GetIndicesTensorUnsupported indices_format passedtype_proto is not of type sequence!type_proto is not of type map!p_type != nullptronnxruntime::Tensor::SizeInBytestensor size overflowtensor failed memory size calculationshape.Size() must >=0strides.empty()Strided tensor is supported for training only for now.D:\a\_work\1\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::Tensordtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypevalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeexpected a registered ONNX typeInvalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerbfloat16(unknown type)tensor type sparse tensor type is not currently registered or supportedMLDataType for: thisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatibleutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())uint8int16uint16int32uint32int64uint64float16onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainer(null)floatbooldoubleint8 dst_size: onnxruntime::SparseTensor::AllocateBufferSparseTensor Allocation failed for size: Must contain Coo format. Got: Format() == SparseFormat::kCooonnxruntime::SparseTensor::AsCooExpecting to contain one index, got: format_data_.size() == 1U must be equal to or twice the values size: Must have the same size. Got src_size: onnxruntime::`anonymous-namespace'::CopyDataD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.ccthe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValuethis tensor already has populated sparse_indices must be less than total buffer size: Values size Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::UseBlockSparseIndicesUse MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataonnxruntime::SparseTensor::MakeBlockSparseStrings to device type: Unable to find a data transfer for copying from device type: onnxruntime::SparseTensor::AsBlockSparseExpecting one index. Got: Expecting to have at lest 3-D shape. Got:onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2 to be equal to values blocks: Expecting index blocks: onnxruntime::SparseTensor::CopyThis instance should not be emptyDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeMust have the same shapeExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooStringsFormat() == SparseFormat::kUndefinedThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptrMust contain Csr format. Contains: Format() == SparseFormat::kCsrconnxruntime::SparseTensor::AsCsrIndex size: values_count == index_sizeonnxruntime::SparseTensor::GetCooIndexDimsSparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesNot expecting an allocator setUse MakeCooStringsonnxruntime::SparseTensor::MakeCooDataOuter index count must be rows + 1 or zero. Got: This method does not expect allocator to be setonnxruntime::SparseTensor::UseCsrIndicesUse MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataonnxruntime::SparseTensor::MakeCsrStringsMust contain BlockSparse format. Got: Format() == SparseFormat::kBlockSparseExpecting two indices. Got: format_data_.size() == 2Udense shape must 2-D. Got: onnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zero the same as values size: Expecting inner index size: rows: dimstart <= dimend && dimend <= values_.size()onnxruntime::TensorShape::Slice{} dimensions. for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.Config key is empty or longer than maximum length 128Config value is longer than maximum length 1024]. It will be overwritten] already exists with value [Config with key [onnxruntime::ConfigOptions::AddConfigEntryD:\a\_work\1\s\onnxruntime\core\framework\config_options.ccAn OrtValue for this name has already been added: Expecting same size spansinit_num == values.size()onnxruntime::SessionOptions::AddExternalInitializersReceived nullptr for nameReceived nullptr for OrtValueReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.onnxruntime::SessionOptions::AddInitializerD:\a\_work\1\s\onnxruntime\core\framework\session_options.cc but is of type: expected to be of type: Attribute: Attribute name and type don't matchNo attribute with this name is defined. is defined.No attribute with name: is expected to have type: Requested attribute: 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 arg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundCPU allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValueinput_offset >= 0 && output_offset >= 0onnxruntime::KernelDefBuilder::VariadicAliasD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cc Encountered following errors: (onnxruntime::KernelRegistry::TryCreateKernelKernel not found kernel is not supported in : Conflicting with a registered kernel with op versions.kernel def can't be NULL: Conflict with existing kernel def hash.Failed to add kernel for This op has been implemented only for the following types (Op with name ( (node_version: However the types are incompatible.Found kernel for Op with name ( in the supported version range but the node in the model has the following type (),onnxruntime::KernelRegistry::Registeronnxruntime::`anonymous-namespace'::TypeBindingResolver::Resolve constraint was not found for kernel_end_version: D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cc node_version: kernel start version: and type ( Version mismatch.onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()kernel_info != nullptrD:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cc capable of executing this nodeonnxruntime::GetCpuPreferredNodesORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs onnxruntime::GetCpuPreferredNodes::::operator ()Candidate for fallback CPU execution: D:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocator type: TensorProto: external data size mismatch. Computed size: nullptr == p_data`anonymous-namespace'::ReadExternalDataForTensoronnxruntime::utils::UnpackTensorWithExternalDataImpl) does not match the data size() in protoTensor does not have external data to read from.UnpackTensor: the pre-allocated size does not match the raw data size, expected D:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc`anonymous-namespace'::GetExternalDataInfo, external_data.length: External data type cannot be UNDEFINED or STRING. data_type: onnxruntime::utils::DenseTensorToSparseTensorProtoUnsupported type: onnxruntime::utils::UnpackInitializerDataInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend() is not supported.onnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Element_size of: HasDataType(dense_proto)Must have a valid data type given file_length: are out of bounds or can not be read in full. offset: size to read: string tensor can not have raw dataExternal initializer: Initialized tensor with unexpected type: tensor can't contain negative dimsUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size(onnxruntime::utils::GetFileContentdata overflow can not be writen into Tensor type TensorProtoToTensor() tensor shape mismatch!onnxruntime::utils::TensorProtoToTensorTensorProto type onnxruntime::utils::CopySparseDataSparse Indices raw data size does not match expected.Sparse indices int32 data size does not match expectedSparse indices int64 data size does not match expectedInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorindices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64string tensor can not use pre-allocated bufferTensorProtoToMLValue() must take a pre-allocated MemBuffer!The preallocated buffer is too small. Requires , Got onnxruntime::utils::ConstantNodeProtoToTensorProtoonnxruntime::utils::TensorProtoToMLValueUnsupported attribute value type of in 'Constant' node 'Invalid TensorProtoonnxruntime::CreateAllocatorReceived invalid value of arena_extend_strategy New allocator: D:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cc device: Existing allocator: onnxruntime::AllocatorManager::InsertAllocatorDuplicate allocator for OrtMemType:onnxruntime::IExecutionProvider::GenerateMetaDefIdD:\a\_work\1\s\onnxruntime\core\framework\execution_provider.cconnxruntime::IExecutionProvider::InsertAllocatorIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider::Compile with fused Node is not implemented by metadef_id_generator_IExecutionProvider constructor must be called with true for use_metadef_id_creatorUnable to convert strings tensor to a sparse tensor that not on CPUCurrently do not support dims higher than 2 dimensions: Outer indices must be M + 1. Got: onnxruntime::sparse_utils::SparseCsrToDenseTensorInput must be of COO formatouter_num == (rows + 1)onnxruntime::sparse_utils::SparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyInvalid index: > dense_size: D:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.cconnxruntime::sparse_utils::DenseTensorToSparseCsrSupport 2-D matrices onlyUnsupported element size: Unable to convert strings tensor to a sparse tensor that is not on CPUInput must be of CSR formatinner_num == src.Values().Shape().Size()Expecting inner indices to be same as nnz. Got: onnxruntime::sparse_utils::DenseTensorToSparseCoo to There's no data transfer registered for copying tensors from data_transfer registered is nullptr.Tensor size mismatchonnxruntime::DataTransferManager::CopySparseTensorsonnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.ccInternalTestingExecutionProviderFailed to find allocator for device allocator != nullptrinvalid allocator.Unsupported OrtValue type.SNPEExecutionProviderfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesUnsupported OrtValue type to copy between device.onnxruntime::utils::FindMemoryInfoForValueonnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::ExecuteGraphOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO A dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while loading custom ops with message: has already been loaded.Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()Could not find OrtValue with idx 'SetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_.has_value()existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateonnxruntime::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::SaveToOrtFormatonnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::UpdateToBeExecutedNodesOnly one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: for attribute Entry exists in node onnxruntime::SessionState::AddOutputNameToNodeInfoMapping, from KernelCreateInfo (via hash lookup): Node execution provider type mismatch. Existing: '. Existing kernel def hash: Cannot overwrite existing kernel for Unable to find kernel hash for node: 'Unable to find compiled kernel hash for node 'onnxruntime::SessionState::LoadFromOrtFormat' optype: onnxruntime::SessionState::CreateSubgraphSessionState) node with name 'Main Graph instance should have populated all subgraphs when being resolved.subgraphonnxruntime::SessionState::LoadFromOrtFormat::::operator (), new kernel def hash: ) in kernel registries for Failed to find kernel def hash (onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::CreateKernelsonnxruntime::SessionState::PopulateKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::KernelUseSharedPrePackedBuffers. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:session.disable_prepackingD:\a\_work\1\s\onnxruntime\core\framework\session_state.ccDone saving OrtValue mappings.SaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfo used in the node: Using cached version of pre-packed weight for constant initializer: !op_type.empty() which is of op type: ) and node is used by node Unable to write the provided PrePackedWeights instance into the container (+allocator_for_caching.get() != nullptr doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node weights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be emptyonnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weightsonnxruntime::OuterScopeNodeArgLocationAccumulatorsubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator () Attribute: Index:onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::VerifyEachNodeIsAssignedToAnEpNode placementsCould not find an implementation for onnxruntime::VerifyEachNodeIsAssignedToAnEpImpl Provider: [onnxruntime::SessionState::FinalizeSessionStateAll nodes have been placed on [: [entry != node_to_subgraph_ss.second.cend()p_op_kernel' OpType:Missing session state for subgraph. Node:'onnxruntime::SessionState::SetupAllocators::::operator (). Ignoring allocator from Allocator already registered for The node is not placed on any Execution Provider. Failed to find kernel for ). (node in KernelRegistryManagerfound duplicated provider D:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccnullptr != type_protoonnxruntime::utils::GetMLDataTypeinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksError mapping feeds: Error mapping output names: onnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FunctionKernel::CreateAPI VERSION cannot be used with this model due to its ONNX opset not being supported by the layout transformer.D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.hCreate state function failed. Return value:Execution Provider: onnxruntime::PartitionOnnxFormatModelImpl::::operator ()onnxruntime::PlaceNode is still using Function style Compile API which is deprecated and will be removed soon. Please migrate to the new Compile API based on FilteredGraphViewer.onnxruntime::InlineNodesonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::PartitionOnnxFormatModelImpl did not return correct number of compiled functions by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP., but was not selectedonnxruntime::GetCapabilityForEPD:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.ccNode '1 == capability.nodes.size() was inserted using the NHWC format as requested by with domain:compiled_kernel_hashes != nullptr. Execution Provider must generate unique names across the entire model.Existing entry in compiled kernel hashes for onnxruntime::PartitionOrtFormatModelImplsingle_node_compute_func should have 1 element.onnxruntime::GraphPartitioner::PartitionCompiled kernel hashes must be providedonnxruntime::GraphPartitioner::PartitionOrtFormatModelNo provider specified.jonnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.hipMalloccudaMallocD:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)Creating bins of max chunk size Reserving memory in BFCArena for size: h != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserve bytes.Failed to allocate memory for requested buffer of size Available memory of is smaller than requested bytes of Allocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendExtended allocation by onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionentry != regions_.end() max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: memory limit: arena_extend_strategy: D:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeCould not find Region for: p_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_int. onnxruntime::BFCArena::FindChunkPtr!chunk->in_use() bytes. The total allocated bytes is now onnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum) (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytesFailed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for . bin_num: (requested) num_bytes: BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::Shrink BFC Arena shrunk by !c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Mergeonnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_node_index_info and ort_value_idx_map are out of sync and cannot be used Requested shape:D:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_Allocation of memory pattern buffer for failed. Error:Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of returned nullptronnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::{ctor}::::operator ()shape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape:fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()invalid index Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferShape mismatch attempting to re-use buffer. . 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.For ort_value with index: ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::AllocateSparseTensormlvalue.Fence() == nullptrort_value.Fence() == nullptrTensor shape cannot contain any negative valueTrying to allocate memory for unused optional inputs/outputs, block in memory pattern size is: but the actually size is: , fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper failed: onnxruntime::ExecutionFrame::GetAllocationPlanort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx= size=Invalid allocation kind: Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanonnxruntime::ExecutionFrame::VerifyOutputSizesExpected shape from model of does not match actual shape of for output D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was onnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeInvalid dim0_offset of onnxruntime::OrtValueTensorSlicer::Iterator::Iteratorort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Create. Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:onnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccchecksumlengthparsing failedmodel format error! Missing 'location'model format error!locationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infoBegin executionParallelExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.cconnxruntime::ParallelExecutor::Executeop_nameGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.onnxruntime::ParallelExecutor::RunNodeAsyncAll implicit inputs should have OrtValue instances by now. does not.Multiple errors were found.D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalentry != nullptrException running nodes starting at '. Unknown exception was caught by catch-all handler.Non-zero status code returned while running node. Name:'' Status Message: _fence_before_fence_after_kernel_timethread_scheduling_statsproviderinput_type_shapeoutput_sizeparameter_sizeactivation_sizeonnxruntime::ReleaseNodeMLValuesSequentialExecutor::Executeoutput_type_shape{"":[]}exec_plan_indexgraph_indexD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::ExecuteSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cc : ) onnxruntime::PlannerImpl::GetElementSizeThere is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_maponnxruntime::PlannerImpl::ComputeUseCounts::::operator ()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reusenullptr != tensor_type_baseonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::GetKernelCreateInfoonnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCountstarts_.empty() || start > ends_.back()No matching 'start' entry.starts_.size() == ends_.size() + 1onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndstarts_.size() == ends_.size()onnxruntime::AllocPlanPerValue::ProgramCounter::AddStartD:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.hInvalid 'start'. Value is smaller than previous 'end'.Previous entry was not terminated.Invalid 'end'. Value is larger than 'start'.end >= starts_.back()!IsNonTensor(*node_output)Invalid program_counter entries at index entry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::VerifyMemoryTimeSchedulespecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::ComputeReusePlanOnly tensors are supported for external outputs for now.Can not find the execution provider allocatoronnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the node Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defonnxruntime::PlannerImpl::ComputeUseCountsAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::CreatePlan is null. Invalid ORT format model.Subgraph SessionState for Kernel create info hashes are null. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. is missing. Invalid ORT format model.Subgraph SessionState entry for SessionState for subgraphs is null. Invalid ORT format model.Kernel create info is null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()onnxruntime::PrePackedWeights::GetHashIdentity_14Identity_16Gather_11Gather_13Identity_1Identity_13UnSqueeze_1UnSqueeze_11UnSqueeze_13Transpose_13Squeeze_1Squeeze_11Squeeze_13Transpose_1session.use_device_allocator_for_initializersFailed memory size calculationOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsstring tensor is not supported for copying between allocatorsFailed to copy tensor to ) is different from what is supplied () because the ORT planned memory location device DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::DeserializeTensorProtoD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cc is not used by any node. input with name SubgraphGraphDone saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( failed.Deserialize tensor D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorCan't use func with null ptr not found.onnxruntime::FuncManager::GetFuncsD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cc already exist.func info for node: onnxruntime::MemPatternPlanner::TraceAllocation!using_counters_current <= buffer_size_D:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.h[TypeInferenceError] ConstantNoOpALLOW_RELEASED_ONNX_OPSET_ONLYshape != nullptrTypeProto must have shape for this to runD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.honnxruntime::utils::GetShapeOutput: . Got: onnxruntime::NodeArg::UpdateTypeAndShapeOptional Type mismatch. Expected: Tensor element type mismatch. D:\a\_work\1\s\onnxruntime\core\graph\graph.ccType mismatch. Current= Input=' source: target:onnxruntime::MergeShapeInfoError merging shape info for output. ' , or optional typed entitiesSource and target must both be tensors. Falling back to lenient merge. , or sparse tensorsGraph ctor should have created NodeArg for initializer. Missing:onnxruntime::Graph::InitializeStateFromModelFileGraphProto) does not exist in the graph.node_argInitializer appears in graph inputs and will not be treated as constant value/weight. graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, ' the model will use the latest encountered initializer. Please, fix your model.This is an invalid model. Tensor does not have type information.Duplicate initializer (dense, sparse or ConstantNode): 'Invalid source node arg slot specified when removing edge.onnxruntime::Graph::RemoveEdgeArgument mismatch when removing edge.Invalid destination node arg slot specified when removing edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.Invalid node indexes specified when removing edge.Argument type mismatch when adding edge.onnxruntime::Graph::SetOuterScopeNodeArgsThis is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.) is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Graph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Subgraph output (Serialization error. Graph attribute was serialized without Graph instancefbs_attr cannot be nullNode::LoadEdgesFromOrtFormat, edge is missing for Node::LoadFromOrtFormat, input_arg_counts is missing], could not find NodeArg node_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormatLoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::SaveToOrtFormatdoes not have the graph for key onnxruntime::Node::LoadFromOrtFormat::::operator ()fbs_node_arg_names cannot be null] op_type [Serialization of fused function body is not currently supported, Node [Sparse initializer must have a name. This model is invalidDuplicate constant node sparse initializer name: 'Duplicate sparse_tensor_initializer: 'utils::HasName(sparse_tensor)graph_proto != nullptrgraph_proto cannot be null' Model is invalid.onnxruntime::Graph::Graphoutput edgesinput edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node ( is not the same as this node's index:onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()onnxruntime::Node::LoadEdgesFromOrtFormatinput index: but usage of initializer in graph expects This is an invalid model. Model input (Type Error: Data in initializer '' has element type ) of output arg () of node () does not have type information.Type Error: Type () type inference failedonnxruntime::Graph::InferAndVerifyTypeMatch) does not match expected type () output arg () bound to different types ( in node (Type Error: Type parameter () of Optype (onnxruntime::Graph::Resolveonnxruntime::Graph::ForThisAndAllSubgraphsouter_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.Error: Duplicate definition-site for () attribute (onnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputs) is not a registered function/oponnxruntime::Graph::VerifyNodeAndOpMatch) is required but not specified.Fatal error: Type Error: Shape of initializer does not match. This is an invalid model. In Node, , Error inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Size mismatch validating subgraph inputs. Got inputs but subgraph has Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.No Graph instance was found for attribute in node Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.onnxruntime::Graph::KahnsTopologicalSortSome nodes are not included in the topological sort, graph have a cycle. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::BuildConnectionsThis is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=) of operator () in node (This is an invalid model. Type Error: Type '' of input parameter () input arg () does not have type information set by parent node.) is invalid.This is an invalid model. Node (onnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()Node () Op (Subgraph input missing type.Node:UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesonnxruntime::Graph::AllocateNodenodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::CreateFusedSubGraphNodenullptr != func_meta_defouter_scope_node_arg != nullptr'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.Removing NodeArg 'onnxruntime::Graph::CleanUnusedInitializersAndNodeArgsinitializer_node_arg != nullptrRemoving initializer ''. It is not used by any node and should be removed from the model.Outer scope node arg name ''was added but does not exist. Cannot find NodeArgs for [onnxruntime::Graph::ToGraphProtoInternal(") : () -> (""Input to set must exist.onnxruntime::Graph::SetInputsinput->Exists()Node has no function body and cannot be inlined.dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::InlineFunctioncallnode.TryGetFunctionProto(inlined_fp)Schema was not found for fused node. Domain: OpType:onnxruntime::Graph::FinalizeFuseSubGraphSetOpSchemaFromRegistryForNode(fused_node)Replacement tensor's data type does not match.Replacement tensor's dimensions do not match.existing_entry != mutable_initializers.pointer_end()graph_proto_ is not in sync with name_to_initial_tensor_Failed to find existing initializer with name !foundTrying to replace non-external initializer with external dataonnxruntime::Graph::ReplaceInitializedTensorImplsparse_tensor_names_.count(tensor_name) == 0sparse_tensor_names_ not in sync with name_to_initial_tensor_graph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::RemoveInitializedTensorAddInitializedTensor already has tensor with name but different TensorProto.onnxruntime::Graph::AddInitializedTensorexisting->second == &tensoronnxruntime::Graph::ToGraphProtoFailed to convert dense initializer to sparsenode->GetOutputEdgesCount() == 0Can't remove node onnxruntime::Graph::RemoveNodeonnxruntime::Graph::SaveToOrtFormat as it still has output edges._token_Replaced external initializer: onnxruntime::Graph::InjectExternalInitializedTensorsD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::ConstPointerContainer > >::atindex < data_.size()NodeEdge is missing. Invalid ORT format model.Node index is out of rangeSparse Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Node is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormatInitializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()than the operator set version , but it its domain is not, but it its version is higherD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalDomain already set in registryD:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.cconnxruntime::GraphViewerToProtoMismatch between Graph and IndexedSubGraph. Node not found: Mismatch between Graph and IndexedSubGraph. Output not found:Invalid ExecutionOrdernodeD:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GraphViewernodeargMismatch between Graph and IndexedSubGraph. Input not found:graph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the GraphNot supported with filtered graph.onnxruntime::GraphViewer::GetNodesInTopologicalOrderonnxruntime::GraphViewer::GetRootNodesfilter_info_ == nullptronnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet are '0' and '1'. The environment variable contained the value: Graph is null. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatUnsupported model IR version: , max supported IR version: 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 model may run depending upon legacy support of some older opset version operators.D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelMissing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. 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 onnxruntime::model_load_utils::ValidateOpsetForDomainModelProto does not have a graph. is till opset D:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. 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 Null entry in metadata_props. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormat is less than 0. less than 0.Protobuf serialization failed.onnxruntime::Model::Saveonnxruntime::Model::LoadProtobuf parsing failed.No graph was found in the protobuf.Failed to load model with error: Load model failed. File doesn't existsystem error number onnxruntime::SaveModelmOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.Output should specify a shapeNegative values are not allowed in a shape specification expected to have tensor or sparse type expected to have tensor typeAttribute input_as_shapeextra_shapeConstrain input and output types to all tensor types.Constrain indices to integer typesdataThreshold valueseq_lengthsgateshidden_prevBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesvaluesGivenTensorFillValue of alphaA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).The scale to apply.Bias applied to each channel, same size as C.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.Value of beta) + scale[0] () + scale[1] () + right_border () needs to be greater than or equal to the left_border () + bottom_border () needs to be greater than or equal to the top_border ('Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)Input's shape must be 4-DIf 0, normalize the mean only. Default is 1.Scaling valueIf 1, mean and variance are computed across channels. Default is 0.hiddenGRUUnitAttribute expected to have tensor or sparse tensor typeC but has rank expected to have rank Incompatible dimensions expected to have tensor or sparse tensor type: is nullDimension mismatch in unification between expected to have rank >Failed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarInputs 0 shall be 2 dimensionsFailed to parse max_length or it is not positive integer scalarInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplication'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesError unexpected extra input in node: = Constant()Error parsing node:Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.maskratioresidual(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-1apply softmax to elements for dimensions softmax_axis or higherSupport padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.Three interpolation modes: bilinear (default), nearest and bicubic.If true, check only for NaN.Constrain the output to a boolean tensor.IsAllFiniteIf true, check only for Inf, -Inf.isnan_onlyBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.Constrain output 'mask' types to uint32 tensors.isinf_onlyConstrain input 'training_mode' types to boolean tensors. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Input rank must be >= 2.AD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) 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.TorchEmbeddingConstrain input and output types to all numeric tensors.scale_grad_by_freqpadding_idxweightsignal_ndimConstrain input0 and output types to float tensorsConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain to integer typesConstrain mask to integer typesscoressequences_scoressequencesprefix_vocab_maskFirst input does not have rank 2Second input does not have rank 2ZPadsConstrain input and output types to float tensorsComplexMulConjWComplexMulIrfftConstrain input and output types to float or half tensors.RfftnormalizedDecoder subgraph to execute in a loop.model type: 0 for GPT-2; 1 for encoder decoder like T5The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.no repeat ngrams sizeearly stop or notThe id of the padding tokenThe id of the token that indicates decoding starts.The id of the end-of-sequence tokenConstrain input types to all tensor types.Constrain output types to float tensors.Gridvocab_maskMrepetition_penaltylength_penaltynum_return_sequencesnum_beamsmin_lengthmax_lengthsparse_tensor(uint32)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(float)Whether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationScalar multiplier for the product of the input tensors.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 int64Constrain input and output types to any tensor type.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensorSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.activation_gammaInput axis is invalid: Scalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Whether A should be transposedWhether B should be transposedConstrain input and output types to float/int tensors.Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredinputs are expected to have tensor type and output type should not be null.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.Input/Output is a string tensorEpsilonConstrain types to float tensors.Constrain types to int tensors.crop_sizebatch_indicesroisThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. batch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionSquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)XU = Cast (X2D)Mean2D = ReduceMean (XU)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)NumReducedAxes = Neg (Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)PrefixShape = Slice (XShape, Zero1D, Axis1D)NumReducedAxes = Sub (Rank, Axis1D)Zero1D = Constant()Axis1D = Constant()XShape = Shape (X)Rank = Size (XShape)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 array'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Constrain to tensor(int32).Constrain to tensor(float).SequenceInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.first input tensor has wrong dimensionrois input tensor has wrong dimensionConstrains input to only numeric types.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".countsInput can be of any tensor type.idxxanchorsboxesConstrain output Y and scale type to float tensors.EfficientNMS_TRTinv_std_varConstrain mean and inv_std_var to be float tensors.Constrain output Y, scale and bias type to float tensors.Constrain input X type to float tensors.Type of Mean and InvStdDev tensors.InvStdDevVersion number of the TRT plugin.plugin_versioniou_thresholdMax detections to output.box_codingBox IOU threshold value.background_classEncoding type for the boxes or anchors inputs.detection_classesBackground class ID.detection_scoresdetection_boxesnum_detectionsmax_output_boxesMean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Scaled = Mul (NormalizedV, Scale2D)B2D = Flatten (B)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)type used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).The epsilon value to use to avoid division by zero.Input 0 shall be 3 dimensionspooled_sizeAttribute 'pooled_size' must be >= 1.Attribute 'max_output_boxes' must be >= 1.Constrain input and output types to uint8, uint16, float tensors.outputsinputs(Optional) Some notes for the modelnotes(Optional) Target device like CPU, DSP, etc.target_device(Optional) SNPE version used to convert the model.snpe_versionpayload of the SNPE DLC file.DLCConstrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.patchesImage size.feature_map_3feature_map_2feature_map_1feature_map_0MultilevelCropAndResize_TRTScore threshold value.score_thresholdActivation function to apply to the scores input.score_activationScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.factorMaximum relative distance, k.spandisentangled_attentionp2c_attentionc2p_attentionc2c_attentionPyramidROIAlign_TRTDisentangledAttention_TRTimage_sizePooled size.q_weightkeyqueryLongformerAttentionglobalglobal_biasGglobal_weighthas_key_padding_maskhas_layer_stateuse_paststatic_kvvalue_cachekey_cachekey_padding_maskkv_weightWhether every token can only attend to previous tokens. Default value is 0.Hidden layer sizes of Q, K, V paths in AttentionNumber of attention headskey and value cache shall be 4 dimensionsOne sided attention windows length W, or half of total window lengthwindowD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccpresentConstrain mask index to integer typesextra_addpastscores_outConstrain scores input and output types to float tensors.The NGram size.Constrain mean and inv_std_var to float tensors.skipsuffix_match_idxConstrain to integer types.tokenspred_tokensprev_suffix_match_idxcur_tokenssrc_tokensThe maximum NGram size for suffix matching.The minimum NGram size for suffix matching.segment_embeddingposition_embeddingword_embeddingsegment_idsConstrain key_padding_mask to bool tensors.DecoderAttentionnew_value_cacheConstrain input and output types to float and float16 tensors.new_key_cacheX_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) oneX_bias = Add (X, bias)baConstrain input and output integer tensors typesConstrain input and output float tensors types.embedding_sum̼+̼+ Dimension=Can't merge shape info. Both source and target dimension have values but they differ. Source=D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer 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 input and output types to 8 bit signed and unsigned tensors.C_zero_pointA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).reducedConstrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.x_zero_pointx_scaleInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarInput data type does not match the expected data typeC_scaleB_zero_pointB_scaleA_zero_pointA_scale{name}{additionalDocumentation}inputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} 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.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.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerConstrain input and output types to 8 bit tensors.Y_zero_pointY_scaleX_zero_pointX_scaleCoefficient of leakage.Pinitial_cinitial_hsequence_lensRCouple the input and forget gates if 1.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.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.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_scaleConstrain output types to 32 bit tensors.MulIntegerConstrain input types to 8 bit signed and unsigned tensors.ReduceSumIntegerreduced_zero_pointQLinearReduceMeandata_zero_pointreduced_scaledata_scalemultiplicationaxis must be in [-rank, rank-1]. input rank was additionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointa_zero_pointConstrain input A data type to 8-bit integer tensor.weight_zero_pointConstrain input and output types to int8 tensors.input_zero_pointweight_scaleinput_scaleConstrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.beta_scalegamma_scalesegment_embedding_scaleposition_embedding_scaleword_embedding_scalebeta_quantposition_embedding_quantgamma_quantword_embedding_quantWhich axis to concat onaxis must be in [-rank, rank)All inputs to Concat must have same rankConstrain weights types to 8 bit tensors.Required attribute axis is missingY_cConstrain seq_lens to integer tensor.Y_hConstrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).TVT8Constrain input and output types to float32 tensors.mask_index_outlayernorm_outbeta_zero_pointgamma_zero_pointsegment_embedding_zero_pointposition_embedding_zero_pointword_embedding_zero_point̼+D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccww_scaleinput and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Constrain input and output types to singed/unsigned int8 tensors.number of groups input channels and output channels are divided into.NhwcConvdilation 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.w_zero_pointonnxruntime::graph_utils::FindPathFailed since multiple edges matched:num_explicit_inputs == static_cast(target_input_idx)onnxruntime::graph_utils::AddNodeInputonnxruntime::graph_utils::ReplaceNodeInputCan only add a new input at the end of the current ones.. Index:Invalid input index for node ImplicitInputs: ExplicitInputs:Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::RemoveNodestd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphonnxruntime::graph_utils::GetIndexFromNameD:\a\_work\1\s\onnxruntime\core\graph\graph_utils.ccAttempting to get index by a name which does not exist:itr != node_args.end()for node: index >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNameonnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.Attempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializeronnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputInitializer with same name exists. Name:Node must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs in one of the subgraphs. cannot be safely updated to D:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccutils::HasName(attribute)onnxruntime::utils::SetNodeAttributeAttributeProto must have a name.Attempting to load runtime optimization records for a previously loaded optimizer: D:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatTENSORGRAPHSSPARSE_TENSORSTRINGSTENSORSFLOATSINTSSTRINGGRAPHFLOATINTUNDEFINEDSPARSE_TENSORSNull tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing string data for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveInitializerOrtFormatNull strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelonnxruntime::fbs::utils::LoadAttributeOrtFormatNull tensor attribute. Invalid ORT format model.Missing dims for sparse initializer: Null string attribute. Invalid ORT format model.Invalid ORT format model.Missing indicies for sparse initializer: onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.nnnnnnnn؍nnnȍn(nonnx_func_protoinput_arg->Type() != nullptr, domain: The given function name: Tout is not found in model local functionsTinoutdomain_version != -1init != opset_imports.end()onnxruntime::function_utils::IOTypeConstraintHelper::::operator () in function opset imports.No opset registered for domain onnxruntime::function_utils::CreateSchemaD:\a\_work\1\s\onnxruntime\core\graph\function_utils.ccactuals.size() <= formals.size()onnxruntime::function_utils::Inliner::bindNumber of actual parameters cannot exceed number of formal parametersD:\a\_work\1\s\onnxruntime\core\graph\function.ccinitializer != nullptronnxruntime::FunctionImpl::FunctionImpl is not found or is not constant initializer.limitConstrain input and output types.Unsupported type:Can not get shape initializer data!deltaUnsupported non-raw-data data type!D:\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.AWmemory_seq_lensMWQWInputs 4 shall be 5 dimensionsqkv_hidden_sizes should have 3 elementsInvalid bias shapeInputs 0 shall be 3 dimensionsbeta should have 1 dimension, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.word_embedding should have 2 dimensions and dimension size is known.segment_ids input shall be 2 dimensionsinput_ids shall be 2 dimensionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionsD:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setD:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPositiondims[d_i] < d_maxrB¥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?@@@) is not supported on this device), BIsSigned(Quant GEMM format: AIsSigned(ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!??    SUCCESSFAILNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]CallContext:[%hs] [%hs(%hs)] [%hs] kernelbase.dllRaiseFailFastException%ws error msg: unmap view of file failed. error code: onnxruntime::`anonymous-namespace'::UnmapFileExceptionReturnHrLogHrFailFast%hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %ws Msg:[%ws] genericsystem, error code: DeleteFile() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathUNC\"" when trying to load " "LoadLibrary failed with error FreeLibrary failed with error in library, error code: Failed to find symbol onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameReceived negative size from stat callfile_path == nullptronnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferoffset < 0length > buffer.size()SetFilePointerEx ReadFile fail: unexpected endonnxruntime::`anonymous-namespace'::WindowsEnv::MapFileIntoMemoryopen file mapping , errcode = , allocation_granularity = , mapped_offset = mapped offset must be a multiple of the allocation granularity\/D:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc/\custom_create_thread_fn returned invalid handle.onnxruntime::`anonymous-namespace'::WindowsThread::WindowsThreadkernel32.dllSetThreadDescriptiononnxruntimeFatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation - fail, errcode = open file GetFileSizeEx Invalid fd was supplied: \*onnxruntime::LoopDirD:\a\_work\1\s\onnxruntime\core/platform/path_lib.hunknown erroronnxruntimeISink must be provided.D:\a\_work\1\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::Defaultonnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. Failed to parse path root: D:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRootonnxruntime::Path::Parse/\UnknownEventWaitRevoke"num_run": "core": Nested parallelism not supported": {onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection!current_parallel_sectionn >= 0More work items than threadsonnxruntime::concurrency::ThreadPool::ParallelForonnxruntime::concurrency::ThreadPoolTempl::RunInParalleln <= num_threads_ + 1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.h!points_.empty()LogStart must pair with LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetpoints_.empty()"thread_id": "", "block_size": [": ], "core": DistributionEnqueueDistributionWaitRunProfiler not started yetunnamed_thread_poolonnxruntime::concurrency::ThreadPoolProfiler::Stopenabled_}}D:\a\_work\1\s\onnxruntime\core\common\threadpool.cc", }, "sub_threads": {{"main_thread": {"thread_pool_name": "onnxruntime::ToUTF8Stringlength overflowonnxruntime::ToWideStringD:\a\_work\1\s\onnxruntime\core\common\helper.ccSessionKernelNode"dur" :"tid" :"ph" : "X","ts" :"args" : {"name" :"" : "" : }, } ] onnxruntime::profiling::Profiler::Startsession_logger != nullptrD:\a\_work\1\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::Initializeonnxruntime::profiling::Profiler::EndTimeAndRecordEventMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndProfilingWriting profiler data to file ",[ "pid" :{"cat" : "hnxnpnunexpected failureillegal input path:api-ms-win-eventing-provider-l1-1-0.dllEventSetInformationadvapi32.dll is not a valid datepmam is not a valid day is not a valid year:, [] GetSystemTimePreciseAsFileTimekernel32.dllmap_typesequence_typetensor_typeNull map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Type: is not supported currentlyNull type info for . Invalid ORT format model.Model must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadValueInfoOrtFormatopset id is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatopset import domain is null. Invalid ORT format model.We do not support type [] for now is missing type info.onnxruntime::fbs::utils::SaveValueInfoOrtFormatdim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for Null entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatonnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatZl8n(nn!(it.GetName().empty())(outputs_.size() - 1) == i' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'tensors'' is expected to have field 'strings'' is expected to have field 'type_protos'' is expected to have field 'graphs'' is missing. has unknown expected typeRequired attribute 'Error parsing function body:Duplicate type constraint name(inputs_.size() - 1) == iExtra unparsed input unexpected.ONNX Schema : failed validating the check: ) in op definition.has output size ) has more inputs () than declared ()'s input is marked single but has an empty string in the graph)'s output ) has more outputs ( for operator ' appeared multiple times.Mismatched attribute type in 'Unrecognized attribute: ' is expected to have field 'sparse_tensor'' is expected to have field 't'' is expected to have field 'type_proto'' is expected to have field 'g' typestr: , has unsupported type: has unsupported type has inconsistent type Operator '' has been deprecated since version not in range [min=, max= not in allowed input sizes.) has input size not in allowed output sizes.) has output size optionalInvalid tensor data type opaquemapcomplex64DataTypeUtils::FromDataTypeString - Received invalid data type string complex128Invalid data type tensor(map(optional(Unsuported type proto value case.sparse_tensor(Value(s) to change toThe 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.Value(s) to change to.A value that needs replacing.Label encoder has only one input.Only one of keys_*'s can be set in label encoder.Label encoder has only one output.Input type is not int64 tensor but keys_int64s is setInput type is not string tensor but key_strings is setOnly one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setOutput type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.map(string, int64)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(string, float)map(int64, double)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.map(string, double)An integer vocabulary array.
One and only one of the vocabularies must be defined.A string vocabulary array.
One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type.The size of each input in the input listA string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.The output is a 1-D tensor of string, float, or integer.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.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'.The input must be a tensor of strings or integers, either [N,C] or [C].The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe output is a tensor of strings or integers. Its shape will be the same as the input shape.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.An 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.The input must be a tensor of a numeric type or string. The output will be of the same tensor type.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccValues greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type. The output will be of the same tensor type.The input must be an integer map to either string or float.map(int64, float)map(int64, string)First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The number of support vectors.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.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.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'The input must be a tensor of a numeric type, either [C] or [N,C].The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.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.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates whether to do OvR or multinomial (0=OvR is the default).Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.The input must be a tensor of a numeric type.Weights of the model(s).Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.One of 'MAX,' 'L1,' 'L2'A list of ints.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.A list of floats.An integer.A string.A float.Input's shape should be 1D or 2DThe output will be a tensor of strings or integers.The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]A collection of intercepts.A collection of weights of the model(s).The total number of targets.The weight for each targetDefines 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))The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.node id that this weight is for.The id of the tree that this node is in.The weight for the class in class_id.The index of the class list that each weight is for.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)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.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 weightOnly one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Tree id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Child node if expression is true.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'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.Child node if expression is false.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels. 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 Y types to float/int tensors.Constrain input X and output types to float/int tensors.slopeCoefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of ELU. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.'axis' must be in [ , D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.ccWhether 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.Constrain input and output types to signed numeric tensors.Wrong op_type name for running propagation: broadcasting: () vs (]. Its actual value is: Invalid rank for Constrain input and output types to all tensors.Axis has less than the requested k elements.KValuesIndicesDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain index tensor to int64Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements. X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) Invalid value for attribute axisK input must be of type int64.K input must be a one-dimensional tensor of size 1.Value of beta.Value of alpha. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } data_0List of tensors for Constrain input and output types to numeric tensors.sum X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_ReduceMax = ReduceMax (input)loss = Mul (loss_unweighted, weight_gather)loss_unweighted = Squeeze (loss_N1dd, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss_Ndd = Mul (loss_unweighted, weight_gather) const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) const_ignore_indexconst_zero_floatinput_gather_element = GatherElements (input, transform_targets)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)const_zero_casted = Cast (const_zero_float)loss_NCdd = Neg (input_gather_element_transform)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)squeeze_mask = Squeeze (mask, axes)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)const_one_casted = Cast (const_one_float)const_one_floataxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.const_oneconst_zeroignore_indexexpanded_target = Unsqueeze (target, axes)loss = Squeeze (loss_N1dd, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = ReduceMean (loss_Ndd)loss_Ndd = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)loss = ReduceSum (loss_Ndd)Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If set to 1 will perform the sums in reverse direction.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.input and zero_point pair is expected to have same type.dft_length input must be a scalar.axis attribute value The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.dft_lengthConstrain scalar length types to int64_t.size input must be greater than 0.size input must be a scalar.If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. A {name} window with length: size. The output has the shape: [size]. X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) Shape3Doutput = NegativeLogLikelihoodLoss (X_Log, labels, weights)log_prob = Identity (X_Log)output = NegativeLogLikelihoodLoss (X_Log, labels)labelslog_probSoftmaxCrossEntropyLoss is invalid for a tensor of rank is_onesided and inverse attributes cannot be enabled at the same timeSpecifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.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.Constrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Number of input tensors does not match the operands in the equation.NegativeLogLikelihoodLoss does not match the equation indices.Ellipsis represents incompatible dimensions.Rank of input Einsum expression string.Constrain input and output types to all numerical tensor types.InputsType 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.weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)Target rank must be 1 less than the input rank. loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Weight rank must be 1.Input and target dimension value mismatch.targetlossframe_stepframe_lengthConstrain signal and output to float tensors.sample_ratelower_edge_hertzupper_edge_hertzConstrain to float tensorsConstrain to integer tensors.Constrain to any numerical types.window input must have rank = 1.frame_length input must be scalar.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!signalConstrain the input size to int64_t.Hann { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Constrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Blackmannum_mel_bins input must be scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. dft_length input must be scalar.num_mel_bins Value=Invalid value of attribute 'axis'. Rank= sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain split size to integral tensor.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Invalid value of attribute 'axis'. Accepted range=[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]`. new_axis must be either 0 or 1], Value=Constrain to any tensor type.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).positionInput type for input at index 0 is null. Type info is expected.Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Input type for input at index Element type of inputs are expected to be the same.SequenceConstruct is expected to have at least 1 input. is null. Type info is expected.output_sequenceConstrain input types to any tensor type. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=Input Sequence and Tensor are expected to have type info. Current type is null.input_sequence(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.Constrain output types to any tensor type.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.cc_cond_out_in_itercount_cond_in_cond_seqemptySequenceMap__seqlenadditional_inputsThe graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.Constrain to any tensor or sequence type.SequenceMapout_sequenceConstrain input types to any sequence type.concat_resultInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Graph attribute inferencer for "body" not available outputs. Expected expected to have type info expected to be a sequence typeExpected 1 or more inputs.Expected 1 or more outputs.Graph attribute inferencing returned type information for Invalid ``body`` argument. Expected a graph expected but not providedSequenceMap_loop_bodyInput 0 expected but not providedExpected a sequence type for input 0This 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. 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.Input tensor must have atleast 2 dimensionsauto_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.Attribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizePadding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain input x and its zero point data type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.The pads attribute cannot be used simultaneously with auto_pad attributeConstrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter type to 8-bit integer tensor.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.p value of the Lp norm used to pool over the input data.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsROI 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 specifiedConstrain input and output types to float and 8 bit tensors.Attribute pads has incorrect sizeSecond input tensor has wrong dimensionThe storage order of the tensor. 0 is row major, and 1 is column major.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\defs.ccMaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.The exponent.The number of channels to sum overScaling parameter.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Constrain input and output types to float tensors.Input is ether string UTF-8 or int32/int641-D tensor of floatsMaximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.training_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The bias value added to output. Default is 0.Constrain input to only numeric types.The lambd value for the Shrink formulation. Default is 0.5.Invalid value() for attribute 'axis'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). Constrain input and output to all tensor types.running_meanConstrain mean and variance types to float tensors.running_varConstrain scale and bias types to float tensors.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.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 padsAdditional 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.This number of op outputs should be 1 when Training_mode = False, but it is not.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum). 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). ``` The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. 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. This number of op outputs should be 3 when Training_mode = True, but it is not.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_varinput_meanEpsilon = Cast (FloatEpsilon)NormalizedT = Cast (Normalized)FloatEpsilonType of Mean and InvStdDev. This also specifies stage one's computation precision.Scaled = Mul (NormalizedT, Scale2D)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Constrain input types and output Y type to float tensors.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.Input shape must have either [C] or [1,C] dimensions where C > 0The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"A 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. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } 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.Constrain input types to all numeric tensors.Constrain input/output to boolean tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input to boolean tensor.Constrain output to boolean tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccoptional(seq(tensor(uint8)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.condOnly boolD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph 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, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) typesv_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.v_initialNumber of scan input axes specified () is not equal to number of scan outputs ( axis value ) is not equal to number of scan inputs (Scan input was notNumber of scan output axes specified ( was not a tensor. but subgraphs produce If node has Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Loop 'body' subgraph scan outputs should all be tensors but output was Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output optional(tensor(float16))optional(tensor(float))optional(tensor(int32))optional(tensor(int64))optional(tensor(bool))optional(tensor(complex64))optional(tensor(double))optional(tensor(string))optional(tensor(bfloat16))optional(tensor(complex128))optional(seq(tensor(bfloat16)))optional(seq(tensor(int64)))optional(seq(tensor(float16)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(tensor(uint8))optional(tensor(uint16))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(int8))optional(tensor(int16))optional(tensor(uint32))optional(tensor(uint64))tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.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.initial_state_and_scan_inputsAn 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).An 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.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].All Tensor typesConstrain input types. Casting from complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtooutput = Cast (input)Constrain output types. Casting to complex is not supported.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccInput: expected to have tensor or sparse tensor typetype: does not match type of output: Value of attribute should be of integer type and specify a type. and Output not specifiedaxis must be in [-rank, rank-1]. does not specify a valid type.indices must be in [-rank, rank-1].Invalid attribute perm {}, input shape = {stepstransposedAttribute perm for Transpose has repeated value: A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Input and output types can be of any tensor type.updatesWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).The input is not evenly splittableOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.'step' cannot be 0 for SliceInput steps has incorrect lengthInput axes has invalid dataIncorrect or missing input value for starts and endsInput axes has incorrect lengthInput rank for starts and ends should be the same: (Constrain output to int64 tensor.(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.Input tensor can be of arbitrary type. has rank All inputs to Concat must have same rank. Input Constrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..) and the split dimension of the input (Mismatch between the sum of 'split' () and outputs (Mismatch between number of splits (target_typeInvalid 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.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.reshapedIf 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".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.sizesSupport padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.grid'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 int64Constrain 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) 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.'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapesqueezedexpandedList 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-dimensionalDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).data tensor must have rank >= 1axis must be in [-r, r-1]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).inverse_indicesLast dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorThe 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.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`constant_valueConstrain to all tensor types.Constrain input and output types to all tensor types (including bfloat).(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.'input' must have rank >= 2'sequence_lens' must have rank of 1(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(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).depthConstrain 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. Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. (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. **History** - Version 16 adds bfloat16 to the types allowed (for the second and third parameter). Indices tensor must have rank >= 1'axis' must be in [-rank(indices), rank(indices)-1]Constrain input and output types to all tensor, sequence, and optional types.condition(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).OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Constrain to boolean tensors.Input 'values' must have exactly two elements.(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).Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.selected_indicesInteger 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.default 1; Pooled output Y's height.default 1; Pooled output Y's width.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).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.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccmax_output_boxes_per_classConstrain output type to all tensor or sequence types.Constrain output type to all optional tensor or optional sequence types.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\optional\defs.ccConstrain input type to all tensor and sequence types.OptionalHasElement is expected to have 1 output.OptionalHasElement is expected to have 1 input.Constrain output to a boolean tensor.Constrain input type to optional tensor and optional sequence types.Input must be an optional-type value containing an element with type information.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Type of the element in the optional outputInput type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.(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 float32D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccConstrain input types.Constrain output types to be numerics.value_intvalue_intssparse_valuevalue_stringvalue_stringsvalue_floatvalue_floatsAttribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string. or UNDEFINED. Got: Attribute expected to have a one-dim sparse tensor expected to have: Attribute expected to have a one-dim tensorAll inputs to 'Range' op must be of the same type { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Constrain input types to common numeric type tensors.output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)Constrain output types to all numeric tensors and bool tensors.BernoulliThe standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Input tensor must have rank 2Constrain output types to integral tensors.(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.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Input tensor must be 2-dimensionalConstrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Lower boundary of the output values.The mean of the normal distribution.The shape of the output tensor.Input to 'Range' op should be scalars (Tensor with only one element and shape empty) 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 and output to only numeric types.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.ccInvalid Target shape product of 0Target shape may not have multiple -1 dimensionsInvalid position of 0'step' cannot be 0length of each output. Values should be >= 0.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.) is not equal to the existing rank value (Ranks inferred (Constrain output types to bool, int32, int64, float16, float, double tensors.Attribute 'scales' is required.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthEnding indices (exclusive) of corresponding axis in axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`consumed_inputsNew shapelegacy optimization attribute.tilesThe scale along width dimension. It takes value greater than or equal to 1.width_scaleConstrain tiles and axis's type to int64 tensors.Two interpolation modes: nearest(default), bilinearThe scale along height dimension. It takes value greater than or equal to 1.height_scaleWhich axis to concat on. Default value is 1.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.outputs...rank must be greater than axisList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Which axis to split onlength of each outputOne float, indicates the value to be filled, default is 0paddingsThree modes: constant(default), reflect, edgeWhich axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.List 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`.One float, indicates the value to be filled.Attribute value for pads is requiredAttribute pads has incorrect lengthConstrain input and output types to all tensor and sequence types.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccExponentX_squaredE_XsquaredX_RMEX_squaredX_varianceProcessed_STDVarianceSTDIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). saved_var 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. Constrain mean and variance types to float tensors. It allows all float type for U. 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. (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropoutConstrain output mask types to boolean tensors.The ratio of random dropoutThe epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testvarsaved_meanDilation value along each spatial axis of filter.Stride along each spatial axis.Stride along each axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.One (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) 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.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 = [batch_size, num_directions, 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) First input tensor must have rank 3All Tensor and Sequence typesMismatched type for output Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors or sequences but output Int64 tensorD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output else= then=An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. 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.axes as an input and attribute cannot be specified at the same time.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).Keep the reduced dimension or not, default 1 means keep reduced dimension.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.Constrain input and output types to high-precision and 8 bit numeric tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccThe 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).Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Value of alpha default to 0.2Value of beta default to 0.5X_LogX_shapeX_LogSM_NCDX_LogSMbroadcastPass 1 to enable broadcasting 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. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeIf set, defines the broadcast dimensions. See doc for details.Coefficient of leakage default to 0.01.loss_sumloss_unweightedweight_gatherloss_Nddtransform_targetsexpanded_target_int64const_zero_target_typedweight_gather_sumconst_one_castedsqueeze_maskinput_gather_element_transformconst_zero_castedX_NDCX_NCDweight_gather_temp_1weight_gather_tempD:\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).'shape' input must be 1D tensorloss_N1ddloss_NCddinput_gather_elementexpanded_targetDimension on which to do the sort.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxScalar multiplier for input tensor C, the default value is 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedNumber of top elements to retrieveInvalid value for attribute kIf set, defines the broadcast dimensions.Enable broadcastingD:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccConstrain input to integral tensors.Constrain input to float tensors.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. 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 equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\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.ccThe sequence output for the hidden is optional if 0. Default 0.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.fowardD:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\old.ccInput type was null expected to have sequence typeElement type of input unknown expected to have tensor or sparse tensor type. Got: expected to have map typeElement type of optional input expected to have optional typeElement type of sequence input Mismatch between number of source and target dimensions. Source= expected to have type but instead is nullValue type of map input Key type of map input Input was expected to have sequence type. Got Output was expected to have tensor type. Got Input element type of does not match existing output type of Input was expected to have map type. Got Element type of optional input was unknownInput was expected to have optional type. Got Element type of sequence input was unknownshape input must be 1D tensorInput was expected to have either tensor, sequence, optional or map type. Got Value type of map input was unknownKey type of map input was unknownMismatched tensor element type:Mismatched type: source= target=source optional type missing element type.target sequence type missing element type.source sequence type missing element type.Mismatched sparse tensor element type:Mismatched map tensor key type:target map type missing key type.source map type missing key type.target optional type missing element type.Input was expected to have tensor or sparse tensor type. Got Element type of tensor or sparse tensor input was unknowntarget map type missing value type.source map type missing value type.load external data into raw data for tensor: ParseData type mismatch for tensor: . Expected: Actual:Data size mismatch. Tensor: expected size does not match the actual sizeCannot parse data from external tensors. Please The type of tensor: is undefined so it cannot be parsed.graphraw_dataint64_datastring_dataint32_dataData 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.) 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!' of 'value_info' is required to be non-empty.elem_type' is required but missing.Field 'nameUnrecognized type value case (value_info name: ): value_typekey_typefloat_datasetting data_type field (tensor name: ) to UNDEFINED is not alloweddata_type ==> Context: domainBad node spec for node. Name: OpType: Op registered for is deprecated in domain_version of 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 uniqueNodes in a graph must be topologically sorted, however input '' of node: name: is not output of any previous nodes.attr) has no index values.) must have rank 1 or 2.) must have INT64 type.) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.type field and data field mismatch in attribute NodeProto (name: , type: ) has zero input and zero output.op_typeNo Op registered for with domain_version of No opset import for domain 'Warning: Checker does not support models with experimental ops: ] out of range [0, Sparse tensor indices () has values, but NNZ is ) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () index value at position [sparse_tensor_proto] not in lexicographic sorted order.] out of range.) second dimension size does not match rank of tensor.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values () must have rank 1.values of data_type '' should be stored in field '' instead of 'STRING data (tensor name: Unrecognized data_type (tensor name: ConstantFill' has been used as output names multiple times.stol argument out of rangeinvalid stol argumentUnhandled type: %dError parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).Unexpected attribute type.doc_stringmodel_versionproducer_versionproducer_nameExpected character (line: column: metadata_propsString value expected, but not found.Unexpected literal type.Integer value expected, but not found.Value expected but not found.Unexpected type.Unexpected type in sparse-tensor element type.Expecting primitive type as map key type.Identifier expected but not found.floatstype_protointgraphstensorsstringsopset_importir_versiontype_protossparse_tensors[ParseError at position Error context: (op_type: type is missing. inputs but were provided were provided.The number of graph input cannot be smaller than the number of node inputCannot use the same name as both a subgraph initializer and subgraph input: Graph has , node name: Cannot find missing input: in initializers. Graph initializer names must appear after the actual inputs: opaque_typeoptional_typetype case mismatch. existing= inferred=NOT_SETsparse_tensor_type optype type case unsupported for symbolic shape inference. inferred=type case unsupported. existing=key type mismatch from MapProto. existing=Shape inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Cannot infer type and shape for node name . No opset import for domain does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.Data for input already exists.: (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (unk__Inferred shape and existing shape differ in dimension Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'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 (onnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TensorProtoonnx.TypeProto.Optionalonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.FunctionProtoonnx.AttributeProtoonnx.ModelProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.StringStringEntryProtoFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   list count program size pattern length DFA out of memory: startpos: RE2: invalid startpos, endpos pair. [Unexpected re_anchor value: bytemap range SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyUnknown encoding D:\a\_work\1\s\cmake\external\re2\re2\re2.cc': Invalid RE2: text size: endpos: Error parsing 'Error compiling 'pattern too large - compile failedError reverse compiling 'invalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledunexpected 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 errorno argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatormissing ]missing )unexpected )trailing \Regexp not destroyed.Stack not empty.D:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hWalk NULL>p>p >p8>pP>p`?pp?p?p?p>p?p0?pH?p=p=pMissing case in Compiler: should never happenD:\a\_work\1\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classBad hex digit AnyBad call to ParseState::ParsePerlFlagsunknown round: RE2: unexpected op: AddFoldedRange recurses too much.D:\a\_work\1\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of Unhandled D:\a\_work\1\s\cmake\external\re2\re2\nfa.cc in stepUnexpected opcode in short circuit: context does not contain textBad args: nsubmatch= in AddToThreadqunhandled RunStateOnByteUnlocked failed after ResetCacheunhandled opcode: D:\a\_work\1\s\cmake\external\re2\re2\dfa.ccStateSaver failed to restore state.Failed to analyze start state.DeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByte|RunStateOnByteUnlocked failed after ResetGrowStack() failed: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccUnexpected opcode: job_.size() = njob_ = Cannot use SearchOnePass for unanchored matches.D:\a\_work\1\s\cmake\external\re2\re2\onepass.ccD:\a\_work\1\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: D:\a\_work\1\s\cmake\external\re2\re2\simplify.ccCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is Case not handled in ComputeSimple: SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [:ascii:][:^ascii:][:blank:][:^blank:][:alnum:][:^alnum:][:alpha:][:^alpha:][:graph:][:^graph:][:lower:][:^lower:][:cntrl:][:^cntrl:][:digit:][:^digit:]\s\S\w\W\d\D!~ !/:@[`{~ 09AZ__az ~09AZ__az09az0909AZaz [:space:][:^space:][:upper:][:^upper:][:print:][:^print:][:punct:][:^punct:]AZazAZ09AFaf[:word:][:^word:][:xdigit:][:^xdigit:]GpXHpGpXHpGpGpGpGpGp@HpGp@Hp Gp`Hp0Gp`Hp@GpIpPGpIpFpxHpFpxHpGp HpGp HpGpHpGpHpGpPHpGpPHp`GpGppGpGpGpTHpGpTHpHp8HpHp8HpHpHpHpHpHppHpHppHpHpIpHpIp Ip(Hp0Ip(Hp@IpIpPIpIpCarianCaucasian_AlbanianCcCfBrailleBugineseBuhidCanadian_AboriginalCoCommonCopticCsChakmaChamCherokeeChorasmianAnatolian_HieroglyphsArabicArmenianAvestanAdlamAhomBengaliBhaiksukiBopomofoBrahmiBalineseBamumBassa_VahBatakLtLuLycianLydianLisuLlLmLoManichaeanMarchenMasaram_GondiMcMahajaniMakasarMalayalamMandaicKharoshthiKhitan_Small_ScriptKhmerKhojkiKaithiKannadaKatakanaKayah_LiLepchaLimbuLinear_ALinear_BKhudawadiLLaoLatinHanHangulHanifi_RohingyaHanunooGreekGujaratiGunjala_GondiGurmukhiInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseHatranHebrewHiraganaImperial_AramaicDevanagariDives_AkuruDograDuployanCuneiformCypriotCyrillicDeseretGeorgianGlagoliticGothicGranthaEgyptian_HieroglyphsElbasanElymaicEthiopicSamaritanSaurashtraScSharadaPsPsalter_PahlaviRejangRunicSkSmSoSogdianShavianSiddhamSignWritingSinhalaOsmanyaPahawh_HmongPalmyrenePau_Cin_HauOld_South_ArabianOld_TurkicOriyaOsagePhags_PaPhoenicianPiPoPcPdPePfNlNoNushuNyiakeng_Puachue_HmongNdNew_Tai_LueNewaNkoOld_North_ArabianOld_PermicOld_PersianOld_SogdianOghamOl_ChikiOld_HungarianOld_ItalicMeroitic_CursiveMeroitic_HieroglyphsMiaoMnMeMedefaidrinMeetei_MayekMende_KikakuiMyanmarNNabataeanNandinagariModiMongolianMroMultaniPR:@[`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~__? @ T T 34MO??55OOooPv`!!!!00!0)080:0@[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++.R.//00000 00070<0?00000001111 2_22222X33MM!09..ϩϩ[[jk>?0RTfhk ;@[epp3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq-/= --  ....:.;.@.@.0000000012XXcc ooWW  ? ? 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