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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 ,e8e@eHeCan'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.trainingVIWEFD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)CPUExecutionProviderTensor sequence must contain only primitive typesonnxruntime::TensorSeq::SetTypeelem_type_ != nullptri < tensors_.size()D:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::GetIsTensor()Trying to get a Tensor, but got: D:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableTrying to get a SparseTensor, but got: IsTensorSequence()IsSparseTensor()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_bytes1.11.0The given version [%u] is not supported, only version 1 to %u is supported in this build. T utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. onnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataTensor must always contain primitive types. Found: Invalid index requested for map type.OrtCreateValueImplMapHelpervalue_type != nullptrValue type is not supported yet: ++index < c.size()Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscriptonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::Tensor::Datanot implementedexecution_mode is not validgraph_optimization_level is not valid 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 == 1OutputT0onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.type == dtype_onnxruntime::Tensor::MutableDataRawTensor type mismatch.Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::DataRaw) != new size (IsSameDataType(tensor)onnxruntime::TensorSeq::Addonnxruntime::Tensor::ReshapeTensorSeq: tensor to be added has a different data type.output_ptronnxruntime::OpKernelContext::RequiredOutput is not present.Required output at index p_ml_valueonnxruntime::OpKernelContext::OutputD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.onnxruntime_providers_shared.dllProvider_SetHostonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderSharedLibrary::EnsureD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime::ProviderLibrary::Unloadonnxruntime_providers_cuda.dllonnxruntime::ProviderLibrary::GetGetProvideronnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_rocm.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_migraphx.dllSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryCUDA execution provider is not enabled in this build.invalid unordered_map keyRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputNo requested allocator availableEnv 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.Provided OrtMemoryInfo is nullOrtMemoryInfo is nullProvided allocator is nullOrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccenv_ptr == p_instance_.get()D:\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()xebad cast: stoi argument out of rangeinvalid stoi argumentBad optional access DeviceId:] OrtAllocatorType:Device:[DeviceType: MemoryType:OrtMemoryInfo:[name: id: OrtMemType:CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProviderD:\a\_work\1\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider has already been registered.onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoRelease_State_Create_State_Compute_D:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.h'IsGraphCaptured()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::ReplayGraph4IsOptionalTensor(type)Provided type is not an optional tensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorORTM[json.exception.] MemcpyTransformerother_errorsession.set_denormal_as_zerosession.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.use_ort_model_bytes_directlysession.intra_op.allow_spinningsession.inter_op.allow_spinningmemory.enable_memory_arena_shrinkagesession.dynamic_block_baseoptimization.minimal_build_optimizations() node with name '%Y-%m-%d_%H-%M-%SD:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::`anonymous-namespace'::VerifyEachNodeIsAssignedToAnEpImpl (Could not find an implementation for All nodes have been placed on [].onnxruntime::`anonymous-namespace'::VerifyEachNodeIsAssignedToAnEpNode placements value of 'save' is only valid when saving an ORT format model.save Provider: [: [ are '0' and '1'. The environment variable contained the value: Invalid value for applyModelProto needs to be parsed to check for ORT config within itonnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configThe only supported values for the environment variable Flush-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.) in kernel registries for '.onnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelonnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesonnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesImplonnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesImpl::::operator ()Failed to find kernel def hash (Initializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: Session has already been initialized.Model was not loaded.Model was not loadedonnxruntime::InferenceSession::InitializeThis 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 will use the allocator registered with the environment.Adding default CPU execution provider.This 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. 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.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.NchwcTransformerUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.loading_ort_format && serialized_session_state != nullptr Please fix either the inputs or the model.session_initializationEncountered unknown exception in Initialize()Session successfully initialized. for the following indices Invalid rank for input: Got: Expected: )) , expected: (Unexpected input data type. Actual: ( index: Got invalid dimensions for input: Size mismatch: feed_names has elements, but feeds has elements.))tensorInput with name: is not expected to be of type tensor.Invalid Feed Input Name: is not expected to be of type tensor sequence.sparse_tensor is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputsp_fetches->size(): At least one output should be requested.Output vector pointer is NULLseqReplaying the captured CUDA Graph for this model with tag: Invalid Output Name:Output vector incorrectly sized: output_names.size(): Running with tag: Session not initialized.Session was not initializedonnxruntime::InferenceSession::Runonnxruntime::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_runEncountered unknown exception in Run()onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingProfiler is disabled. 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: error message: The registered allocator for device-id combination is not an arena based allocator: Did not find an arena based allocator registered for device-id run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformers/961c151d2e87f2686a955a9be24d316f1362bf21 3.9.1.jsonLoad model from failed:model_loading_uri_ failed. Only bytes were able to be read.onnxruntime::LoadOrtModelByteslist too longonnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0com.microsoft.nchwccom.microsoft.experimentalorg.pytorch.atencom.microsoftcom.ms.internal.nhwc Target=Unsupported Source/Target type=[ShapeInferenceError] Mismatch between source and target type. Source=tensor(uint32)unknowntensor(float16)tensor(int64)tensor(int32)tensor(uint64)tensor(uint8)tensor(bfloat16)tensor(double)tensor(float)seq(tensor(uint8))tensor(int16)tensor(int8)tensor(uint16)seq(tensor(int8))seq(tensor(uint64))seq(tensor(uint32))seq(tensor(uint16))seq(tensor(float16))seq(tensor(int64))seq(tensor(int32))seq(tensor(int16))tensor(bool)tensor(string)seq(tensor(double))seq(tensor(float))seq(tensor(bool))seq(tensor(string))tensor(complex128)tensor(complex64)seq(tensor(bfloat16))seq(tensor(complex128))seq(tensor(complex64))Schema error: version: ) from file , but it is already registered from file line , but its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h)., but its version is not in the inclusive range [Only CPU devices are supported for now.An allocator for this device has already been registered for sharing.Only CPU allocators can be shared between multiple sessions for now.stringNo allocator for this device has been registered for sharing.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.D:\a\_work\1\s\onnxruntime\core\session\environment.ccinter-opintra-opException caught: outputXinputMemcpyFromHostMemcpyToHostConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.Yvalueparse error at line , column parse_errorort_configout_of_rangetype_errorinvalid_iteratorD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: session_optionsSetting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting intra_op_num_threads to Parallel modeSequential modeonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: Setting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to Unsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to onnxruntime::SetEnableProfilingORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.intra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.execution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsintra_op_num_threads option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelexecution_mode option in the model file must be an integer' not foundIgnoring unsupported session option in ORT config: enable_profiling option in the model file must be an integercannot use at() with key 'cannot use key() for non-object iteratorsarrayobjectnullinvalid map keynumberdiscardedbinarybooleancannot compare iterators of different containerscannot get value; last read: '- while parsing syntax error invalid BOM; must be 0xEF 0xBB 0xBF if given; expected unexpected false literaltrue literalinvalid literal'['number literalstring literalnull literal':''}'']''{''[', '{', or a literalend of input','invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid number; expected digit after '-'unknown tokeninvalid string: missing closing quoteinvalid comment; expecting '/' or '*' after '/'invalid comment; missing closing '*/'invalid number; expected digit after exponent signinvalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: ill-formed UTF-8 bytevector too longnumber overflow parsing 'object separatorobject keyiterator does not fit current valuecannot use erase() with iterator out of rangetype must be number, but is InsertedCast_cast node to cast from float16 to float32 on cpuCasttoonnxruntime::GraphTransformer::RecurseD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hdtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()RemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImplACLExecutionProviderArmNNExecutionProviderROCMExecutionProvidersession.enable_quant_qdq_cleanupoptimization.enable_gelu_approximationsession.qdqisint8allowedNhwcTransformerQDQFinalCleanupTransformersession.disable_quant_qdqBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationConvAddFusionAttentionFusionBiasDropoutFusionBiasGeluFusionDivEliminateDropoutDropoutDynamicQuantizeMatMulFusionConvConvBNFusionConvMulFusionDivMulFusionGeluApproximationGeluFusionGemmActivationFusionGemmSumFusionEmbedLayerNormFusionExpandEliminationExpandFastGeluFusionLayerNormFusionSimplifiedLayerNormFusionMatMulAddFusionMatMulIntegerToFloatFusionGemmGemmTransposeFusionEliminateIdentityIdentityNotWhereFusionWhereClipQuantRewriteClipMatMulScaleFusionMatmulTransposeFusionNoopEliminationAddFuseReluClipReshapeFusionSkipLayerNormFusionEliminateSliceQDQPropagationTransformerQDQS8ToU8TransformerReluQuantRewriteRelu_RuleBasedTransformerLevelUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRewriteRulesSliceTransposeOptimizerUnsqueezeEliminationUnsqueezeD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformeronnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildVitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::MemcpyTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'Memcpyonnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.ccPermutation length perm.size() == gsl::narrow_cast(shape_proto->dim_size())onnxruntime::ApiValueInfo::PermuteDimsD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\optimizer_api_impl.cc does not match rank size >= 0onnxruntime::ApiTensor::NumElementsonnxruntime::ApiTensor::DataNo NodeArg found for name out of bounds for shape Permutation entry 0 <= p && p_int < shape_proto->dim_size()Failed to get size of TensorProtoonnxruntime::ApiGraph::TransposeInitializerFailed to find initializer to reshape with name onnxruntime::ApiGraph::ReshapeInitializer to have different number of elementsnode_arg_ != nullptronnxruntime::ApiGraph::GetValueInfoFailed to find initializer for name: successSqueezeGatherTranspose provided for op: Cannot reshape initializer new_num_elts == old_num_eltsAdded in transpose optimizeropset_import_iter != domain_to_version_map.end()An entry for this node should be added in onnx_ops_available_versions and static_kernel_hashes map.Transpose Optimizer is adding an unexpected node: iter != onnx_ops_available_versions.end()Transpose optimizer is expected to add only onnx domain ops. Domain: domain == kOnnxDomainonnxruntime::GetSinceVersionForNewOpOnnx domain not found in opset imports.ResizeFusedConvQLinearAveragePoolQLinearGlobalAveragePoolconst_transpose_optimizerExisting destination type is not compatible with source type.dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)onnxruntime::ApiGraph::CopyValueInfochannels_lastOptimization after layout transformation failed: D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::GraphTransformerManager::ApplyTransformerssize_ == sizesize is differentReadExternalRawData() failed: unsupported data type: D:\a\_work\1\s\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::InitializerCould not find OrtValue with name 'startConstantFoldingDequantizeLinearendCould not find a CPU kernel and hence can't constant fold . Can't constant fold fetches.size() == node->OutputDefs().size()D:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImpl node 'ShapeUnsupported output type of Unsupported data type: Constant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializershapeinput_node.InputDefs().size() == 2 && scale_and_index->second < 2mul_inputs.size() == 2output_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetInputNodeMergesdiv_inputs.size() == 2D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccMulonnxruntime::`anonymous-namespace'::GetScaleFromNode_FusedMatMulAndScaleFused MatMul and Scaleonnxruntime::MatMulScaleFusion::ApplyImplMatMulonnxruntime::`anonymous-namespace'::GetOutputNodeMergesalphaFusedMatMulonnxruntime::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::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()input_indices.size() == expected_values.size() && input_indices.size() > 0D:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.honnxruntime::AttentionFusionHelper::CheckSliceParametersonnxruntime::AttentionFusionHelper::MatchGemmSubgraphStart MatchGemmSubgraphConcatReshapeSlice ends is less than INT_MAXSlice does not have enough number of inputsSlice parameter is not expected. Input index:Expected value:Gemm bias shape not expectedGemm bias is not constantconcat first input value is not -1CheckSliceParameters return falseInput of reshape_before_gemm is not the input of subgraphFaild to match gemm pathGemm does not have 3 inputsOutput edge count not expected for nodes in gemm pathaxesOutput edge count not expected for nodes in gemm gather pathaxisunsqueeze_after_gather axes value not expectedconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsFaild to match gemm gather pathconcat_after_gather input 2 does not have expected valueGemm bias is not constant initializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm weight is not constant initializerGemm bias shape is not expectedgather input 1 value is not expectedgather axis value not expectedStart ValidateGemmInitializerPass MatchGemmSubgraphThis optimizer does not support external data for unidirectional mask right nowunidir mask shape not expectedExpect mask data type is uint8 or floatMask is neither unidirectional nor all onesPass ValidateGemmInitializerGemm weight shape is not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask is not constantOutput edge count not expected for nodes in path 1 of unidirectional maskFaild to match path 1 for unidirectional maskCheckSliceParameters returns false for last_sliceDiv and Shape1 does not have edgeonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphSubFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for mask_sliceFaild to match path 2 for unidirectional maskCheckSliceParameters returns false for slice1onnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeSoftmaxOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskDiv and Shape does not have edgePass MatchUnidirMaskSubgraphCheckSliceParameters return false for slice2mask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1mask_mul const input not matchedmask_sub const input not matchedFailed to find path for maskOutput edge count not expected for SoftmaxSoftmax attribute axis is expected to be 3Output edge count not expected for mask nodesFailed to find shape pathwhere const not matched.Failed to find reshape shape path 1equal const not matched.Start MatchInputMaskSubgraphDistilBertPass MatchInputMaskSubgraphFailed to find mask pathEqualFailed to find path for past_konnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for present_v and past_vFailed to find path for present_kgather indices not matched.Failed to find reshape shape path 2Start MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertpresent_v_unsqueeze axes value not expectedpresent_k_unsqueeze axes value not expectedpast_k_gather indices != 0past_v_gather indices != 1permFailed to match v_concatpresent_k_transpose perm attribute not matchedpast_k_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathVStart CheckNodesInPathVFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Output edge count not expected for nodes in path vOutput edge count not expected for nodes in past subgraphpast_v_gather and past_k_gather does not have same past inputonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapePass MatchPastSubgraphStart CheckNodesInPathQreshape initializer value is not expectedq_reshape const not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathQhidden_size != num_heads * head_sizeFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Pass CheckNodesInPathVv_reshape initializer value is not expectedk_transpose has not perm attributeonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_reshape const not matchedk_transpose perm attribute not matchedq_transpose perm attribute not matchedqk_div const not matched.Start CheckNodesInPathKPass CheckNodesInPathQonnxruntime::AttentionFusionHelper::FuseGptAttentionStart FuseGptAttentionSplitFaild to find path to qkv_matmulMask_Int32Pass CheckNodesInPathKCast mask from int64 to int32q and v are not from same Split nodeFailed to find path for qUsing transpose optimized patternCheckNodesInPathQ returns falseCheckNodesInPathV return falseFaild to find path v to SplitMatchUnidirMaskSubgraph returns NULLMatchInputMaskSubgraph returns falseFused Attention subgraphs MatchPastSubgraph returns falsenum_headsAttentionFailed to find path for kopt_k_transpose perm attribute not matchedCheckNodesInPathK returns falsek and v are not from same Split nodeonnxruntime::ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccFused an attention node for GPT.unidirectionalqkv_biasqkv_weightsq root should be layer normalizationonnxruntime::FuseSubGraphQKImplk root is not layer normq_matmul and q_add shape not matchedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplTotal fused Attention node count: shape of layer norm bias tensor not expectedFused an attention node.onnxruntime::FuseSubGraphQKFaild to find path vonnxruntime::FuseSubGraphQKDistilBertFailed to load Q, K and V weights, or data type is not float or float16.k_matmul and k_add shape not matchedFailed to convert mask to int32Failed to load Q, K and V bias tensors, or data type is not float or float16.Output edge count not expected for Add or MatMul in path vonnxruntime::AttentionFusion::FuseSubGraphFailed in match input mask subgraphFailed in match v_matmul and v_add input shapedata type is not supportedonnxruntime::Initializer::ToProtobn_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_var_tensor_protobn_mean_tensor_protoepsilononnxruntime::ConvBNFusion::Applybn_scale_tensor_protoBatchNormalizationconv_B_tensor_protoconv_W_tensor_protoConvBnFusion_BN_B_ConvBnFusion_W_qdq_s8_to_u8_quantonnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_zp_conversionD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccMaxPoolArgMaxdropDQ2DQSigmoidQDQSelectorActionTransformer*DQAveragePool1DQGlobalAveragePoolLeakyRelufonnxruntime::NodesToOptimize::GetNodeD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)TanhFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)D:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccactivationactivation != nullptronnxruntime::`anonymous-namespace'::actions::FuseConvActivation::ExtraAttributesHardSigmoidExpected activation node.ConvAddReluConvActivationFusiononnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesConvActExpected Conv then Add.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"betaactivation_paramsUnsqueezeElimination_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::Applymindata type is not supported.FuseReluClip_D:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantBiasGeluFastGeluTotal Gelu Approximation (FastGelu) node count: D:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationGeluonnxruntime::GeluApproximation::ApplyImplf(ffFused 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_SubgraphstartsendsD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccnew_gemm_output_defs.size() == 1new_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::ApplytransAtransBsum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())!sum_input_movedgraph.RemoveNode(gemm_node.Index())gemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0Fused Gemm with Sum_sum_transformedonnxruntime::GemmSumFusion::SatisfyConditionSumother_sum_input != nullptr and node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodeD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndnode != nullptronnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndInvalid node index node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputsrc_node || dst_nodeonnxruntime::`anonymous-namespace'::InsertQDQPairQuantizeLinearAt least one graph node must be specified in the propagation edge._pre_qnode ("Inserting Q/DQ pair between at NodeArg "", index: D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cc".src_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdge_dqFailed to set op schema for added DQ node._qFailed to set op schema for added Q node.Inserted by QDQPropagationTransformeronnxruntime::QDQPropagationTransformer::ApplyImplonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::PropagateQBackwarddst_node != nullptronnxruntime::`anonymous-namespace'::GetNextPropagationEdgeCast Input from int64 to int32_Int32Output edge count not expected for nodes in path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.Input 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_CastNonZerotwo paths share the same shapeThe parent of two shape nodes are expected to be input_ids.Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.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.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.The first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.RangeConstantOfShapeEmbedLayerNormalizationfused EmbedLayerNorm subgraphs mask_indexposition_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 get position embedding weights.Failed to match position embedding subgraph.Position embedding data type shall be float or float16.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.onnxruntime::EmbedLayerNormFusion::ApplyImplonnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). Gamma 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.̼+MatMulIntegerToFloatD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplDynamicQuantizeMatMulDynamicQuantizeLinearwhich does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.FreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input '' with a fixed dimension size gemmfused Matmul and Add D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplEluSeluSoftplusSoftsignScaledTanhParametricSoftplusThresholdedReluonnxruntime::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.ccFused Gemm with Transpose_transformedonnxruntime::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 nodestranspose_node.InputDefs().size() == 1onnxruntime::MatmulTransposeFusion::ApplyImpltransBatchAtransBatchBfused MatMul and Transpose MatMul_With_Transposedeque too longonnxruntime::ConvMulFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_onnxruntime::LayerNormFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccReduceMeanSqrtPowfused LayerNorm subgraphs onnxruntime::SimplifiedLayerNormFusion::ApplyImplCast_Scalecast scale of layer normSimplifiedLayerNormalizationlayer_norm_outcast output of layer normf(f f'7onnxruntime::SkipLayerNormFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs SkipLayerNormalization̼+f(ffreorderpadsReorderInputBuffer overflowgrouponnxruntime::NchwcTransformerImpl::TransformConvoutput_channels <= nchwc_output_channels_nchwcD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.ccspatialreshapedilationsstridesauto_padkernel_shapeSAME_UPPERNOTSETVALIDSAME_LOWERasymmetriccoordinate_transformation_modehalf_pixelalign_cornersfloornearest_modescalesUpsampleReorderOutput_bn_nchwcmodechannelslinearnearestonnxruntime::NchwcTransformer::ApplyImplGlobalMaxPool into softmax(input + bias)softmax_axisBiasSoftmax 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.ccPe(;fNot eliminating output D:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::CommonSubexpressionElimination::ApplyImpl] because it's the graph's output.representative.output_index != kInvalidOutputIndex of node [onnxruntime::QDQFinalCleanupTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.ccNotTranspose optimizer failed: D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.cconnxruntime::TransposeOptimizer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplQLinearConvonnxruntime::BiasDropoutFusion::ApplyImplBiasDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccseedfused 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 Geluonnxruntime::BiasGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccfast_gelu_outputonnxruntime::FastGeluFusion::ApplyImplfused GPT2Gelu subgraphs GPT2Geluf(ff(fadd_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_Add_B_ConvAddFusion_B_noop_with_empty_axeskeepdimsLogExpstorage_orderNhwcMaxPoolHardSwishCeluSignShrinkSinhTanCosSinFloorCeilAbsReciprocalIsNaNIsInfRoundNegXorOrAndMinGreaterBitShiftPReluModAtanAcosAsinCoshMaxAtanhAcoshAsinhReduceProdReduceMinReduceMaxReduceLogSumExpArgMinReduceL2ReduceL1ReduceSumSquareMeanLessOrEqualGreaterOrEqualLessReduceLogSumReduceSumPadCastLikecom.microsoft.com.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearMulGridSampleLRNUnsupported ONNX opsetcom.microsoft.QLinearReduceMeanLogSoftmaxHardmaxTilecom.microsoft.QLinearAddcom.microsoft.QLinearConcatcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearSigmoidD:\a\_work\1\s\onnxruntime\core\optimizer\initializer.cconnxruntime::Initializer::ReadExternalRawDataExternal data type must not be UNDEFINED or STRING.TensorProto external data size mismatch. Computed size: , external_data.length: model_path must not be empty. Ensure that a path is provided when the model is created or loaded.RandomUniformMultinomialRandomNormalLikeRandomUniformLikeRandomNormalmaxD:\a\_work\1\s\onnxruntime\core\optimizer\utils.cconnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of fffȦffallocator_ptr_Failed to get allocator for optimizerD:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()Tried to allocate without valid type information, ort_value index=onnxruntime::OptimizerExecutionFrame::Info::Infoonnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointq_or_dq_input_defs.size() >= 2D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::RegisterProducedNodesWithGraphToo many produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations for transformer Matched onnxruntime::MatchAndProcessonnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsNot enough produced nodes in the runtime optimization record.Existing registration with name inserted_in_name_to_entryonnxruntime::SelectorActionRegistry::RegisterSelectorAndActionD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccApplying runtime optimization action Missing action Nodes to optimize are not valid, skipping action.init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aQGemmgenerated at runtimeQLinearinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8onnxruntime::ReplaceWithNew::Runonnxruntime::ReplaceWithNew::RunForSaveFailed to set node op schema.Failed to remove node.onnxruntime::MergeIntoTarget::RunD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::CreateReplacementNodeonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::MoveInputOutputnode_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.target_node != NodesToOptimizeIndices::kEmptyNodeIndexIndex out of rangeonnxruntime::`anonymous-namespace'::MoveInputOutputImplD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.ccNode index value is too large to save to ORT format model: RknpuExecutionProviderCoreMLExecutionProviderTvmExecutionProviderDnnlExecutionProviderOpenVINOExecutionProviderNupharExecutionProviderNnapiExecutionProviderhePexAf(;ffffhfXAfбfPf;f:fe8f0eonnxruntime::CPUExecutionProvider::GetKernelRegistryD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsperm: is repeated.Attribute perm of Transpose has an invalid value. Value does not align with rank of input data: [ onnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max() is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.ccMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.input_tensor_ptr != nullptrMismatched data types between input and output Tensors. onnxruntime::Transpose::Computeonnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks)Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)center_point_box0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].boxes and scores should have same num_batches.scores must be a 3D tensor.The most inner dimension in boxes must have 4 data.boxes and scores should have same spatial_dimension.D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeboxes must be a 3D tensor.scores_tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseboxes_tensorNonMaxSuppressionoutput != nullptronnxruntime::NonMaxSuppression::ComputeTindD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeMissing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBaseinfo.GetAttr("axis", &axis_).IsOK()Gather Tind type not supported in this build.indices element out of data bounds, idx= must be within the inclusive range [info.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBaseonnxruntime::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 != nullptronnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncecalled_ == 1onnxruntime::BroadcastIterator::AppendD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hCan broadcast 0 by 0 or 1. is invalid.Attempting to broadcast an axis by a dimension other than 1. by onnxruntime::BroadcastIterator::Initaxis == 1 || axis == largestonnxruntime::TensorAllocator::TensorAllocator) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) for tensor of length:onnxruntime::InputBroadcaster::AdvanceBystart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [onnxruntime::Broadcaster::Broadcasterlargest <= 1offset % span_size_ == 0InputBroadcaster can only start at span boundary!Must have 1 or more inputsUnsupported X type: onnxruntime::Min_6::ComputeinputCount >= 1T1fmod must have value either 0 or 1fmodonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)Tensor with shape information must be 1 dimensional.onnxruntime::ExpandBroadcastLooperonnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1data_1.Shape() == shapeonnxruntime::Mean_6::Compute!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.All inputs must have the same shapeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Max_6::Computedata_n.Shape() == shapeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::mod_internal::CallModImpl::operator ()fmod attribute must be true for floating point typesonnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()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 >::Computecur1 == end1onnxruntime::Sum_6::Computecur_out == end_outonnxruntime::BitShift::Compute::::operator ()onnxruntime::Sum_6::Computeinput_count >= 1BroadcastLooper requires two tensors as input.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()onnxruntime::BroadcastLooperhelper.HaveTwoTensorInputs()onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.Unsupported Y type: onnxruntime::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 >::ElementWiseKerneldirectiononnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftInvalid direction value of 'onnxruntime::BitShift::BitShiftLEFTonnxruntime::BitShift::BitShift'. Valid values are 'LEFT' or 'RIGHT'.RIGHTonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOonnxruntime::scan::detail::OutputIterator::GetOutputD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hAttempt to retrieve final output before it was set.final_output_mlvalue_gsl::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.ccbodyinfo.GetAttr("body", &proto).IsOK()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_info_ == nullptronnxruntime::Scan<9>::SetupSubgraphExecutionInfoonnxruntime::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::SetupInputs of Invalid value in scan_input_axes for input Scanonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for ''is defined.No attribute with name:'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 >::Computeunknown kernel typeonnxruntime::functors::ElementWiseRangedTransform::CreateD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelreductioninput shape: input tensor and indices tensor must has rank larger than 0. ScatterND, indices shape: CPU execution provider: BFloat16 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: MLFloat16 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 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. last dimension of indices must not be larger than rank of input tensor, data shape: onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()onnxruntime::PrepareForComputeinvalid indice found, indice = onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()CPU execution provider: MLFloat16 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 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.ScatterElementsCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.ScatterIndices type is not supported. at pos=Indices dim=Indices must have the same rank as Input. Indices rank= is greater than input dim=Indices vs updates dimensions differs at position=. Input rank=Indices and updates must have the same rank vs data type is different from updates typenoneonnxruntime::Scatter,class std::allocator > > >::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccT2Last dimension of grid: , expect 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cconnxruntime::GridSample::Computegrid_dims[0] == NGrid batch size does not match input batch size Only 4-D tensor is supportedgrid_dims[3] == 2onnxruntime::GridSample::GridSamplemode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"mode "" 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.houtput_widthoutput_heightD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBaseInvalid mode of value specified. It should be either avg or maxavgspatial_scalesampling_ratio_ >= 0Sampling ratio should be >=0, but it was sampling_ratioFirst 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 ptrRoiAlignD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:LoopBILoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: 'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::InitInconsistent shape in loop output for output. Expected: Got:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsthen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrinfo.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()then_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.Ifonnxruntime::IfImpl::ExecuteFailed to create output tensor for If output Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for onnxruntime::IfImpl::InitializeSubgraph SessionState was not found for '' attribute.onnxruntime::If::Computeonnxruntime::IdentityOp<1>::ComputeUnable to get an allocatorD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::Optionalattr->has_tp()Optional op must have a TypeProto in the 'type' attribute if the attribute is presenttypeonnxruntime::OptionalGetElement::ComputeTrying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::Optional::ComputeOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.cconnxruntime::PropagateInputOrtValueToFirstOutputOptionalGetElementOptionalHasElementOptionalObad allocationUonnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::ComputeInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: training_modemomentumTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormforwardreversebidirectionalRNNonnxruntime::RNN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccAffinecliplayoutinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNND:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betaactivationsinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0clip_ > 0.finput_forgetsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 3LSTMonnxruntime::DeepCpuLstmOp::PrePackD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::Compute'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()onnxruntime::LSTMBase::LSTMBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()activation_func_names.size() == static_cast(num_directions_) * 2GRUGRU operator does not support double yetInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::ComputeImpllda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointeronnxruntime::Tensor::MutableDataAsSpan, requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeA dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()A shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1upperinfo.GetAttr("upper", &temp).IsOK()onnxruntime::Trilu::TriluD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hTriluk should be a 1-D or 0-D tensor.IsScalarOr1ElementVector(k)onnxruntime::Trilu::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccInput tensor should have a rank of at least 2Unsupported input data type of dims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::InitAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`CumSumCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::Computeexclusivestarts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::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.cconnxruntime::Hardmax::Compute. N=, D=Hardmax inputs N, D and N * D must be < onnxruntime::UpsampleBase::StringToUpsampleModemode attribute is tf_half_pixel_for_nnpytorch_half_pixel] is not supportted!tf_crop_and_resizeonnxruntime::UpsampleBase::StringToCoordinateTransformationModecoordinate_transform_mode:[ceilround_prefer_ceilnearest_mode:[] is not supported!Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModeonnxruntime::UpsampleBase::ScalesValidationscale >= 1info.GetAttr("mode", &mode).IsOK()cubicD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBaseextrapolation_valueinfo.GetAttrs("scales", scales_).IsOK()yet this opset model uses the deprecated attributeround_prefer_floor`tf_half_pixel_for_nn` is deprecated since opset 13, exclude_outsidecubic_coeff_a or exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to . It can only be (default) or scale > 0Scale value should be greater than 0.Upsample operatorResize operatorscales.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)'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)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales_size > 0scales size should be greater than 0.onnxruntime::UpsampleBase::ParseScalesDataonnxruntime::ValidateFastReduceKRKfast_shape.size() == 3onnxruntime::ValidateFastReduceRKRfast_shape[0] * fast_shape[2] == output.Shape().Size()count == 1Reduction on all axes, output size should be 1.onnxruntime::ValidateCommonFastReduceonnxruntime::ValidateNoTransposeReduceaxes_tensor->Shape().NumDimensions() == 1An axes tensor must be a vector tensor.onnxruntime::ValidateKeepDimsCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:last_loop_red_size > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyprojected_index.size() > 0last_loop_size > 0onnxruntime::ValidateMustBeOverloadedmust be overloaded.fast_shape.size() == 2Only works on matrices with two dimensions.Output size mismatch.onnxruntime::ValidateFastReduceKRonnxruntime::ValidateFastReduceRKfast_shape[0] == output.Shape().Size()Only works on matrices with three dimensions.fast_shape[1] == output.Shape().Size()info.GetAttr("keepdims", &keepdims).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseselect_last_indexonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseconstantedgereflectonnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueInvalid 'pads' attribute valueD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hPads tensor size should be equal to twice the input dimension count pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::PadValueFromFloatpads_tensor.IsDataType()Pads tensor should be an INT64 tensorPads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]onnxruntime::Pad::ComputeInput tensor has no dimensionsonnxruntime::PadImpldata_rank > 0data_rank * 2 == pads.size()'pads' has wrong number of valuesonnxruntime::PadInputWithDimValueOfZeroGatherNDbatch_dimsGatherNDBase PrepareForCompute: Input count mismatchindicesonnxruntime::GatherND::Computeinput_tensor != nullptr && indices_tensor != nullptrindices tensor data type not supportedD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor must has rank larger than 0invalid index found, index = X input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!onnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeNull input ptrblocksizeAttribute blocksize is not set.DepthToSpaceinput count mismatchSpaceToDepthUnsupported input type in SpaceToDepth op: onnxruntime::DepthToSpace::Computeonnxruntime::SpaceToDepth::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceDepth ops require a 4-D input. Provided rank: info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseDepthToSpace requires input depth to be a multiple of (block_size * blok_size)SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeonnxruntime::DepthToSpace::DepthToSpaceDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedUnsupported input type in DepthToSpace op: GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputonnxruntime::GatherElements::GatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElements]. Actual value is onnxruntime::core_impl::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cc , GatherElements op: Value in indices must be within bounds [onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()Tile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.ccInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the first one is emptyTile doesn't support string type yet!input_tensor.IsDataType()'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor. shape=input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShape must be 1 instead of Dimension of input D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBasesplitInvalid value in 'split' attribute. All values must be > 0 Axis=Input cannot be split evenly on selected axis. Input shape= NumOutputs= Input shape=Cannot split using values in 'split' attribute. Axis= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was onnxruntime::Split::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cc yetSplit operator does not support onnxruntime::Split::ComputeImplAn split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1'axes' has duplicates'axes' has an axis outside of the tensor dimension counthas_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 attributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of Starts and ends shape mismatchEnds must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D array'step' value cannot be 0Cannot slice scalarsData type for starts and ends inputs' is not supported in this build. Got Starts and steps shape mismatchStarts and axes shape mismatchonnxruntime::SliceBase::Computeonnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()input != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeinput_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: got: Cannot concatenate scalarsonnxruntime::ConcatBase::ComputeImplData type mismatchNon concat axis dimensions must match: Axis has mismatched dimensions of new_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeD:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopydst.DataType() == src.DataType()src and dst types must matchonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()counter.current_offset == lastonnxruntime::StridedCopy::::operator ()last >= firstonnxruntime::StridedCopydst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shapeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.hratio input should have a single value.0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1Input is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationinfo.GetAttr("across_channels", &across_channels_).IsOK()across_channelsinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4size_ % 2 == 1size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()biasinfo.GetAttr("size", &size).IsOK()sizegsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenFlattenonnxruntime::Flatten::Computezero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_zero_point must be null or a scalar or 1D tensor or size 1.scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.cconnxruntime::PrepareForQDQzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })DequantizeLinear with type int32 should have no zero point or all zero points should be 0onnxruntime::DequantizeLinear::ComputeSizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperinfo.GetAttr("transB", &temp).IsOK()info.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hGEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasonnxruntime::MatMulComputeHelper::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims and right_num_dims must be >= 1M_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsonnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computemin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max should be a scalar.max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccinvalid stoull argumentstoull argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stod argumentstod argument out of rangeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.onnxruntime::`anonymous-namespace'::Cast::Castonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorsnprintf() failed with return value: snprintf_result > 0onnxruntime::`anonymous-namespace'::CastToStringFailed to write value with snprintf().NaN-INFINF%.8gsnprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1invalid expand shapeonnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum op is not supported yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.ccD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::EinsumEinsum op: There must be atleast one inputEinsumEinsum op: An implementation for the input type equation->info.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeInvalid input shape. Only N can be zero. Got:dilations.size() == kernel_shape.size()onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]Pad should be smaller than kernel.Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()info.GetAttr("p", &p_).IsOK()pD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initonnxruntime::PoolAttributes::InferOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.GlobalLpPoolinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.onnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringinfo.GetAttr("count_include_pad", &temp).IsOK()count_include_padkernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()LpPoolkernel_shape num_dims is not compatible with X num_dims.onnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.Unsupported pooling size.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.cc`InlinedVector::at(size_type)` failed bounds checkUnsupported pooling size : onnxruntime::Pool::Computeonnxruntime::MaxPoolV8::ComputeImplT3Invalid argument for depth; it's not a scalar.OneHotInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsDepth is negative.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::ComputeTopKop_kernel_info.GetAttr("k", &k_temp).IsOK()kD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()sortedop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0onnxruntime::TopkOpset11ConstructorCommononnxruntime::TopkOpset10ConstructorCommonvalue of k must not be negativek tensor should be a 1D tensor of size 1k argument [] should not be greater than specified axis dim value [input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueinput count mismatch, expected 1 input - the tensor to be processedoutput count mismatch, expected 2 outputs to be present for TopK operatorUnsupported tensor type of Uniquestart in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!) specified for sequence of size (D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceEmptyInvalid sequence index (SequenceLengthSonnxruntime::GetSeqIdxSequenceAtData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceEraseonnxruntime::SequenceInsert::Computeonnxruntime::SequenceEmpty::ComputeUnsupported 'dtype' value: onnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertSplitToSequenceViolation of the requirment that all input tensors must have the same data type.) != split_dim_size (SplitToSequence operator does not support SequenceConstructonnxruntime::SequenceErase::Computeonnxruntime::SequenceConstruct::Computenum_inputs >= 1Invalid data type for split tensor split_size_sum (onnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0Split should be > 0onnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0Got nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeConcatFromSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccX num_dims does not match W num_dims. group: kernel_shape is not compatible with W shape. X: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: auto_pad == AutoPadType::NOTSETonnxruntime::ConvAttributes::ConvAttributesA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesConvTransposeonnxruntime::ConvTranspose::DoConvTranspose filter_number: filter number not equal to input channel number.group count is <= 0 num_input_channels: output_paddingoutput_shapeInvalid input shape: onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeonnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. C: Input channels C is not equal to kernel channels * group. kernel channels: onnxruntime::Conv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::ConvAttributes::InferOutputShapeNot enough elements in dilations. Expected: Not enough elements in pads. Expected: Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: M: Output channels M is not divisible by group.pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0info.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpooloutput_shape is smaller than minimum required. output_shape:Shape must be 1 dimensional as it's tensor data of a shape inferred output shape:D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.MaxUnpoolonnxruntime::MaxUnpool::ComputeDetMatrix dimensions are not equal. Square matrix is expectedonnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccCompressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()onnxruntime::Compress::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccx_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cctime_axis < 2Invalid time_axis of batch_axis < 2Invalid batch_axis of . Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()time_axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.hbatch_axis != time_axistime_axis and batch_axis must have different values but both are onnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()batch_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of sequence_lens shape must be {batch_size}. Got:. batch_size=ReverseSequenceInvalid sequence length: . Value must be in range [0,detect_positiveFailed to obtain detect_negativedetect_negativeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfFailed to obtain detect_positiveT4onnxruntime::QLinearConv::UseSharedPrePackedBuffersprepacked_buffers[0].get() == nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.cconnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::ComputeOffsetonnxruntime::QLinearConv::ComputeOutputScaleW_zero_point_data[i] == W_zero_point_valueIsValidQuantParam(W_scale, M)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : zero point of per-channel filter must be sameIsValidQuantParam(W_zero_point, M)QLinearConv : filter zero point shape invalidIsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOffsetIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.ConvIntegeronnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1onnxruntime::MatMulComputeHelper::Compute::::operator ()Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1QLinearMatMulIsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(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 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1*attribute case_change_action has invalid valueNONEUPPERLOWERattribute case_change_action is not setcase_change_actiononnxruntime::StringNormalizer::StringNormalizerattribute is_case_sensitive is not setwstr != wconv_errorStopword contains invalid utf8 charsp.secondDuplicate stopwords not allowed!sw.empty()Empty stopwords not allowedstopwordslocaleStringNormalizeris_case_sensitiveen-USD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cconnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesConversion ErrorConversion Errorbad conversionInput contains invalid utf8 charsInput dimensions are either[C > 0] or [1][C > 0] allowedSingle dimension value must be greater than 0onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension. Output dim value: Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. output_dims[i] == 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.roi_input_idx_ > 0Only one of scales or sizes must be provided as input.sizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::UpsampleNearestResize: 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.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.TfIdfVectorizermode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFTFIDFNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weights must be of equal size but ngram_indexes size: Got weights of size: Non-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexes >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0ngram_counts is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNonemin_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0max_gram_lengthn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0.impl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specified!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()-gramsonnxruntime::ngram_details::PopulateGrams id: Duplicate ngram detected, size: p.first->second->id_ == 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()op_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkEyeLikeEyeLike : Input tensor dimension is not 2t_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseMust have a single dimension of 1t_proto_p->dims()[0] == 1onnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: Must have a valid input shape.onnxruntime::ConstantOfShapeBase >::PrepareComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensiononnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type()) did not match batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executedirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initialize has batch size of but Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::ValidateInputScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hMaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0LpNormalizationop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormMismatch between input data and B: size of B != input channel count Invalid input data: number of dimensions is less than 3: vs. Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::ComputeInstanceNormalizationD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDhighonnxruntime::RandomNormalLike::RandomNormalLikeonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()lowinfo.GetAttr("mean", &mean_).IsOK()meanD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("scale", &scale_).IsOK()scaleInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()Could not infer data type from input tensor with data type D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computenum_classes is < 1batch_size is < 1Invalid data type of num_samples is < 1onnxruntime::RandomNormalLike::Computesample_sizeonnxruntime::RandomUniformLike::RandomUniformLikeonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDOutput type not supported in this build: onnxruntime::MultinomialComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsinfo.GetAttr("default_string", &default_string_).IsOK()default_stringinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64Input of tensor(string) must have output of tensor(int64)LabelEncoderkeys_floatsInput of tensor(int64) must have output of tensor(string)info.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder_Unusedvalues_stringsvalues_floatskeys_stringskeys_int64sdefault_floatvalues_int64sonnxruntime::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<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2 and the number of values is ) must have the same length. However, the number of key is attribtues in LabelEncoder (name: num_keys == num_valuesThe info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2Zipmap does not support empty dim countD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cc] != number of classlabels[Zipmap only supports 1D or 2D input tensorsInput features_per_batch[classlabels_int64sZipMapMust provide classlabels_strings or classlabels_int64s but not both.classlabels_stringsonnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()SOFTMAXLOGISTICSOFTMAX_ZEROSUMTreeEnsembleRegressorInput shape needs to be at least a single dimension.aggregate_functionbase_valuesonnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_nodeidsnodes_treeidsnodes_missing_value_tracks_truenodes_modesnodes_featureidsnodes_hitratesn_targetsnodes_falsenodeidstarget_weightstarget_nodeidstarget_treeidspost_transformtarget_idsnodes_truenodeidsnodes_valuesUnable to find node (truenode). (falsenode).One falsenode is pointing either to itself, either to another tree.onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon (weights).nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_values.size() is already there.target_class_ids.size() == target_class_treeids.size()Node in tree n_targets_or_classes > 0-nodes_falsenodeids.size() == nodes_featureids.size()onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonnodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_modes.size()onnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresthis->base_values_.size() == predictions.size()it->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictionTreeEnsembleClassifierX dims is empty.predictions.size() == 2onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresclass_nodeidsclass_treeidsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeclass_idsonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeclass_weightsonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1LINEARonnxruntime::ml::SVMCommon::SVMCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hkernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()SVMRegressorPOLYkernel_typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccUnexpected mode:num_features == feature_count_onnxruntime::ml::SVMRegressor::Computecoefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()info.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMRegressor::SVMRegressor!coefficients_.empty()one_classsupport_vectorsrhon_supportsUnsupported data type of proba_.size() == probb_.size()classlabels_intsonnxruntime::ml::SVMClassifier::SVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cccoefficients_.size() > 0onnxruntime::ml::SVMClassifier::Computeinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0prob_aprob_bSVMClassifiervectors_per_classD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.honnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of scores.size() == static_cast(expected_num_scores)ScalerEither both scale and offset can be of feature size (Invalid argument: input has empty dimensions.) or 1onnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.ccEmpty scale in attributes!scale_.empty()scale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOp) != (Scale size: (offsetOneHotEncoderUnknown Category and zeros = 0.cats_int64sonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0Invalid normalize value of MAXD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hinfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::Normalizeronnxruntime::ml::MakeNormalizenormD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorinterceptstargetsonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeLinearClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:multi_classonnxruntime::ml::LinearClassifier::LinearClassifiershape_size == out.length()onnxruntime::ml::CastInputToFloatimputed_value_floatsimputed_value_int64sImputeronnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()replaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedonnxruntime::ml::ImputerOp::ComputeInvalid typeEmpty input dimensions.Empty value of imputed values.).) does not match number of inputdimensions values (D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizeronnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccNumber of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()status.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerinputdimensionsinputdimensions attribute must be providedInvalid input type:DictVectorizerinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularystring_vocabularyonnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs("cats_strings", string_categories).IsOK()Input of string must have output of int64CategoryMappernum_entries == int_categories.size()info.GetAttrs("cats_int64s", int_categories).IsOK()Input of int64 must have output of string stof argument out of rangeinvalid stof argument%f0.fInvalid input type of value: Expected std::map or std::mapCastMapUnexpected CAST_TO value of info.GetAttr("map_form", &attr).IsOK()map_formD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()max_map Expected DENSE or SPARSESPARSEDENSEonnxruntime::ml::MakeCastinfo.GetAttr("cast_to", &attr).IsOK()cast_toonnxruntime::ml::MakePackInvalid PACK_MAP value of TO_FLOATInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64TO_STRINGD:\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 BinarizerInput data with index: is NaNthresholdArrayFeatureExtractorInvalid Y argument: index is out of range: Y[] () >=Invalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::Initializeonnxruntime::scan::detail::OutputIterator::operator *cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput!is_concrete_shape_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()Mismatch between expected shape and shape from first output is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextiteration_num_ < sequence_len_validInvalid values in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but did not.onnxruntime::scan::detail::Info::Infonum_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given directions.size() == num_entriesNumber of entries in '' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::OrtValueTensorSlicer::Iterator::operator *Unsupported typeInput initial_h must have shape {affinereluleakyreluInput R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input X must have 3 dimensions only. Actual:}. Actual:Input W must have shape {onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameonnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccA + (M * K) <= A_endweights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fsoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountthresholdedreluscaledtanhhardsigmoideluInput 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 < Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignRight shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Left shape override: Left shape: The override dims are not compatible with given tensor's shape. onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputcandidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_ in the same dimensionAnother operand has a dim value of . The shape is: is incompatible in the dimension 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 inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)onnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Output subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the inputInputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersonnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibledim_iter == rank...onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]Einsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulData types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulInverseinput is expected to have 3 dimensions, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchskip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::LayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormScaleinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelBias size (ImageScaler) does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerDynamicSlice) + scale_[0] (Input's width (Crop) + scale_[1] (Input's height () needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder () + rightBorder (Attribute border needs to be specified with four border elements, got ) + bottomBorder ( input dimensions insteadD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::Computeonnxruntime::contrib::BifurcationDetector::BifurcationDetectorinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_size_ > 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_sizeonnxruntime::contrib::BifurcationDetector::Computesrc_tokens_len >= prev_suffix_match_idx_dataBifurcationDetectorpred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_sizemax_ngram_size_ >= min_ngram_size_max_ngram_size_ > 0token_id < vocab_sizengram_size_ > 0input_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()info.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.honnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockNGramRepeatBlockscores_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::ComputeTidscores_dims[0] == batch_sizeuse_approximationonnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccCDistThe second input of CDist kernel has wrong shape: Input shape dimensions mismatch:The first input of CDist kernel has wrong shape: euclideanonnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaninfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistmetricCropAndResize specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1onnxruntime::contrib::CropAndResize::CropAndResizeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskonnxruntime::contrib::MaxpoolWithMask::ComputeTransposeMatMulinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::ComputekeysMurmurHash3positiveD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cconnxruntime::contrib::SparseToDenseMatMul::ComputeCurrently supporting only 2-D matricesSparseToDenseMatMulCurrently support only COO and CSR(x64) formatsOuter size must be M + 1Expecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesExpecting COO 2-D indices shapeCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: COO m index: is out of bounds of out_left: COO k index: is out of bounds of lhs_right: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator () char_embedding_size attribute: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Conv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: char_embedding_sizeconv_window_sizeembedding_sizeWordConvEmbeddingD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2. Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute.Input string contains invalid utf8 chars: Input string contains invalid utf8 charsCan not digest tokenexp: Can not digest separators: !char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezation!separators.empty()separators must not be empty!tokenexp.empty()Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is settokenexpseparatorsmincharnum_ > 0attribute mincharnum must have a positive valueattribute mincharnum is not setmincharnumattribute pad_value is not setpad_valueD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute mark is not setmarkTokenizerInput dimensions are either [C] or [N][C] allowedtensor(string) expected as inputMatch contains invalid utf8 chars: onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention v weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{}, Got: Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, ], while found!Attention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {}, actural: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTMstatic_cast(activation_func_names.size()) == num_directions_ * 3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmGetFusedActivationAttr(info, activation_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatExpandDimsaxis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.onnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccBeamSearchdecoderinfo.GetAttr("decoder", &proto).IsOK()onnxruntime::contrib::transformers::BeamSearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.ccgpt_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoSupport of 'model_type' != 0 is not implementedSubgraph SessionState was not found for 'decoder' attribute.onnxruntime::contrib::transformers::BeamSearch::Computeonnxruntime::contrib::transformers::BeamSearchImpl::Executeonnxruntime::contrib::transformers::BeamSearchImpl::Initializeonnxruntime::contrib::transformers::BeamSearchImpl::Executeonnxruntime::contrib::transformers::BeamSearchImpl::Initialize'BeamSearch' input min_length should be a scalar. Got shape of 'BeamSearch' input max_length should be a scalar. Got shape of 'BeamSearch' input max_length is required'BeamSearch' input num_beams should be a scalar. Got shape of 'BeamSearch' input num_beams is required'BeamSearch' input num_return_sequences should be a scalar. Got shape of 'BeamSearch' input num_return_sequences is required'BeamSearch' input temperature should be a scalar. Got shape of 'BeamSearch' input temperature is required'BeamSearch' input length_penalty should be a scalar. Got shape of 'BeamSearch' input length_penalty is required'num_return_sequences' has to be smaller or equal to 'num_beams'.Input 'input_ids' is expected to have 2 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' shape does not match with vocab_size, got Input 'prefix_vocab_mask' is expected to have 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'prefix_vocab_mask' shape does not match with vocab_size, got onnxruntime::contrib::transformers::BeamSearchImpl::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchImpl::GenerateNextTokeninfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0qkv_sizes doesn't match the wights dimensionInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of 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)Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 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' is expected to have 4 dimensions, got Input 'extra_add_qk' dimension 0 should be same as batch_size, 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 Input 'extra_add_qk' dimension 3 should be same as sequence_length, got Expect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionBase::AttentionBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hqkv_hidden_sizesAttention cannot have past sequence and extra add qkInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 1 dimension 1 should be 3 times of hidden dimensionhidden_size should be divisiable by num_heads.qkv_hidden_sizes attribute should have 3 elementsqkv_hidden_sizes first element should be same as the secondhidden_size should be divisiable by num_heads:onnxruntime::contrib::AttentionCPUBase::ApplyAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::Attention::Compute4D mask in attention cpu kernel is not supportedSampleOponnxruntime::contrib::QGemm::CheckInputsQGemm : zero point of input a must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zp)QGemm : zero point 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 : scale of input b must be a scalar or 1D tensor of size 1 or Nb_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : zero point and scale of input b should have same shape sizeb_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)QGemm : scale of y must be null or a scalar or 1D tensor of size 1y_scale == nullptr || IsScalarOr1ElementVector(y_scale)TATBTCTYZTYD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationonnxruntime::contrib::QEmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::NhwcMaxPool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonMatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)IsScalarOr1ElementVector(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::MatMulIntegerToFloat::ComputeInput D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePack, 4*} for per-channel quantization. Actual: must have shape {} for per-tensor/layer quantization or shape {R_zero_pointW_zero_pointR_scaleW_scaleDynamicQuantizeLSTM : Weight zero point must be zeroRecurrentWeight point must be constantDynamicQuantizeLSTMQAttentiononnxruntime::contrib::QAttention::Computeinput scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.QLinearMulQLinearAddIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImpltensor_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_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_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 givenQLinearSigmoidQLinearLeakyReluIsScalarOr1ElementVector(tensor_x_scale)Input x_scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.cconnxruntime::contrib::QLinearAveragePool::ComputeImpltensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)input y_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if givenQLinear Pooling unsupported pooling size!input_def_count >= 8 && (input_def_count - 2) % 3 == 0At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcattensor_x_scale->IsDataType()Input scale is not float for input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @onnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 6 && input_count_x3 % 3 == 0Wrong input type encountered for zero point of quantized input @Input scale is not float for quantized input @QLinearConcatonnxruntime::contrib::QLinearGlobalAveragePool::ComputeIsScalarOr1ElementVector(tensor_x_zero_point)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_y_zero_point)MatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.cconnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4output and sum shape must matchonnxruntime::contrib::NchwcPoolBase::NchwcPoolonnxruntime::contrib::NchwcUpsample::Computeinfo.GetAttr("channels_last", &channels_last_).IsOK()info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputonnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2invalid channel countchannels_ > 0onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()X_rank == 4onnxruntime::contrib::ReorderInput::Computetransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'Input 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got [seqno= rows[ row[..!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttentionnot support normalize yet.Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, onnxruntime::contrib::BahdanauAttention::PrepareMemoryunimplemented activation: activation_params count mismatchinput_ids and position_ids shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got Input 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeword_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of input_ids_shape.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_device_helper.cconnxruntime::contrib::BeamSearchCpuDeviceHelper::ExpandInputselement_type == DataTypeImpl::GetType()onnxruntime::contrib::BeamSearchCpuDeviceHelper::CreateInputsinput_ids, position_ids and attention_mask is required to be int32 data typeBeamSearch op: An implementation for the input type logits_shape.NumDimensions() == 3onnxruntime::contrib::BeamSearchCpuDeviceHelper::ProcessLogitsSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeronext_beam_scores_.empty()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamHypotheses::Outputtop_k <= Size()next_scores.size() == next_indices.size()onnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()onnxruntime::contrib::transformers::BeamSearchScorer::Initializehypothesis_buffer_offset_ <= batch_size_ * num_beams_ * max_length_beam_idx == num_beams_beam_hyp.Size() >= gsl::narrow_cast(num_beams_)Batch can only be done if all beams have been generatedoutput_sequences != nullptronnxruntime::contrib::transformers::BeamSearchScorer::Finalizesequences != nullptrD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.cconnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processprefix.length() == prefix_lengthnum_return_sequences shall be a positive integer, got num_beams >= 1 && num_beams <= kMaxNumBeamsnum_beams shall be a positive integer no more than max_length <= kMaxSequenceLengthnum_beams >= num_return_sequencesnum_return_sequences () shall be be no more than num_beams (num_return_sequences >= 1repetition_penalty > 0.0frepetition_penalty shall be greater than 0, got temperature > 0.0ftemperature shall be greater than 0, got model_typeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccno_repeat_ngram_sizepad_token_ideos_token_idearly_stoppingdims.size() == 2input_ids shall have 2 dimensions. Got onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputscontext != nullptr) shall be no more than max_length > sequence_lengthmax_length () shall be greater than input sequence length (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\gpt_subgraph.cconnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in inputs and outputs).position_idssubgraph input 0 shall be named as input_ids, got: input_idsInvalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2past_0subgraph input 2 shall be named as attention_mask, got: attention_masksubgraph input 1 shall be named as position_ids, got: subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 0 shall have length of 2subgraph past state is expected to have 5 dimension, got subgraph input 3 shall be named as past_0, got: present_0subgraph output 0 shall be named as logits, got: logitssubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph input 0 (input_ids) shall have int32 typesubgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph logits output is expected to have 3 dimension, got subgraph input 1 shall be named as present_0, got: subgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 0 (logits) shall be float or float16 data typesubgraph input 2 (attention_mask) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesession_state_ != nullptrSetup must be called before CreateInitialFeedsonnxruntime::contrib::transformers::GptSubgraph::Setupsubgraph output 1 (present_0) shall shall have same data type of logits outputonnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsD:\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 1QlinearBuildLookupTable : 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 1OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled already is not supportedDMLOpenVINO_GPUCudaCudaPinnedSpecified device is not supported.onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccNot implementedUnsupported indices_format passed`anonymous-namespace'::GetIndicesTensorthe ort_value must contain a constructed tensor or sparse tensorArgument is not a tensorOrtApis::GetTensorTypeAndShapeD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.cctype_proto is not of type sequence!type_proto is not of type map!p_type != nullptronnxruntime::Tensor::TensorD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0Tensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrtensor failed memory size calculationtensor size overflowonnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Initonnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.ccexpected a registered ONNX typeonnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypevalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeuint32int64uint64float16uint8int16uint16int32sparse tensor type is not currently registered or supportedMLDataType for: bfloat16(unknown type)tensor type Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::data_types_internal::IsCompatibleD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())onnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatiblethisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatibleutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypefloatbooldoubleint8onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainer(null)onnxruntime::SparseTensor::GetSparseTensorFromOrtValuethe ort_value must contain a constructed sparse tensor must be less than total buffer size: this tensor already has populated sparse_indicesMust have the same size. Got src_size: dst_size: D:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cconnxruntime::`anonymous-namespace'::CopyDataonnxruntime::SparseTensor::AsCooFormat() == SparseFormat::kCooformat_data_.size() == 1UExpecting to contain one index, got: onnxruntime::SparseTensor::AllocateBufferValues size Must contain Coo format. Got: SparseTensor Allocation failed for size: onnxruntime::SparseTensor::UseCooIndicesSparse format must not be set. Already contains format: Use MakeCooStringsNot expecting an allocator setIndex size: must be equal to or twice the values size: onnxruntime::SparseTensor::GetCooIndexDimsvalues_count == index_sizeonnxruntime::SparseTensor::MakeBlockSparseDataUse MakeBlockSparseStrings to device type: onnxruntime::SparseTensor::MakeBlockSparseStringsExpecting fully sparse tensors to have value shape {0}Expecting index blocks: onnxruntime::SparseTensor::UseBlockSparseIndicesExpecting fully sparse tensors to have indices shape {0}X-device copy of strings not supportedDestination must have a CPU allocator setMust have the same shapeSrc and Dst must be of the same typeonnxruntime::SparseTensor::CopyUnable to find a data transfer for copying from device type: Destination should be emptyThis instance should not be emptyallocator_ != nullptrThis method should follow a call to constructor that supplies the allocatorFormat() == SparseFormat::kCsrcMust contain Csr format. Contains: Expecting data type to be set as stringonnxruntime::SparseTensor::MakeCooDataFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooStringsInner and Outer indices must either be both zero or non-zeroonnxruntime::SparseTensor::ValidateCsrIndicesExpecting inner index size: the same as values size: Expecting two indices. Got: onnxruntime::SparseTensor::AsCsrdense shape must 2-D. Got: format_data_.size() == 2Uonnxruntime::SparseTensor::MakeCsrDataUse MakeCsrStringsMust contain BlockSparse format. Got: onnxruntime::SparseTensor::MakeCsrStringsOuter index count must be rows + 1 or zero. Got: rows: onnxruntime::SparseTensor::UseCsrIndicesThis method does not expect allocator to be setExpecting indices to have 2-D shape . Got: onnxruntime::SparseTensor::ValidateBlockSparseShapes to be equal to values blocks: Indices shape must have dim[0] == 2onnxruntime::SparseTensor::AsBlockSparseFormat() == SparseFormat::kBlockSparseExpecting to have at lest 3-D shape. Got:Expecting one index. Got: Invalid dimension of for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeToDimensiondimension <= num_dims dimensions.{onnxruntime::TensorShape::Slice}onnxruntime::TensorShape::SizeFromDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccdimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.onnxruntime::ConfigOptions::AddConfigEntryConfig with key [D:\a\_work\1\s\onnxruntime\core\framework\config_options.ccConfig value is longer than maximum length 1024Config key is empty or longer than maximum length 128] already exists with value []. It will be overwrittenReceived nullptr for OrtValue.Received nullptr for name.Buffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.An OrtValue for this name has already been added.Attibute name and type don't match is defined. but is of type: No attribute with name: No attribute with this name is defined.Attribute: expected to be of type: Requested attribute: is expected to have type: onnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.hTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsonnxruntime::OpKernelContext::GetOrCreateOutputMLValueCPU allocator not foundframe != nullptrExecution frame was null. Num args is onnxruntime::OpKernelContext::OutputMLValuearg_num < arg_counts.size()Invalid arg_num of D:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextkernel != nullptrOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocatoronnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0D:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cc constraint was not found for D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::`anonymous-namespace'::TypeBindingResolver::ResolveOp with name ( and type ( However the types are incompatible. This op has been implemented only for the following types ( kernel start version: kernel_end_version: Version mismatch. node_version: onnxruntime::KernelRegistry::TryCreateKernel but the node in the model has the following type ( kernel is not supported in Encountered following errors: ( in the supported version range (node_version: ),Found kernel for Op with name (onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()onnxruntime::KernelRegistry::Register: Conflict with existing kernel def hash.kernel def can't be NULLKernel not foundFailed to add kernel for : Conflicting with a registered kernel with op versions.Candidate for fallback CPU execution: onnxruntime::GetCpuPreferredNodes::::operator () because the CPU execution path is deemed faster than overhead involved with execution on other EPs ORT optimization- Force fallback to CPU execution for node: onnxruntime::GetCpuPreferredNodes capable of executing this nodeD:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cckernel_info != nullptr type: can not be writen into Tensor type TensorProto type onnxruntime::utils::GetFileContentTensorProtoToTensor() tensor shape mismatch!UnpackTensor: the pre-allocate size does not match the size in protodata overflow) does not match the data size(corrupted protobuf data: tensor shape size(, Got The preallocated buffer is too small. Requires TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated buffertensor can't contain negative dimsInitialized tensor with unexpected type: onnxruntime::utils::TensorProtoToTensorstring tensor can not have raw dataSparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedSparse Indices raw data size does not match expected.onnxruntime::utils::CopySparseData in 'Constant' node 'Unsupported attribute value type of onnxruntime::utils::TensorProtoToMLValueonnxruntime::utils::ConstantNodeProtoToTensorProtoonnxruntime::utils::SparseTensorProtoToDenseTensorProto is not supported.cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Invalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorTensor does not have external data to read from.`anonymous-namespace'::GetExternalDataInfoUnpackTensor: the pre-allocated size does not match the raw data size, expected ) in protonullptr == p_dataonnxruntime::utils::UnpackTensorWithExternalDataImplTensorProto external data size mismatch. Computed size: `anonymous-namespace'::ReadExternalDataForTensorD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccExternal data type cannot be UNDEFINED or STRING.Invalid TensorProtoonnxruntime::utils::UnpackInitializerDataUnsupported type: onnxruntime::utils::DenseTensorToSparseTensorProto data_type: Must have a valid data typeHasDataType(dense_proto)Element_size of: Unsupported sparse tensor data type of onnxruntime::AllocatorManager::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.ccduplicated allocatorReceived invalid value of arena_extend_strategy onnxruntime::CreateAllocatoronnxruntime::IExecutionProvider::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccmetadef_id_generator_onnxruntime::IExecutionProvider::GenerateMetaDefIdIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatorIExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by duplicated allocator: onnxruntime::IExecutionProvider::TryInsertAllocatorInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUUnsupported element size: Support 2-D matrices onlyonnxruntime::sparse_utils::DenseTensorToSparseCsrD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPUExpecting indices to be equal the number of values or be twice as manyonnxruntime::sparse_utils::SparseCooToDenseTensorouter_num == (rows + 1)Input must be of COO formatonnxruntime::sparse_utils::SparseCsrToDenseTensorOuter indices must be M + 1. Got: Expecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::DenseTensorToSparseCoo > dense_size: Invalid index: D:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsThere's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsTensor size mismatch to data_transfer registered is nullptr.InternalTestingExecutionProviderUnsupported OrtValue type to copy between device.onnxruntime::utils::FindMemoryInfoForValueonnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccFailed to find allocator for device allocator != nullptrinvalid allocator.Unsupported OrtValue type.fetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsonnxruntime::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::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicessession.disable_prepackingCould not find OrtValue with idx 'existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionState for attribute Entry exists in node onnxruntime::SessionState::AddOutputNameToNodeInfoMappingOnly one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()Main Graph instance should have populated all subgraphs when being resolved.subgraphonnxruntime::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::SaveToOrtFormatonnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::UpdateToBeExecutedNodesSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::FinalizeSessionStateonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()Unable to find kernel hash for node:Unable to find compiled kernel hash for node 'onnxruntime::SessionState::LoadFromOrtFormat optype:onnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::LoadFromOrtFormat::::operator () Index:' OpType:onnxruntime::SessionState::FinalizeSessionStateImpl Attribute:subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulatorDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfoonnxruntime::SessionState::SetupAllocatorsD:\a\_work\1\s\onnxruntime\core\framework\session_state.cc. Ignoring allocator from Allocator already registered for onnxruntime::KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsduplicated ort_value index:onnxruntime::SessionState::CreateKernels. Do you have duplicated calls to SessionState::AddInitializedTensor function?entry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfoThe op type of a node cannot be empty!op_type.empty() doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0allocator_for_caching.get() != nullptronnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()The kernel corresponding to the node +Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: ) and node is used by node Using cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container which is of op type: used in the node: p_op_kernelMissing session state for subgraph. Node:'entry != node_to_subgraph_ss.second.cend()Subgraph SessionState entry for is missing. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.Kernel create info is null. Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Subgraph SessionState for is null. Invalid ORT format model.). found duplicated provider in KernelRegistryManagerFailed to find kernel for The node is not placed on any Execution Provider. (node Identity_16Identity_14Identity_13Identity_1Gather_13Gather_11UnSqueeze_13UnSqueeze_11UnSqueeze_1Squeeze_13Squeeze_11Squeeze_1Transpose_13Transpose_1D:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypenullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: Error mapping feeds: onnxruntime::FunctionKernel::CreateGraph contains an invalid node: Op Type: with domain: . These are temporary nodes added during layout transformations and are not expected to remain in the graph post partitioning. This is a bug in layout transformer.API VERSION Create state function failed. Return value:D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.h did not return correct number of compiled functionsfusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll.onnxruntime::PartitionOnnxFormatModelImplonnxruntime::PlaceNode1 == capability.nodes.size()D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::GetCapabilityForEPNo provider specified.onnxruntime::GraphPartitioner::PartitionOrtFormatModelExisting entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model.single_node_compute_func should have 1 element.onnxruntime::PartitionOrtFormatModelImplonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::InlineNodescompiled_kernel_hashes != nullptrCompiled kernel hashes must be providedonnxruntime::GraphPartitioner::PartitionD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionentry != regions_.end()Could not find Region for: p_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_intD:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: memory limit: arena_extend_strategy: onnxruntime::BFCArena::FindChunkPtr!chunk->in_use()Failed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for . bin_num: (requested) num_bytes: !c1->in_use() && !c2->in_use()onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::Shrink BFC Arena shrunk by bytes. The total allocated bytes is now onnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Mergeonnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)Creating bins of max chunk size bytes.Failed to allocate memory for requested buffer of size Available memory of is smaller than requested bytes of onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.hipMalloccudaMallocReserving memory in BFCArena for size: Allocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendExtended allocation by (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytesh != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserve. D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::LoadExternalLibA dso with name has already been loaded.Caught exception while loading custom ops with message: Failed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoaderUnloading DSO onnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:D:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be used failed. Error:Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()invalid index shape && sp_tensor.DenseShape() == *shapeTensor shape cannot contain any negative valueTrying to allocate memory for unused optional inputs/outputs returned nullptronnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::{ctor}::::operator ()Allocation of memory pattern buffer for Shape 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: , block in memory pattern size is: but the actually size is: , fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperort_value.Fence() == nullptrort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueAllocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::AllocateSparseTensormlvalue.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferonnxruntime::ExecutionFrame::GetAllocationPlanort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::VerifyOutputSizesExpected shape from model of does not match actual shape of for output Invalid allocation kind: Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=onnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx= size= failed: model format error! Missing 'location'model format error!checksumlengthparsing failedlocationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infoonnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()Invalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::CreateD:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated(). Shape:Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is D:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccsource and destination buffer size mismatchonnxruntime::utils::detail::CopyLittleEndian does not.Begin executiononnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.Got nullptr from GetKernel for node: op_name_fence_before' Status Message: node. Name:'All implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.onnxruntime::ParallelExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::ExecuteNon-zero status code returned while running providerthread_scheduling_stats_kernel_time_fence_afterUnknown exception was caught by catch-all handler.'. Exception running nodes starting at activation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapeSequentialExecutor::Executeonnxruntime::ReleaseNodeMLValues},":[{"onnxruntime::SequentialExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.ccgraph_indexexec_plan_indexonnxruntime::AllocPlanPerValue::ProgramCounter::AddEndInvalid 'end'. Value is larger than 'start'.end >= starts_.back() : ) Previous entry was not terminated.starts_.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'.starts_.empty() || start > ends_.back()No matching 'start' entry.starts_.size() == ends_.size() + 1entry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::VerifyMemoryTimeScheduleAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::GetLocationForNodeInputspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::ComputeReusePlanOnly tensors are supported for external outputs for now.!IsNonTensor(*node_output)Invalid program_counter entries at index SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoonnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccfound_in_outer_scope_location_maponnxruntime::PlannerImpl::ComputeUseCounts::::operator ()Can not find the node Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defonnxruntime::PlannerImpl::ComputeUseCountsCan not find the execution provider allocatoronnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reusenullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeThere is no location for this node arg in the outer scope location mapbuffers_.size() == buffer_sizes_.size()onnxruntime::PrePackedWeights::GetHashD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.ccsession.use_device_allocator_for_initializersFailed memory size calculationDeserializeTensorProto() takes either pre-allocated buffer or an allocator!onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsOrtValue 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 [Memory] SessionStateInitializer statically allocates Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::DeserializeTensorProtoD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.ccstring 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 Cannot use user supplied initializer with name: ( input with name SubgraphGraphUsing user supplied initializer with name ( failed.Deserialize tensor Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node.onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorUnsupported device allocator in the context of pre-packed weights caching: D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncsfunc info for node: already exist. not found.Can't use func with null ptrcurrent <= buffer_size_D:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.honnxruntime::MemPatternPlanner::TraceAllocation!using_counters_[TypeInferenceError] ConstantNoOpD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hTypeProto must have shape for this to runonnxruntime::utils::GetShapeshape != nullptrD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.honnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet are '0' and '1'. The environment variable contained the value: ALLOW_RELEASED_ONNX_OPSET_ONLYOutput: , or optional typed entitiesSource and target must both be tensors. Falling back to lenient merge. , or sparse tensors . Got: onnxruntime::NodeArg::UpdateTypeAndShape] op_type [Optional Type mismatch. Expected: Serialization of fused function body is not currently supported, Node [onnxruntime::Node::SaveToOrtFormatdoes not have the graph for key ' source: target:onnxruntime::MergeShapeInfoError merging shape info for output. 'Tensor element type mismatch. D:\a\_work\1\s\onnxruntime\core\graph\graph.ccType mismatch. Current= Input=) does not exist in the graph.node_argThis is an invalid model. Error: two nodes with same node name (This is an invalid model. Graph output (onnxruntime::Graph::SetOuterScopeNodeArgsThis is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.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, Initializer 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.Graph ctor should have created NodeArg for initializer. Missing:onnxruntime::Graph::InitializeStateFromModelFileGraphProto 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=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.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.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.Node::LoadEdgesFromOrtFormat, edge is missing for Node::LoadFromOrtFormat, input_arg_counts is missing is not the same as this node's index:onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()onnxruntime::Node::LoadEdgesFromOrtFormatinput index: output edgesinput edgesonnxruntime::Node::LoadFromOrtFormat::::operator ()fbs_node_arg_names cannot be null], could not find NodeArg node_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormatLoadNodeArgsFromOrtFormat: Node [Serialization error. Graph attribute was serialized without Graph instancefbs_attr cannot be nullDuplicate sparse_tensor_initializer: 'utils::HasName(sparse_tensor)' 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): 'by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto != nullptrgraph_proto cannot be null' Model is invalid.onnxruntime::Graph::GraphSparse initializer must have a name. This model is invalidDuplicate constant node sparse initializer name: 'Type Error: Shape of initializer does not match. This is an invalid model. In Node, , Error is not a registered function/oponnxruntime::Graph::VerifyNodeAndOpMatch) is required but not specified.Fatal error: ) of output arg () of node () does not have type information.Type Error: Type ( but usage of initializer in graph expects This is an invalid model. Model input (Type Error: Data in initializer '' has element type Error: Duplicate definition-site for (onnxruntime::Graph::InitFunctionBodyForNodeonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::Resolveonnxruntime::Graph::ForThisAndAllSubgraphsouter_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.) attribute (. Error message . Execution will fail if ORT does not have a specialized kernel for this opFunction body initialization failed for node '' optype Subgraph input missing type.Node:UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesonnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()Node () Op (Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.No Graph instance was found for attribute in node inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Size mismatch validating subgraph inputs. Got inputs but subgraph has ) bound to different types ( in node (Type Error: Type parameter () of Optype () type inference failedonnxruntime::Graph::InferAndVerifyTypeMatch) does not match expected type () output arg () input arg () does not have type information set by parent node.) is invalid.This is an invalid model. Node () of operator () in node (This is an invalid model. Type Error: Type '' of input parameter (nodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.nullptr != func_meta_defonnxruntime::Graph::AllocateNodeonnxruntime::Graph::FinalizeFuseSubGraphonnxruntime::Graph::CreateFusedSubGraphNodedst_implicit_input_idx < (int)node->ImplicitInputDefs().size()initializer_node_arg != nullptrCannot find NodeArgs for ['. It is not used by any node and should be removed from the model.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is no longer used by any node.Removing initializer 'Removing NodeArg 'outer_scope_node_arg != nullptr) -> (""NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormatonnxruntime::Graph::InlineFunctioninput->Exists()Input to set must exist."onnxruntime::Graph::SetInputs(") : (!foundAddInitializedTensor already has tensor with name but different TensorProto.onnxruntime::Graph::AddInitializedTensorexisting->second == &tensorsparse_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::RemoveInitializedTensorFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProto'was added but does not exist. onnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name '_token_onnxruntime::Graph::SaveToOrtFormatCan't remove node as it still has output edges.onnxruntime::Graph::RemoveNodenode->GetOutputEdgesCount() == 0Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Sparse 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.NodeEdge is missing. Invalid ORT format model.Node index is out of rangeonnxruntime::ConstPointerContainer > >::atindex < data_.size()D:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.h/iDomain already set in registryD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalthan the operator set version , but it its domain is not, but it its version is higherNot supported with filtered graph.filter_info_ == nullptrInvalid ExecutionOrderonnxruntime::GraphViewer::GetNodesInTopologicalOrderonnxruntime::GraphViewer::GetRootNodesD:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Input not found:graph_->GetNode(idx) != nullptronnxruntime::GraphViewer::GraphViewerMismatch between Graph and IndexedSubGraph. Node not found: nodenodeargMismatch between Graph and IndexedSubGraph. Output not found:IndexedSubGraph contains values not present in the GraphD:\a\_work\1\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.onnxruntime::Model::ModelUnsupported model IR version: model may run depending upon legacy support of some older opset version operators.Missing model IR version., max supported IR version: 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 ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is till opset onnxruntime::model_load_utils::ValidateOpsetForDomain 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 less than 0. is less than 0.Protobuf parsing failed.onnxruntime::Model::SaveProtobuf serialization failed.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 onnxruntime::Model::LoadFailed to load model with error: No graph was found in the protobuf.onnxruntime::SaveModelonnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatNull entry in metadata_props. Invalid ORT format model.Load model system error number failed. File doesn't exist should specify a shapeNegative values are not allowed in a shape specification expected to have tensor or sparse type expected to have tensor typeAttribute Output dataA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Threshold valuehidden_prevBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesinput_as_shapeextra_shapeConstrain input and output types to all tensor types.Constrain indices to integer typesvaluesGivenTensorFillD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.The scale to apply.Bias applied to each channel, same size as C.Value of betaValue of alphaIf 0, normalize the mean only. Default is 1.Scaling valueIf 1, mean and variance are computed across channels. Default is 0.'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-Dseq_lengthsgateshiddenGRUUnit) + 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 () + scale[0] () + scale[1] (CAttribute expected to have tensor or sparse tensor type but has rank expected to have rank Dimension mismatch in unification between expected to have rank > expected to have tensor or sparse tensor type: is nullIncompatible dimensionsError parsing node:apply softmax to elements for dimensions softmax_axis or higherConstrain input and output types to all numeric tensors and bool tensors.(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-1Constrain input and output types to all numeric tensors.scale_grad_by_freqBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.TorchEmbeddingisinf_onlyisnan_onlyConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.If true, check only for Inf, -Inf.If true, check only for NaN.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.IsAllFiniteresidualmaskratioFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) '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()Inputs 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 multiplicationpadding_idxweightAD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cc != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Input rank must be >= 2.sequencesprefix_vocab_maskscoressequences_scoreslength_penaltytemperaturevocab_maskrepetition_penaltyMConstrain input and output types to float or half tensors.normalizedonesidedRfftIrfftConstrain 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 typessignal_ndimConstrain input0 and output types to float tensorsConstrain input types to all tensor types.GridThe id of the end-of-sequence tokenThe id of the padding tokenConstrain output types to float tensors.Constrain the output to a boolean tensor.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.Support 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.min_lengthmax_lengthnum_return_sequencesnum_beamsmodel type: 0 for GPT-2; 1 for encoder decoder like T5subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.no repeat ngrams sizeearly stop or notencoder_decoder_initDecoder subgraph to execute in a loop.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationinputs are expected to have tensor type and output type should not be null.Constrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input A data types as 16-bit integer tensorConstrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.If value is 1, output type is uint32_t, else int32_t. Default value is 1.both data and indices tensor need to have rank larger than zero.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.sparse_tensor(float)sparse_tensor(double)sparse_tensor(uint64)sparse_tensor(uint32)sparse_tensor(int64)sparse_tensor(int32)Second input does not have rank 2Constrain input and output types to float/int tensors.Whether A should be transposedWPadsComplexMulComplexMulConjZFirst input does not have rank 2Constrain input and output types to float tensorsInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).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.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredThe 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.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.Scalar multiplier for input tensor C.activation_gammaWhether B should be transposedScalar multiplier for the product of input tensors A * B.Input axis is invalid: crop_size shape input tensor has wrong dimensionThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.rois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionroisValue used for extrapolation, when applicable. Default is 0.0f. 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".Input can be of any tensor type.Constrains input to only numeric types.first input tensor has wrong dimensionXShape = Shape (X)Rank = Size (XShape)stash_typeEpsilonPrefixShape = Slice (XShape, Zero1D, Axis1D)NumReducedAxes = Sub (Rank, Axis1D)Zero1D = Constant()Axis1D = Constant()crop_sizebatch_indicesConstrain types to int tensors.Constrain types to float tensors.SequenceThis 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.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Constrain to tensor(int32).last dimension of indices must not be larger and rank of data tensorInteger representing the embedding vector size for each word.If not provide, use the fileter size of conv weightConstrain input and output types to any tensor type.Constrain indice type to int32 or int64xcountsidx'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Three 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 arrayConstrain to tensor(float).Type of Mean and InvStdDev tensors.InvStdDevConstrain input types and output Y type to float tensors.Constrain mean and inv_std_var to be float tensors.EfficientNMS_TRTinv_std_varConstrain input and output types (except mean and inv_std_var) to float tensors.The epsilon value to use to avoid division by zero.type used for stash mean/inv_std_vardetection_scoresdetection_boxesbackground_classEncoding type for the boxes or anchors inputs.detection_classesBackground class ID.boxesnum_detectionsanchorsVarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)NormalizedT = Cast (Normalized)Scale2D = Flatten (Scale)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)NumReducedAxes = Neg (Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)XU = Cast (X2D)Mean2D = ReduceMean (XU)Attribute 'max_output_boxes' must be >= 1.max_output_boxesThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).pooled_sizeAttribute 'pooled_size' must be >= 1.Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Scaled = Mul (NormalizedT, Scale2D)B2D = Flatten (B)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)MultilevelCropAndResize_TRTScore threshold value.score_thresholdfeature_map_1feature_map_0iou_thresholdMax detections to output.box_codingBox IOU threshold value.Activation function to apply to the scores input.score_activationVersion number of the TRT plugin.plugin_versionPyramidROIAlign_TRTfeature_map_3feature_map_2image_sizePooled size.patchesImage size.extra_addpastkey and value cache shall be 4 dimensionsWhether every token can only attend to previous tokens. Default value is 0.Hidden layer sizes of Q, K, V paths in AttentionNumber of attention headsnew_value_cacheConstrain input and output types to float and float16 tensors.new_key_cacheConstrain key_padding_mask to bool tensors.DecoderAttentionuse_paststatic_kvhas_key_padding_maskhas_layer_stateConstrain input and output integer tensors typesConstrain input and output float tensors types.embedding_sumword_embeddingsegment_idssegment_embeddingposition_embeddingglobal_weightglobalglobal_biasGpresentConstrain mask index to integer typesOne sided attention windows length W, or half of total window lengthwindowD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.cckey_padding_maskkv_weightvalue_cachekey_cachequeryLongformerAttentionq_weightkeypred_tokensprev_suffix_match_idxsuffix_match_idxConstrain to integer types.tokensskipbaX_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)The maximum NGram size for suffix matching.The minimum NGram size for suffix matching.cur_tokenssrc_tokensThe NGram size.Constrain mean and inv_std_var to float tensors.scores_outConstrain scores input and output types to float tensors.̼+̼+̼+Scale and Zero-point must be a scalarInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}inputs are expected to have tensor type.Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=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).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.reducedConstrain input type to 8-bit integer tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.ReduceSumIntegerConstrain 'x' and 'x_zero_point' to 8-bit integer tensors.x_zero_pointConstrain 'y', 'x_scale' to float tensors.a_scaleConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.a_zero_pointMulIntegerConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.b_zero_pointb_scaleB_zero_pointB_scaleC_zero_pointC_scaleA_scale{additionalDocumentation}A_zero_pointConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'x', 'y_scale' to float tensors.x_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.y_zero_pointy_scaleinitial_cinitial_hPRsequence_lensAll inputs to Concat must have same rankWhich axis to concat onRequired attribute axis is missingaxis must be in [-rank, rank)T8Y_hConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Y_creduced_scalereduced_zero_pointdata_zero_pointCoefficient of leakage.QLinearReduceMeanC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditiondata_scaleaxis must be in [-rank, rank-1]. input rank was Number of neurons in the hidden layerOptional 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.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.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.Couple the input and forget gates if 1.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.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.X_zero_pointX_scaleY_zero_pointConstrain input and output types to 8 bit tensors.Y_scalegamma_scalesegment_embedding_scaleword_embedding_zero_pointbeta_scalebeta_quantposition_embedding_quantgamma_quantposition_embedding_scaleword_embedding_scalemask_index_outlayernorm_outConstrain input and output types to float32 tensors.segment_embedding_zero_pointposition_embedding_zero_pointbeta_zero_pointgamma_zero_pointConstrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.inputsTVConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).input_zero_pointword_embedding_quantweight_zero_pointConstrain input and output types to int8 tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.weight_scaleinput_scaleConstrain input and output types to singed/unsigned int8 tensors.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.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.Output tensor must have at least 2 dimensionsD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccInput tensor must have at least 2 dimensionsw_zero_pointww_scaleThe shape of the convolution kernel. If not present, should be inferred from input W.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.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.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.onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used output cannot be safely updated to Implicit input name Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputitr != node_args.end()onnxruntime::graph_utils::GetIndexFromNamefor node: Attempting to get index by a name which does not exist:onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph in one of the subgraphs.D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.ccstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 ExplicitInputs:. Index:onnxruntime::graph_utils::RemoveNode ImplicitInputs:Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputindex >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameonnxruntime::graph_utils::AddInitializerAttempting to get an input that does not exist.onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Attempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()Failed since multiple edges matched:onnxruntime::graph_utils::FindPathonnxruntime::graph_utils::AddNodeInputAttempting 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::SaveToOrtFormatTENSORSTRINGSTENSORSFLOATSINTSSPARSE_TENSORSGRAPHSSPARSE_TENSORD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveInitializerOrtFormatUNDEFINEDSTRINGGRAPHFLOATINTInvalid ORT format model.Missing indicies for sparse initializer: onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull tensor attribute. Invalid ORT format model.Missing dims for sparse initializer: Null string attribute. 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.Missing raw data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing string data for initializer. 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 modelNull tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.iԽiܽiĽii̽iiȼiiiiiмi is out of bounds.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. optype does not contain a graph.Not supportedonnxruntime::ViewerFunctionImpl::BodyD:\a\_work\1\s\onnxruntime\core/graph/function_impl.honnxruntime::ViewerFunctionImpl::MutableBodyinTinonnxruntime::IOTypeConstraintHelperD:\a\_work\1\s\onnxruntime\core\graph\function.cc'. Error message outToutCannot infer type and shape for function referenced by function body node . No opset import for domainNo opset registered for domain domain_version != -1 in function opset imports._dummyResolve subgraph failed:initializer != nullptronnxruntime::FunctionImpl::FunctionImpl's number of outputs is different from function body graph's number of outputs.node_in_parent_graph->OutputDefs().size() == function_body_graph.GetOutputs().size()'s number of inputs is different from function body graph's number of input.node_in_parent_graph->InputDefs().size() == function_body_graph.GetInputsIncludingInitializers().size()A node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::UpdateSubgraphsWithinFunctionBodyFunction body initialization failed for Function 'onnxruntime::InitNestedModelLocalFunctionfused_function_subgraph is not found or is not constant initializer.input_arg->Type() != nullptronnxruntime::CreateSchemaUnsupported type:Can not get shape initializer data!limitConstrain input and output types.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 smallmemory_seq_lensAWMWQWConstrain seq_lens to integral tensors.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.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 dimensionsInputs 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.Inputs 4 shall be 5 dimensionsqkv_hidden_sizes should have 3 elementsInvalid bias shapeReceived null OrtThreadingOptionsD:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setReceived invalid value for allow_spinning. Valid values are 0 or 1D:\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?@@@), BIsSigned(Quant GEMM format: AIsSigned() is not supported on this deviceElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!??    code != static_cast(common::OK)onnxruntime::common::Status::StatusNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDSUCCESSFAILNO_SUCHFILENO_MODELD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]systemgenericFatal error: 0 count processors from GetLogicalProcessorInformation - fail, errcode = open file SetThreadDescriptiononnxruntimeFatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCorescustom_create_thread_fn returned invalid handle.onnxruntime::`anonymous-namespace'::WindowsThread::WindowsThreadD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cckernel32.dll/\MapFileIntoMemory is not implemented on Windows.\/, error code: DeleteFile() failed - path: length > buffer.size()SetFilePointerEx ReadFile fail: unexpected endfile_path == nullptronnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferoffset < 0GetFileSizeEx Invalid fd was supplied: Received negative size from stat callExceptionReturnHrLogHrFailFast%ws[%hs] kernelbase.dllRaiseFailFastException Msg:[%ws] CallContext:[%hs] [%hs(%hs)] %hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %wsD:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameLoadLibrary failed with error FreeLibrary failed with error in library, error code: Failed to find symbol UNC\"" when trying to load " "onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPath\*onnxruntime::LoopDirunknown erroronnxruntime::logging::LoggingManager::CreateDefaultLoggerdefault_logger_id must be provided if instance_type is InstanceType::DefaultD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccDefault logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.onnxruntimeonnxruntime::logging::LoggingManager::LoggingManagerISink must be provided.onnxruntime::Path::Parseonnxruntime::`anonymous-namespace'::ParsePathRootFailed to parse path root: D:\a\_work\1\s\onnxruntime\core\common\path.cc/\onnxruntime::concurrency::ThreadPoolProfiler::Stopenabled_}}D:\a\_work\1\s\onnxruntime\core\common\threadpool.ccProfiler not started yetunnamed_thread_poolonnxruntime::concurrency::ThreadPoolTempl::RunInParalleln <= num_threads_+1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.h"thread_id": "", "block_size": [": ], "core": onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetpoints_.empty()!points_.empty()LogStart must pair with LogEnd", }, "sub_threads": {{"main_thread": {"thread_pool_name": "n >= 0More work items than threadsonnxruntime::concurrency::ThreadPool::ParallelForNested parallelism not supported": {onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection!current_parallel_sectionUnknownEventWaitRevoke"num_run": "core": DistributionEnqueueDistributionWaitRunonnxruntime::ToUTF8Stringlength overflowonnxruntime::ToWideStringD:\a\_work\1\s\onnxruntime\core\common\helper.ccD:\a\_work\1\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::Startonnxruntime::profiling::Profiler::Initializesession_logger != nullptrNodeSessionKernel"ts" :"dur" :"name" :""ph" : "X",{"cat" : "","tid" :"pid" :Writing profiler data to file onnxruntime::profiling::Profiler::EndTimeAndRecordEvent[ onnxruntime::profiling::Profiler::EndProfilingMaximum number of events reached, could not record profile event.] }, " : "args" : {} " : "iiiillegal input path:unexpected failureadvapi32.dllapi-ms-win-eventing-provider-l1-1-0.dllEventSetInformation is not a valid day is not a valid year is not a valid datepmam:, [] GetSystemTimePreciseAsFileTimekernel32.dllmap_typesequence_typetensor_typeSaveValueInfoOrtFormat: value_info_proto for is missing type info.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.] for nowonnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveValueInfoOrtFormatWe do not support type [D:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormat. Invalid ORT format model.Type:onnxruntime::fbs::utils::LoadValueInfoOrtFormatNull type info for Null sequence type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormat is not supported currentlyNull map type info. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.opset id is null. Invalid ORT format model.Pgiiihas output size not in allowed output sizes.) than declared () in op definition.) has input size not in range [min=) has output size not in allowed input sizes.' has been deprecated since version has unsupported type , max=Operator ', has unsupported type: has inconsistent type typestr: ' is expected to have field 'type_proto'' is expected to have field 'g'' is expected to have field 'ints'' is expected to have field 'floats'Mismatched attribute type in 'Unrecognized attribute: ' is expected to have field 'sparse_tensor'' is expected to have field 't'' appeared multiple times.)'s output for operator Attribute ' is marked single but has an empty string in the graph) has more inputs () has more outputs ()'s input ONNX Schema : failed validating the check: !(it.GetName().empty())(outputs_.size() - 1) == iError parsing function body:Duplicate type constraint name(inputs_.size() - 1) == iExtra unparsed input unexpected.' is missing. has unknown expected typeRequired attribute '' 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'complex64DataTypeUtils::FromDataTypeString - Received invalid data type string complex128optionalInvalid tensor data type opaquemapmap(optional(Unsuported type proto value case.sparse_tensor(Invalid data type tensor(Values 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 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.ccThe 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.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'.map(int64, float)map(int64, string)A 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.The input must be an integer map to either string or float.A float.Input's shape should be 1D or 2DA list of strings. One and only one of 'value_*'s should be set.A list of floats.An integer.A string.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.A list of ints.A list of strings. One and only one of 'keys_*'s should be set.Only one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setThe 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'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.A collection of intercepts.A collection of weights of the model(s).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).The 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]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.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)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.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.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 setValue(s) to change to.A value that needs replacing.Label encoder has only one input.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.The input type must be a tensor of a numeric type.The size of each input in the input listOnly one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.base_values_as_tensorOnly 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.nodes_values_as_tensorclass_weights_as_tensornodes_hitrates_as_tensorSupport 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.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.'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.node id that this weight is for.The id of the tree that this node is in.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'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.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.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.One of 'MAX,' 'L1,' 'L2'The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].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.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.First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.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].Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The node id of each weightThe id of the tree that each node is in.The weight for each targetThe index of the target that each weight is forChild node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.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.Child node if expression is falsetarget_weights_as_tensorOnly one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The output will be a sequence of string or integer maps to float.seq(map(int64, float))seq(map(string, float))D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.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.) vs (Wrong op_type name for running propagation: Constrain input and output types to high-precision numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentD:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.ccInvalid rank for broadcasting: ('axis' must be in []. Its actual value is: Constrain input and output types to numeric tensors.sum { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } data_0List of tensors for Value of beta.Value of alpha.Invalid value for attribute axis 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) 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)Coefficient of ELU. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) Coefficient 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).Constrain input and output types to signed numeric tensors.slopeConstrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. 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. The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.input and zero_point pair is expected to have same type.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.IndicesDimension 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 int64KValuesK input must be of type int64.K input must be a one-dimensional tensor of size 1.Axis has less than the requested k elements.Constrain input and output types to all tensors.'shape' input must be 1D tensorWeight rank must be 1.target loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) weight_gather = Squeeze (weight_gather_temp_1, axes)Input and target dimension value mismatch.Target rank must be 1 less than the input rank.weight_gather_temp = Gather (weight, transform_targets)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)const_one_floatsqueeze_mask = Squeeze (mask, axes)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)const_one_casted = Cast (const_one_float)Einsum expression string.InputsEllipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.Rank of input does not match the equation indices.Constrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.NegativeLogLikelihoodLossConstrain target to integer typesType 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.loss input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) ignore_indexloss_Ndd = Squeeze (loss_N1dd, axes)loss = Squeeze (loss_N1dd, axes)const_zeroexpanded_target = Unsqueeze (target, axes)const_oneaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.If set to 1 will perform the sums in reverse direction.input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)loss_NCdd = Neg (input_gather_element_transform)input_gather_element = GatherElements (input, transform_targets) 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_zero_casted = Cast (const_zero_float)const_zero_floatloss_Ndd = Mul (loss_unweighted, weight_gather)loss = Mul (loss_unweighted, weight_gather)const_ignore_index loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss = ReduceSum (loss_Ndd)loss = ReduceMean (loss_Ndd)loss_unweighted = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)SoftmaxCrossEntropyLosslog_problabelslog_prob = Identity (X_Log) 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) output = NegativeLogLikelihoodLoss (X_Log, labels)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.Shape3DConstrains input/output to boolean tensors.Constrains input types to all numeric tensors.Constrains output to boolean tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input to boolean tensor. { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.optional(tensor(complex128))optional(seq(tensor(bfloat16)))optional(tensor(bool))optional(tensor(complex64))optional(tensor(double))optional(tensor(string))optional(tensor(float16))optional(tensor(float))optional(tensor(int32))optional(tensor(int64))optional(tensor(int8))optional(tensor(int16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(uint8))optional(tensor(uint16))Scan input outputs. Expected Number of scan output axes specified ( was not a tensor.Number of scan input axes specified () is not equal to number of scan outputs ( axis value ) is not equal to number of scan inputs ( is invalid for a tensor of rank optional(tensor(bfloat16))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(int64)))optional(seq(tensor(float16)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint8)))Int64 tensorAll Tensor typesAn 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 optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].An optional list of M flags. The i-th element of the list specifies the 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, 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.The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. final_state_and_scan_outputsGraph 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.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.outputscondLoop '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 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. Graph attribute inferencing returned type information for was notinitial_state_and_scan_inputstensor of bool, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.v_final_and_scan_outputsv_initialD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) typesOnly boolD:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoaxis must be in [-rank, rank-1]. should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute expected to have tensor or sparse tensor type and Output does not match type of output: Input: type: Incorrect or missing input value for starts and endsInput axes has incorrect lengthOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.'step' cannot be 0 for SliceWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Mismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (Invalid value of attribute 'axis'. Rank=) and outputs (Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedAttribute perm for Transpose has repeated value: A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {}, input shape = {stepsInput rank for starts and ends should be the same: (Input steps has incorrect lengthInput axes has invalid datareshaped(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.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesInvalid position of 0.Invalid dimension value: Target shape may not have multiple -1 dimensions.target_typeoutput = Cast (input) Value=concat_resultConstrain output types to any tensor type.All inputs to Concat must have same rank. Input Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Constrain output to int64 tensor, which should be a scalar though. has rank Input tensor can be of arbitrary type.Constrain output to int64 tensor.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(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.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.repeats'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 int64DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Blocks of [blocksize, blocksize] are moved.sizesroiWhen 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.If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". 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.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.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]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.Input and output types can be of any tensor type.updatesBlocksize must be positiveInput tensor must be 4-dimensionalexpandedList 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).'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapesqueezedList 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).'sequence_lens' must have rank of 1(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'input' must have rank >= 2Constrain to all tensor types.Constrain to boolean tensors.Constrain input and output types to all tensor types (including bfloat). 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. **History** - Version 16 adds bfloat16 to the types allowed (for the second and third parameter). (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.(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.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorinverse_indices(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Constrains to boolean tensors.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).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.Support 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.gridConstrain roi type to float or double.Constrain output types to boolean tensors.Constrain input types to float tensors.Constrain to any tensor type.depthInput '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.OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.constant_valueSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.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).default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.selected_indicesmax_output_boxes_per_classD:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccConstrains input type to all tensor and sequence types.Constrains output type to all optional tensor or optional sequence types.Type of the element in the optional outputOptional is expected to have either an input or the type attribute set.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have an output.Constrain output type to all tensor or sequence types.Input type is null. Input must have Type information.Input must be an optional-type value containing an element with type information.OptionalGetElement must have an input element.Constrains input type to optional tensor and optional sequence types.Constrains output to a boolean tensor.OptionalHasElement is expected to have 1 input.OptionalHasElement is expected to have 1 output.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\optional\defs.ccvalue_floatsvalue_stringvalue_intsvalue_floatsparse_valuevalue_intThe values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the elements of the output tensor.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.value_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute expected to have a one-dim sparse tensor expected to have: Attribute expected to have a one-dim tensor or UNDEFINED. Got: All inputs to 'Range' op must be of the same typeConstrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor must have rank 2Number of times to sample.Output type must be int32 or int64BernoulliConstrain output types to all numeric tensors and bool tensors.The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.X_greater = Greater (X_random, input)output = Cast (X_greater)X_random = RandomUniformLike (input)Constrain input types to common numeric type tensors. { 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) }> } (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.Constrain output types to be numerics.Constrain input types.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Shape input must be a one-dimensional tensor.Invalid shape value: D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Lower boundary of the output values.Upper boundary of the output values.Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)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.MaxUnpool 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.The 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.ccConstrain input and output types to float and 8 bit tensors.Attribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeThis operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeConstrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.number of groups input channels and output channels are divided into. default is 1.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not. 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. Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthp value of the Lp norm used to pool over the input data.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Attribute pooled_shape must be specifiedROI pool output shape (height, width).1-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Input is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.Scaling parameter.The exponent.The number of channels to sum overConstrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). List of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseInput shape must have either [C] or [1,C] dimensions where C > 0list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.running_varrunning_meaninput_varinput_mean) for attribute 'axis'Invalid value(The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The axis on which to apply normalization, -1 mean last axis. { 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) } 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.length of each output. Values should be >= 0.Invalid Target shape product of 0Target shape may not have multiple -1 dimensionsInvalid position of 0D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.ccThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Constrains input and output to only numeric types. 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).'step' cannot be 0Ending indices (exclusive) of corresponding axis in axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAttribute 'scales' is required.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.) is not equal to the existing rank value (Ranks inferred ((Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]New shapelegacy optimization attribute.One float, indicates the value to be filled, default is 0paddingsThree modes: constant(default), reflect, edgeList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Which axis to split onlength of each outputoutputs...rank must be greater than axisWhich axis to concat on. Default value is 1.Constrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearThe scale along height dimension. It takes value greater than or equal to 1.height_scaleThe scale along width dimension. It takes value greater than or equal to 1.width_scaleConstrain tiles and axis's type to int64 tensors.tilesconsumed_inputsConstrain input and output types to all tensor and sequence types.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 lengthWhich axis to split on. dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Dilation value along each spatial axis of filter.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.Processed_STDStride along each spatial axis.STDX_variancevarFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.E_XsquaredVarianceEX_squaredX_squaredExponentX_RMD:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccThe ratio of random dropout(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. 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. 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 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. saved_varsaved_meanIf 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). Constrain output mask types to boolean tensors.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) First input tensor must have rank 3The 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) 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.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 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.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Mismatched tensor element type for output then=Mismatched type for output 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. else=(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).Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { 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.(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.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\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).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.ccA 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).axes as an input and attribute cannot be specified at the same time.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.const_zero_target_typedexpanded_target_int64loss_sumweight_gather_sumweight_gatherloss_unweightedloss_N1ddloss_Nddinput_gather_elementloss_NCddexpanded_target 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. Pass 1 to enable broadcastingX_LogDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeX_LogSM_NCDX_shapeX_NDCX_LogSMweight_gather_temp_1X_NCDconst_one_castedweight_gather_tempinput_gather_element_transformsqueeze_masktransform_targetsconst_zero_castedDescribes 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).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\old.ccNumber of top elements to retrieveDimension on which to do the sort.Invalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.broadcastIf set, defines the broadcast dimensions. See doc for details.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxValue of alpha default to 0.2Value of beta default to 0.5Constrains input to integral tensors.Constrains input to float tensors.If set, defines the broadcast dimensions.Enable broadcastingD:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccD:\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.Attribute 'value' of Constant node must exist with 'Tensor' data. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\old.ccElement type of inputs are expected to be the same.Input type for input at index is null. Type info is expected.SequenceConstruct is expected to have at least 1 input.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.cc(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.Input type for input at index 0 is null. Type info is expected.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).positioninput_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.Constrain input types to any tensor type.output_sequenceWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output types to all tensor types.Constrain split size to integral tensor.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\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.foward expected to have map typeElement type of optional input expected to have optional typeElement type of sequence input expected to have sequence typeElement type of input unknown expected to have tensor or sparse tensor type. Got: Input type was nullMismatched map tensor key type:target map type missing key type.source map type missing key type.target optional type missing element type.source optional type missing element type.target sequence type missing element type.source sequence type missing element type.Mismatched sparse tensor element type:Mismatched tensor element type:Mismatched type: source= target=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 either tensor, sequence, optional or map type. Got Value type of map input was unknownKey type of map input was unknownInput 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 unknownInput 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 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.. Expected: Actual:The type of tensor: is undefined so it cannot be parsed.Data size mismatch. Tensor: expected size does not match the actual sizeCannot parse data from external tensors. Please load external data into raw data for tensor: ParseData type mismatch for tensor: graph ==> Context: Data of TensorProto ( tensor name: ) is stored externally and should not have data field.uint64_datadouble_dataraw_dataint64_datastring_dataint32_datafloat_datasetting data_type field (tensor name: ) to UNDEFINED is not alloweddata_typeUnrecognized type value case (value_info name: ): value_typekey_typeUnrecognized data_type (tensor name: values of data_type '' should be stored in field '' instead of 'STRING data (tensor name: ) 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!TensorProto ( tensor name: ) is stored externally but doesn't have a location.) should be stored in , but it doesn't exist or is not accessible.elem_type is required but missing.Field 'name' of value_info is required to be non-empty.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.Bad node spec for node. Name: OpType: Op registered for is deprecated in domain_version of No Op registered for with domain_version of No opset import for domain 'Warning: Checker does not support models with experimental ops: NodeProto (name: , type: ) has zero input and zero output.op_type' has been used as output names multiple times.Nodes in a graph must be topologically sorted, however input '' of node: name: is not output of any previous nodes. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty name in initializer but not in graph input initializer name is not unique) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () index value at position [] out of range [0, Sparse tensor indices () has values, but NNZ is ) 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 attr) has no index values.) must have rank 1 or 2.) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values () must have rank 1.sparse_tensor_proto] not in lexicographic sorted order.] out of range.) second dimension size does not match rank of tensor.ConstantFillstol argument out of rangeinvalid stol argumentUnexpected type.Identifier expected but not found.String value expected, but not found.Unexpected literal type.Integer value expected, but not found.Value expected but not found.Expected character (line: column: Unexpected attribute type.Unhandled type: %dError parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).type_protossparse_tensorsgraphstensorsstringsfloatstype_protoint[ParseError at position Error context: type case unsupported for symbolic shape inference. inferred=type case unsupported. existing=key type mismatch from MapProto. existing=type case mismatch. existing= inferred=NOT_SETsparse_tensor_typeopaque_typeoptional_typeData for input already exists.in initializers. Graph initializer names must appear after the actual inputs: 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 inputs but were providedContainer for generated shape data cannot be nullptr when enable_data_propagation option is set.Shape inference error(s): Cannot infer type and shape for node name unk__Inferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: ((op_type:, node name: Cannot find missing input: 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.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.ModelProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.AttributeProtoonnx.FunctionProtoonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Optionalonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TensorProtoFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError parsing 'Error compiling 'pattern too large - compile failedError reverse compiling 'Invalid RE2: text size: endpos: startpos: RE2: invalid startpos, endpos pair. [Unexpected re_anchor value: bytemap range list count program size pattern length DFA out of memory: Unknown encoding D:\a\_work\1\s\cmake\external\re2\re2\re2.cc': invalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledRegexp not destroyed.D:\a\_work\1\s\cmake\external\re2\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: no errorunexpected errorStack not empty.D:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hWalk NULL@NkPNk`LkxLkLkLkLkLkLkLkMk0MkHMk`MkpMkD:\a\_work\1\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classMissing case in Compiler: should never happenAddFoldedRange recurses too much.D:\a\_work\1\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad hex digit AnyBad call to ParseState::ParsePerlFlags in AddToThreadqunhandled D:\a\_work\1\s\cmake\external\re2\re2\nfa.cc in stepUnhandled context does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: unhandled opcode: D:\a\_work\1\s\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.Failed to analyze start state.|RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCachejob_.size() = njob_ = GrowStack() failed: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccUnexpected opcode: Cannot use SearchOnePass for unanchored matches.D:\a\_work\1\s\cmake\external\re2\re2\onepass.ccUnexpected opcode in IsMatch: D:\a\_work\1\s\cmake\external\re2\re2\prog.ccCase not handled in ComputeSimple: D:\a\_work\1\s\cmake\external\re2\re2\simplify.ccCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat \k]k]k]k]kH^k]kH^k ]k0^k]k0^k]kp^k(]kp^k8]k]kH]k]kX]k]kh]k]kx]k^k]k^k]kX^k\kX^k\k]k \k]k0\k@^k@\k@^kP\k]k`\k]kp\k(^k\k(^k\k`^k\k`^k\k]k\k]k\k]k\k]k\k^k]k^k]k^k]k^k[:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:]\d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:] 09az09[:^word:][:xdigit:][:^xdigit:]AZazAZ09AFaf 09AZ__az ~09AZ__az!~ !/:@[`{~09AZazkpkkkkk kkk#(kk8k(k@kkLkkk@k kLkkXkk`kkpkk|kkkkkXkk`kbik`k kkkkȃkk܃kkk@k 8kkkkkkkkkkdk8kk@k[ kRkkkpkk(kȂk@kЂkkkkkkkkkhkkk kؓk8kkk kk k kk kkРkĆk@kІk0k؆kpk!kkkk8kkЧk kk kkHkkhk(kk0kk8kȫk kkБkkXkȅk k0k؅kkk`kkkkk kk (kkk8kkHkkXkklkktkkkXkkpk|0k$kؤk (kk 0kk8kk@kkPk`k\kk0kdkkck"hk k7klk@k"kpkk tkkYPk%xkkkkkikkgkkkkXkPkhkpkpkkkpkkkkkmkBkkkkkkkkЄkkkkkkkkku kkkkkTkkXkk`kkhkkCkBpkkkkkk%kk8kk(kkxkkkHkk`kk*kkkkЉkk؉kkxk(kkkk0kkkkkЈkkkkk(kkk kkkkipkСk5 kk0kk@kkLk8kPkkpkkkHkk kԤkkkkȶk kkPk4kkK kk0kk8kpkhgkpkQ@kkPkPk\kkk`khkhkkpkk@kkPk k kXkkk\k0k5Pk `k kpkFhkkpkxkkkkxkkkkkkkk(kćkkЇkkk k8kУk@kkkHk8kPkxk Xk0k`kkhkhkpkkkxkkkkkk(kkHkkkkpkxxixkkpk(kk,kk0kkCherokeeChorasmianCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamAdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdMroMultaniMyanmarNNabataeanNandinagariNdNew_Tai_LueNewaNkoNlNoNushuNyiakeng_Puachue_HmongOghamOl_Chiki$$   ?? 88ii@[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++.R.//00000 00070<0?00000001111 2_22222X33MM!09..ϩϩ[[jk>?0RTfhk ;@[epp3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq-/= --  ....:.;.@.@.0000000012XXcc ooWW  ? ? 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Ld.?AV@@ Ld.?AV@@ Ld.?AU?$Powx@M@functors@onnxruntime@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.P6A?AVStatus@common@onnxruntime@@AEBVTensor@2@AEAV32@PEAX@Z Ld.P6A?AVStatus@common@onnxruntime@@PEB_J0PEA_J_K222222PEAVThreadPool@concurrency@2@PEAX@Z Ld.P6A?AVStatus@common@onnxruntime@@AEBV?$span@$$CB_K@gsl@@AEBVTensor@2@AEAV52@PEBVTensorShape@2@PEAX@Z Ld.P6A?AV?$unique_ptr@VTensor@onnxruntime@@U?$default_delete@VTensor@onnxruntime@@@std@@@std@@AEBVTensor@onnxruntime@@_J1V?$shared_ptr@VIAllocator@onnxruntime@@@1@PEAX@Z Ld.P6A?AV?$unique_ptr@VTensor@onnxruntime@@U?$default_delete@VTensor@onnxruntime@@@std@@@std@@AEBVTensor@onnxruntime@@V?$span@$$CB_J@gsl@@_NV?$shared_ptr@VIAllocator@onnxruntime@@@1@PEBVTensorShape@3@PEAVThreadPool@concurrency@3@PEAX@Z Ld.P6A?AVStatus@common@onnxruntime@@PEBM0PEAM_K222222PEAVThreadPool@concurrency@2@PEAX@Z Ld.P6A?AVStatus@common@onnxruntime@@PEBN0PEAN_K222222PEAVThreadPool@concurrency@2@PEAX@Z Ld.P6A?AVStatus@common@onnxruntime@@PEBH0PEAH_K222222PEAVThreadPool@concurrency@2@PEAX@Z Ld.?AU?$MaxPool1DTask@C@onnxruntime@@ Ld.?AU?$MaxPool3DTask@C@onnxruntime@@ Ld.?AU?$MaxPool2DTask@N@onnxruntime@@ Ld.?AU?$MaxPool3DTask@E@onnxruntime@@ Ld.?AU?$MaxPool1DTask@M@onnxruntime@@ Ld.?AU?$MaxPool2DTask@M@onnxruntime@@ Ld.?AU?$Pool3DTask@MVLpPool@onnxruntime@@@onnxruntime@@ Ld.?AU?$MaxPool1DTask@E@onnxruntime@@ Ld.?AU?$MaxPool2DTask@E@onnxruntime@@ Ld.?AU?$MaxPool3DTask@M@onnxruntime@@ Ld.?AU?$MaxPool3DTask@N@onnxruntime@@ Ld.?AU?$MaxPool2DTask@C@onnxruntime@@ Ld.?AU?$Pool2DTask@MVLpPool@onnxruntime@@@onnxruntime@@ Ld.?AU?$MaxPool1DTask@N@onnxruntime@@ Ld.?AU?$Pool1DTask@MVLpPool@onnxruntime@@@onnxruntime@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ 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Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AU?$QLinearPool3DTask@EVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPool3DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AV@@ Ld.?AU?$QLinearPoolNhwc2DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPoolNhwc3DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPoolNhwc1DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPool2DTask@EVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPoolNhwc1DTask@EVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPool1DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPoolNhwc2DTask@EVAveragePool@onnxruntime@@@contrib@onnxruntime@@ Ld.?AU?$QLinearPool2DTask@CVAveragePool@onnxruntime@@@contrib@onnxruntime@@ 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Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.P6A_NAEBUFunctionBodyBuildContext@onnx@@AEBVOpSchema@1@AEAVFunctionProto@1@@Z Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ Ld.?AV@@ 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