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Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %I64u̽]ؽ]]]Can't happenD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): falsetrue\\x%02xty10invalid string position...\\?\\/INVALID_ARGUMENT:, map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.Stacktrace: Cpuai.onnx.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)CPUExecutionProviderelem_type_ != nullptrTensor sequence must contain only primitive typesD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::Geti < tensors_.size()Trying to get a Tensor, but got: OrtValue::GetIsTensor()OrtValue::GetMutableD:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hIsTensorSequence()Trying to get a TensorSeq, but got: IsSparseTensor()Trying to get a SparseTensor, but got: ORT_LOAD_CONFIG_FROM_MODELInteger overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowtried creating tensor with negative value in shape, got size overflowNot able to find appropriate IDataTransfer to copy sparse datanot enough space: expected D:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cc`anonymous-namespace'::GetDataTransfer`anonymous-namespace'::ValidateFillInputArgsStrings can only reside in CPU memoryOrtApis::FillSparseTensorCootried Filling sparse tensor with negative value in values shapetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::FillSparseTensorCsrCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::FillSparseTensorBlockSparseOrtApis::UseCsrIndicesOrtApis::UseCooIndicesthe ort_value must contain a constructed tensorOrtApis::UseBlockSparseIndicesUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsonnxruntime_profile_EnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledoutput name cannot be emptyinput name cannot be emptystring buffer allocation failedlengths allocation failedinput array doesn't equal tensor sizeOutput buffer allocation failedOrtValue should contain a Tensor or a Sparse Tensorelement index is out of boundsThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse dataindex is out of boundsshape is invalidoutput buffer is too small. Use GetStringTensorDataLength.offsets buffer is not equal to tensor sizeout of indexbuffer size is too small for string elementindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.input array is too shortin[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.OrtCreateValueImplSeqHelperAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrlocation dimensions do not match shape sizethis API does not support stringsmax_meminvalid location rangeinitial_chunk_size_bytesarena_extend_strategyinitial_growth_chunk_size_bytesmax_dead_bytes_per_chunkThe given version [%u] is not supported, only version 1 to %u is supported in this build. Invalid key found: 1.9.0Tensor type mismatch. T onnxruntime::Tensor::MutableDatautils::IsPrimitiveDataType(dtype_)Invalid index requested for map type.onnxruntime::Tensor::DataAsSpanvalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelper++index < c.size()Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::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 execution provider is not enabled in this build. is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Unsupported version 'Result buffer is not large enoughcustom op registered at runtimeTonnxruntime::CustomOpKernel::CustomOpKernelD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.type_id_counter == 1Inputall typesOutputonnxruntime::CreateCustomRegistryT0OrtMemoryInfo is nullProvided allocator is nullNo 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 nullAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hTensor type mismatch.type == dtype_) != new size (Tensor size (onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensorSeq: tensor to be added has a different data type.shape_.Size() == new_shape.Size()onnxruntime::Tensor::ReshapeIsSameDataType(tensor)onnxruntime::TensorSeq::Addoutput_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::ProviderLibrary::Getonnxruntime::ProviderSharedLibrary::EnsureD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dllGetProvideronnxruntime_providers_cuda.dllonnxruntime_providers_tensorrt.dllSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA execution provider is either not enabled or not available.SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryTensorRT execution provider is not enabled in this build.invalid unordered_map keyonnxruntime::OpKernelContext::InputRequired input at index Missing Input: input_ptronnxruntime::OpKernelContext::RequiredInputenv_ptr == p_instance_OrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\ort_env.cconnxruntime::IOBinding::BindInputD:\a\_work\1\s\onnxruntime\core\session\IOBinding.ccp_providerbad cast MemoryType: DeviceId:] OrtMemType: OrtAllocatorType:Device:[DeviceType:OrtMemoryInfo:[name: id:CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProvider Max:D:\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: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'ORTM[json.exception.] MemcpyTransformerother_errorsession.set_denormal_as_zerosession.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.inter_op.allow_spinningmemory.enable_memory_arena_shrinkagesession.use_ort_model_bytes_directlysession.intra_op.allow_spinning() node with name '%Y-%m-%d_%H-%M-%Sonnxruntime::`anonymous-namespace'::VerifyEachNodeIsAssignedToAnEpNode placements (Could not find an implementation for : [All nodes have been placed on [].D:\a\_work\1\s\onnxruntime\core\session\inference_session.cc are '0' and '1'. The environment variable contained the value: 4 Provider: [ModelProto 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()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 SEQUENTIAL-inter-opsession-Given model could not be parsed while creating inference session. Error message: CastFloat16Transformersession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessiononnxruntime::InferenceSession::~InferenceSessionUnknown error during EndProfiling()onnxruntime::InferenceSession::{dtor}::::operator ()Error during EndProfiling(): Execution providers must be registered before the session is initialized.onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized. Received nullptr for exec providerParallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsParallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.onnxruntime::InferenceSession::LoadThis session already contains a loaded model.Exception during loading: onnxruntime::InferenceSession::SaveToOrtFormatModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Encountered unknown exception in Load()Unknown exception in Load()model_loading_arrayFailed to load model because protobuf parsing failed.onnxruntime::InferenceSession::TransformGraphmodel_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.ORT model verification failed.This session has already been initialized.onnxruntime::InferenceSession::LoadOrtModelonnxruntime::InferenceSession::LoadOrtModel::::operator ()The ORT format model version [] is not supported this build Serialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.The session already has a PrePackedWeightsContainer instanceThe provided PrePackedWeightsContainer instance to be added to the session is nullSessionState is null. Invalid ORT format model.Missing Model. Invalid ORT format model.'.onnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesImplonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelException during initialization: onnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesFailed to find kernel def hash () in kernel registries for onnxruntime::InferenceSession::InitializeInitializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Adding default CPU execution provider.Session has already been initialized.Model was not loaded.Model was not loadedNchwcTransformerUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.loading_ort_format && serialized_session_state != nullptrThis session will use the allocator registered with the environment.session_initializationEncountered unknown exception in Initialize()Session successfully initialized.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Unexpected input data type. Actual: ( index: Got invalid dimensions for input: for the following indices elements, but feeds has elements.)))) , expected: (Input with name: is not expected to be of type tensor.Invalid Feed Input Name:Size mismatch: feed_names has sparse_tensor is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputstensorAt least one output should be requested.Output vector pointer is NULLseq is not expected to be of type tensor sequence.Session was not initializedInvalid Output Name:Output vector incorrectly sized: output_names.size(): p_fetches->size(): Encountered unknown exception in Run()Running with tag: Session not initialized.onnxruntime::InferenceSession::Runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelMetadatamodel_runonnxruntime::InferenceSession::EndProfilingProfiler is disabled.onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsUnsupported device specified in the memory arena shrink list: Could not write a profile because no model was loaded. combination is not an arena based allocator: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: error message: The registered allocator for device-id Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)/961c151d2e87f2686a955a9be24d316f1362bf21 3.9.1model_loading_uri_.json bytes were able to be read.onnxruntime::LoadOrtModelBytesLoad model from failed: failed. Only list too longonnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0com.microsoft.nchwccom.microsoft.experimentalcom.microsoftcom.microsoft.mlfeaturizersInput type was null[TypeInferenceError] unknownInput expected to have tensor or sparse tensor type. Got: expected to have sequence type. Got: expected to have sequence typeOutput Element type of input expected to have type but instead is nullUnsupported Source/Target type=[ShapeInferenceError] Mismatch between source and target type. Source= Target=tensor(int32)tensor(uint64)tensor(uint32)unknowntensor(double)tensor(float)tensor(float16)tensor(int64)tensor(int8)tensor(uint16)tensor(uint8)tensor(bfloat16)seq(tensor(uint32))seq(tensor(uint16))seq(tensor(uint8))tensor(int16)seq(tensor(int32))seq(tensor(int16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(double))seq(tensor(float))seq(tensor(float16))seq(tensor(int64))tensor(complex128)tensor(complex64)tensor(bool)tensor(string)seq(tensor(complex128))seq(tensor(complex64))seq(tensor(bool))seq(tensor(string)) line Schema error: (domain: version: ) from file , but it is already registered from file in onnx/defs/schema.h)., but its domain is not known by the checker.Trying to register schema with name in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange , but its version is not Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.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.seq(tensor(bfloat16))stringNo allocator for this device has been registered for sharing.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.Y: valueparse_errorparse errorinvalid_iterator at line , column session_optionsort_configout_of_rangetype_errorSetting intra_op_num_threads to D:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Unsupported execution_mode value in ORT config: Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting execution_mode to Parallel modeSequential modeonnxruntime::SetExecutionModeSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to The Model Proto hasn't been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelintra_op_num_threads option in the model file must be an integerintra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelexecution_mode option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsenable_profiling option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelkey '' not foundIgnoring unsupported session option in ORT config: cannot use at() with cannot use key() for non-object iteratorsobjectnullinvalid map keydiscardedbinarybooleanarraynumbercannot get valuecannot compare iterators of different containerssyntax error unexpected ; last read: '- while parsing invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if given; expected null literalfalse literaltrue literal'{''['number literalstring literal','':''}'']'unknown token'[', '{', or a literalend of inputinvalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid number; expected digit after '-'invalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid comment; expecting '/' or '*' after '/'invalid comment; missing closing '*/'invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dvector too longobject keynumber overflow parsing 'object separatoriterator out of rangeiterator does not fit current valuecannot use erase() with type must be number, but is cast node to cast from float16 to float32 on cpuCasttoRemoveDuplicateCastTransformeronnxruntime::GraphTransformer::RecurseD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hInsertedCast_InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()onnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImpldtypeACLExecutionProviderArmNNExecutionProviderROCMExecutionProviderCastEliminationCommonSubexpressionEliminationAttentionFusionBiasGeluFusionBiasSoftmaxFusionConvBNFusionConvMulFusionDivMulFusionDivConvActivationFusionConvAddFusionConvExpandEliminationExpandFastGeluFusionGeluApproximationEliminateDropoutDropoutDynamicQuantizeMatMulFusionEmbedLayerNormFusionEliminateIdentityIdentityLayerNormFusionSimplifiedLayerNormFusionGeluFusionGemmActivationFusionGemmTransposeFusionGemmNoopEliminationAddNotWhereFusionWhereMatMulAddFusionMatMulIntegerToFloatFusionMatMulScaleFusionNhwcTransformerEliminateSliceSliceUnsqueezeEliminationUnsqueezeFuseReluClipReluReshapeFusionSkipLayerNormFusionQDQPropagationTransformerQDQS8ToU8TransformerReluQuantRewritesession.disable_quant_qdqoptimization.enable_gelu_approximationUnsupported levelonnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.ccMatmulTransposeFusionBiasDropoutFusion_RuleBasedTransformerLevelonnxruntime::optimizer_utils::GenerateTransformersUnsupported level VitisAIExecutionProvideronnxruntime::Node::ForEachWithIndexD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'onnxruntime::MemcpyTransformer::ApplyImplonnxruntime::TransformerMemcpyImpl::ProcessInitializersMemcpyCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: ConstantFoldingstartCould not find OrtValue with name 'Shape node 'can't constant fold Could not find a CPU kernel and hence endDequantizeLinearonnxruntime::ConstantFolding::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccfetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of Unsupported data type: shapeConstant initializer NodeArg shape should not be null. NodeArg: D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.cconnxruntime::`anonymous-namespace'::GetScalarConstantInitializeronnxruntime::`anonymous-namespace'::GetInputNodeMergesinput_node.InputDefs().size() == 2 && scale_and_index.value().second < 2onnxruntime::`anonymous-namespace'::GetOutputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetScaleFromNodediv_inputs.size() == 2mul_inputs.size() == 2Mulonnxruntime::MatMulScaleFusion::ApplyImpl_FusedMatMulAndScaleFusedMatMulMatMulFused MatMul and Scalealphaonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::AttentionFusionHelper::CheckSliceParametersinput_indices.size() == expected_values.size() && input_indices.size() > 0Slice does not have enough number of inputsD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hReshapeonnxruntime::AttentionFusionHelper::MatchGemmSubgraphSqueezeConcatExpected value:Slice ends is less than INT_MAXStart MatchGemmSubgraphSlice parameter is not expected. Input index: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 pathOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathunsqueeze_after_gather axes value not expectedaxesconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsGatherconcat_after_gather input 2 does not have expected valueonnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerGemm bias shape is not expectedGemm bias is not constant initializergather axis value not expectedaxisPass MatchGemmSubgraphgather input 1 value is not expectedunidir mask shape not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskMask is neither unidirectional nor all onesThis optimizer does not support external data for unidirectional mask right nowGemm weight shape is not expectedGemm weight is not constant initializerunidir mask is not constantPass ValidateGemmInitializerFaild to match path 1 for unidirectional maskSubDiv and Shape1 does not have edgeOutput edge count not expected for nodes in path 1 of unidirectional maskStart MatchUnidirMaskSubgraphExpect mask data type is uint8 or floatFaild to match the path (Div-->Where-->Add) for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for slice1ValidateUnidirMask returns false for mask_sliceStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskmask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3mask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2Output edge count not expected for SoftmaxFailed to find Softmax nodeOutput edge count not expected for mask nodesFailed to find path for maskwhere const not matched.Failed to find mask pathequal const not matched.Failed to find shape pathPass MatchInputMaskSubgraphmask_mul const input not matchedEqualStart MatchInputMaskSubgraphDistilBertonnxruntime::AttentionFusionHelper::MatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kTransposeFailed to find reshape shape path 2Failed to find reshape shape path 1Pass MatchInputMaskSubgraphDistilBertgather indices not matched.present_k_transpose perm attribute not matchedpast_k_transpose perm attribute not matchedpresent_v_unsqueeze axes value not expectedpresent_k_unsqueeze axes value not expectedFailed to find path for present_v and past_vFailed to find path for present_kpermFailed to match v_concatonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapePass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckNodesInPathVStart CheckNodesInPathVpast_k_gather indices != 0past_v_gather indices != 1Output edge count not expected for nodes in past subgraphpast_v_gather and past_k_gather does not have same past inputPass CheckNodesInPathVv_reshape initializer value is not expectedStart CheckNodesInPathQreshape initializer value is not expectedFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Output edge count not expected for nodes in path vhidden_size != num_heads * head_sizeFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Start CheckNodesInPathKPass CheckNodesInPathQk_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKq_reshape const not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_transpose perm attribute not matchedqk_div const not matched.Start FuseGptAttentionFaild to find path to qkv_matmulonnxruntime::AttentionFusionHelper::FuseGptAttentionPass CheckNodesInPathKk_reshape const not matchedCast mask from int64 to int32Mask_Int32Failed to find path for qMatchUnidirMaskSubgraph returns NULLCheckNodesInPathQ returns falseq and v are not from same Split nodeFaild to find path v to SplitSplitMatchInputMaskSubgraph returns falseCheckNodesInPathV return falseAttentionFused Attention subgraphs unidirectionalnum_headsk and v are not from same Split nodeFailed to find path for kMatchPastSubgraph returns falseCheckNodesInPathK returns falseD:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32onnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64 or float32qkv_weightsFused an attention node for GPT.Mask shape is unknown or not 2D, or data type unknownqkv_biasq_matmul and q_add shape not matchedq root should be layer normalizationk_matmul and k_add shape not matchedk root is not layer normshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::FuseSubGraphQKImplTotal fused Attention node count: onnxruntime::FuseSubGraphQKDistilBertFused an attention node.onnxruntime::AttentionFusion::FuseSubGraphFaild to find path vFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.onnxruntime::FuseSubGraphQKFailed to convert mask to int32Failed in match v_matmul and v_add input shapeOutput edge count not expected for Add or MatMul in path vFailed in match input mask subgraphdata 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_QuantizeLinearD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccqdq_s8_to_u8_quantqdq_s8_to_u8_zp_conversiononnxruntime::QDQS8ToU8Transformer::ApplyImpl2DQAveragePoolQDQSelectorActionTransformer*DQdrop1DQMaxPoolClipD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccmaxminonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of LeakyReluTanhbetaHardSigmoidFusedConvonnxruntime::ConvActivationFusion::ApplyImplSigmoidactivationfused Conv with activation activation_paramsfused UnsqueezeElimination cannot remove node UnsqueezeElimination_D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::Applydata type Unexpected data type for Clip 'min' input of is not supported.D:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyFuseReluClip__min_zero_constantBiasGeluGeluTotal Gelu Approximation (FastGelu) node count: Gelu approximationFastGeluonnxruntime::GeluApproximation::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.cc^^@^endsCannot replace concat node with initializer:Total fused reshape node count: startsallowzeroFused reshape node: onnxruntime::ReshapeFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::Fuse_Subgraphup_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0).edge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index()onnxruntime::SwapAdjacentNodesD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.ccup_node should have only one Edge that points to down_node and its output is not graph outputoptimizer_utils::CheckOutputEdges(graph, up_node, 1)SwapAdjacentNodesonnxruntime::QDQPropagationTransformer::ApplyImpl̼+_Int32Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphD:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Input shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputCast Input from int64 to int32_CastThe parent of two shape nodes are expected to be input_ids.two paths share the same shapeGather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.RangeOutput edge count not expected for nodes in path1.NonZeroConstantOfShapemask_indexfused EmbedLayerNorm subgraphs Failed to match position subgraph.position_embeddingsOptional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get initializer tensor.Position embedding shape not matched.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.EmbedLayerNormalizationWord embedding shape not expected.Beta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertInput_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Input id is not valid. Segment id is not valid. Failed to get position embedding weights.Position embedding shape is not expected.onnxruntime::EmbedLayerNormFusion::ApplyImplonnxruntime::DynamicQuantizeMatMulFusion::ApplyImplDynamicQuantizeLinearDynamicQuantizeMatMulD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatwith a fixed dimension size which does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.FreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input '' D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplgemmfused Matmul and Add onnxruntime::GemmActivationFusion::ApplyImplThresholdedReluParametricSoftplusScaledTanhSoftsignSoftplusSeluEluactivation_FusedGemmfused Gemm D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cc_transformedFused Gemm with TransposetransBtransAonnxruntime::UpdateConsumerCount!node_consumers.empty()D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cconnxruntime::GetTransposePermstranspose_node.InputDefs().size() == 1MatMul_With_Transposefused MatMul and Transpose onnxruntime::MatmulTransposeFusion::ApplyImplCreated a new Transpose node to interchange Cast and Transpose nodesCreated a new Cast node to interchange Cast and Transpose nodesonnxruntime::ReorderCastAndTransposecast != nullptrdeque too longonnxruntime::ConvMulFusion::ApplyConvMulFusion_Mul_B_ConvMulFusion_W_mul_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::SimplifiedLayerNormFusion::ApplyImplfused LayerNorm subgraphs PowSqrtReduceMeanD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplcast output of layer normlayer_norm_outSimplifiedLayerNormalizationcast scale of layer normCast_Scale'7SkipLayerNormalizationfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cconnxruntime::SkipLayerNormFusion::ApplyImpl^^@^̼+reorderReorderInputchannels_lastpadsstridesdilationsreshapespatial_bn_nchwcReorderOutputchannelsmodekernel_shapeauto_padNOTSETSAME_UPPERSAME_LOWERVALIDgroup_nchwcUpsamplescalesSumResizeGlobalMaxPoolGlobalAveragePoolonnxruntime::NchwcTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.ccnearestlinearcoordinate_transformation_modeasymmetricalign_cornershalf_pixelnearest_modefloor and is not in valid range [-axis axis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxisD:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::BiasSoftmaxFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cc into softmax(input + bias)BiasSoftmaxsoftmax_axisbroadcast_axisonnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.[ of node Not eliminating output Not_nhwcQLinearConvNhwcMaxPoolstorage_ordernhwc_permutated_padsQLinearAddQLinearMulQLinearLeakyReluQLinearSigmoidQLinearGlobalAveragePoolQLinearAveragePoolQLinearConcatPadonnxruntime::NhwcTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::BiasDropoutFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBiasDropoutfused Add and Dropoutseedonnxruntime::MatMulIntegerToFloatFusion::ApplyImplMatMulIntegerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.ccD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::GeluFusion::ApplyImplErfD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccfused Gelu subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGeluFusion::ApplyImplfused Add and GeluGPT2Gelufused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplfast_gelu_outputD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cc^^@^^ConvAddFusion_Add_B_ConvAddFusion_B_onnxruntime::ConvAddFusion::Applyadd_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccTensorProto external data size mismatch. onnxruntime::Initializer::ReadExternalRawDataExternal data type must not be UNDEFINED or STRING.model_path must not be empty. Ensure that a path is provided when the model is created or loaded.D:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccComputed size: , external_data.length: RandomNormalRandomUniformLikeRandomUniformRandomNormalLikeMultinomialFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=target_node != nullptronnxruntime::NodesToOptimizeBuilder::BuildD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hA target node must be set.onnxruntime::NodesToOptimize::GetNodestatic_cast(index) < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)QLinearb33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8generated at runtimeb33f88f7-c464-43e3-8692-97ac832bb14aonnxruntime::SelectorActionTransformer::ApplyImplonnxruntime::SelectorActionTransformer::MatchAndProcessTODO: Save the selected nodes into the Graph.onnxruntime::SelectorActionTransformer::SelectorActionTransformerD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMultiple entries for operator is not supported. OpType=insertedonnxruntime::SelectorsAndActions::RegisterSelectorAndActionMatched Existing registration with name selectors_and_actions_map_.find(name) == selectors_and_actions_map_.cend()onnxruntime::MergeIntoTarget::RunD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::ReplaceWithNew::Runonnxruntime::`anonymous-namespace'::MoveInputOutputImplD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.ccIndex out of rangeonnxruntime::MoveInputOutputNnapiExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderCoreMLExecutionProviderMIGraphXExecutionProviderRknpuExecutionProvider]]З^ :^^^h^(?^P^^:^9^]^]D:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsscores_tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeboxes_tensorboxes and scores should have same spatial_dimension.boxes and scores should have same num_batches.scores must be a 3D tensor.boxes must be a 3D tensor.center_point_box only support 0 or 1center_point_boxNonMaxSuppressionD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase0 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].The most inner dimension in boxes must have 4 data.output != nullptronnxruntime::NonMaxSuppression::Computeinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueonnxruntime::Gather::ComputeTindD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.honnxruntime::GatherBase::GatherBaseGather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccindices 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::UnsqueezeBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareComputeX != nullptraxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a scalar or a 1-D tensor.axes_tensor != nullptrAxes input is nullonnxruntime::Unsqueeze::Computenullptr != p.output_tensor'axes' has a duplicate axis'axes' has an out of range axisT1bad allocationInvalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != momentumTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNormD:\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))training_modeUT2onnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNorm((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOcalled_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceCan broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!onnxruntime::BroadcastIterator::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid. by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largeststart_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocatorstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Broadcast Output range [offset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:Invalid start/ending offset [ReciprocalExpLogMaxAbsNegFloorCeilinputCount >= 1onnxruntime::Min_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeOrXorUnsupported X type: Must have 1 or more inputsGreaterOrEqualMeanBitShiftAndMinLessGreaterLessOrEqualAsinhAcoshAtanhPReluAcosAtanSinhCoshSinCosTanAsindata_n.Shape() == shapeonnxruntime::Max_6::Computeonnxruntime::Mean_6::Computedata_1.Shape() == shapeonnxruntime::mod_internal::CallModImpl::operator ()input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.h(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModModfmod attribute must be true for floating point typesshape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandfmodfmod must have value either 0 or 1ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernel'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::BitShift::Compute::::operator ()directiononnxruntime::BitShift::BitShiftLEFTRIGHTinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::ComputeBroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()RNNonnxruntime::RNN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccbidirectionalforwardreverseinfo.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()activation_betaactivationsinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeonnxruntime::RNN::RNND:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()activation_alphaAffinecliplayoutinfo.GetAttr("direction", &direction_).IsOK()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0onnxruntime::LSTMBase::LSTMBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.fD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeLSTMonnxruntime::DeepCpuLstmOp::PrePackD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccLSTM operator does not support double yetinput_forgetsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 3info.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetGRUactivation_func_names.size() == static_cast(num_directions_) * 2D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpInvalid data type for GRU operator of GRU operator does not support double yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::Computeonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::Tensor::MutableDataAsSpanonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())C + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endonnxruntime::rnn::detail::SafeRawConstPointercur + size <= endlda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmVD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::IdentityOp<1>::ComputeUnable to get an allocatorD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelperThe input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:requested_shape[i] >= -1A dimension cannot be less than -1, got size != 0 && (input_shape.Size() % size) == 0i < input_shape.NumDimensions()gsl::narrow_cast(input_shape.Size()) == sizeAt most one dimension can be -1.!allow_zeroThe dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1Attribute shape is not set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1A shape tensor must be a vector tensor.onnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1Input tensor should have a rank of at least 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccUnsupported input data type of k should be a 1-D or 0-D tensor.Triluonnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)info.GetAttr("upper", &temp).IsOK()upperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::Triluonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelAttribute name and type don't match for 'No attribute with name:'onnxruntime::ElementWiseKernel >::ComputeCelu'is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelgammaD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::HardSigmoid::Initonnxruntime::functors::Selu::Initunknown kernel typeonnxruntime::functors::ScaledTanh::Initonnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::SliceSkips::SliceSkipsdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hCumSumAxis tensor should be of type `int32_t` or `int64_t`Axis tensor should be 0D or 1DAxis tensor must be provided to the CumSum oponnxruntime::SliceIteratorBase::Initexclusiveonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.ccCannot apply CumSum operator on a scalaronnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initsteps.size()=dims.size() == extents_.size()onnxruntime::WritableSliceIterator::Initdims.size() == steps.size()dims.size() == starts.size()dims.size()=extents.size()=onnxruntime::WritableSliceIterator<__int64>::Initstarts.size()=LogSoftmaxD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13Hardmax. N=Hardmax inputs N, D and N * D must be < D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=onnxruntime::Hardmax::ComputeIf outputs.Bthen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::Computeonnxruntime::If::SetupSubgraphExecutionInfoCreateFeedsFetchesManager must be called prior to execution of graph.SetupSubgraphExecutionInfo should only be called once for each subgraph.info == nullptrelse_branchinfo.GetAttr("else_branch", &proto).IsOK()info.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.ccthen_branchnum_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::Info outputs which doesn't match the subgraph's 'If' node has Failed to create output tensor for If output onnxruntime::IfImpl::ExecuteFailed to create output tensor for Only tensors or sequence of tensors are suppportedsession_stateonnxruntime::IfImpl::Initialize' attribute.Subgraph SessionState was not found for 'Loop inputs. Found:Iinfo_ == nullptronnxruntime::Loop::SetupSubgraphExecutionInfoinfo.GetAttr("body", &proto).IsOK()onnxruntime::Loop::InitInconsistent shape in loop output for output. body Got: Expected:'Loop' node has num_subgraph_outputs - 1 == num_outputs but has outputs so the subgraph requires onnxruntime::Loop::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.ccGraph in 'body' attribute of Loop should have static_cast(num_subgraph_inputs) == subgraph_inputs.size() was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::LoopImpl::ConcatenateLoopOutputAll scan outputs MUST be tensorslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::InitializeLoop subgraph input 1 has unknown shape: 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: feeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of Subgraph SessionState was not found for 'body' attribute.onnxruntime::Loop::Computeexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outside`tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floor model uses the deprecated attributeyet this opset info.GetAttrs("scales", scales_).IsOK()extrapolation_valueonnxruntime::UpsampleBase::UpsampleBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsample.hcubicinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.] is not supported!nearest_mode:[round_prefer_ceilceilcoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModetf_crop_and_resize] is not supportted!pytorch_half_pixeltf_half_pixel_for_nnmode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be onnxruntime::UpsampleBase::ParseScalesDatascales size should be greater than 0.scales_size > 0'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)Resize operatorUpsample operatorScale value should be greater than 0.scale > 0scale >= 1onnxruntime::UpsampleBase::ScalesValidationReduceL2ReduceLogSumReduceL1onnxruntime::ValidateMustBeOverloadedOnly works on matrices with two dimensions.projected_index.size() > 0must be overloaded.D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cclast_loop_size > 0last_loop_red_size > 0onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyArgMaxArgMinReduceSumReduceSumSquareReduceMinReduceProdReduceLogSumExpReduceMaxaxes_tensor->Shape().NumDimensions() == 1Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateCommonFastReduceAn axes tensor must be a vector tensor.count == 1onnxruntime::ValidateNoTransposeReducefast_shape[0] * fast_shape[2] == output.Shape().Size()Reduction on all axes, output size should be 1.fast_shape.size() == 3onnxruntime::ValidateFastReduceKRKonnxruntime::ValidateFastReduceRKfast_shape[1] == output.Shape().Size()Output size mismatch.fast_shape[0] == output.Shape().Size()fast_shape.size() == 2onnxruntime::ValidateFastReduceKRkeepdimsonnxruntime::ValidateKeepDimsselect_last_indexonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.hnoop_with_empty_axesinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valueInvalid 'mode' attribute valueonnxruntime::PadBase::PadBasereflectedgeconstantPads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankPads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)pads_tensor.IsDataType()onnxruntime::Pad::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorUnexpected mode of onnxruntime::PadValueFromFloatCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1'pads' has wrong number of valuesdata_rank * 2 == pads.size()data_rank > 0onnxruntime::PadImplInput tensor has no dimensionsonnxruntime::PadInputWithDimValueOfZeroD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computeinput_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchGatherNDbatch_dimsindices tensor data type not supportedlast dimension of indices must not be larger than rank of input tensorinvalid index found, index = indices tensor must has rank larger than 0onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::Computefailed to get first output!D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::ComputeX input is required!Null input ptrIsNaND:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDBase::PrepareForComputeupdates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: input tensor and indices tensor must has rank larger than 0. input shape: , indices shape: ScatterNDonnxruntime::ScatterND::Computeinvalid indice found, indice = D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::ScatterScatterElementsScatterIndices type is not supported.Indices dim= at pos= is greater than input dim=Indices must have the same rank as Input. Indices rank=. Input rank=Indices vs updates dimensions differs at position= vs Indices and updates must have the same rankdata type is different from updates typeDepthToSpace requires input depth to be a multiple of (block_size * blok_size)SpaceToDepth requires input width to be a multiple of block_sizeSpaceToDepth requires input height to be a multiple of block_sizeSpaceDepth ops require a 4-D input. Provided rank: D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.blocksizeonnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCRUnsupported input type in DepthToSpace op: onnxruntime::DepthToSpace::ComputeUnsupported input type in SpaceToDepth op: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::Computeinput count mismatchDepthToSpaceSpaceToDepthGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsonnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_impl::::operator ()GatherElements op: Value in indices must be within bounds [ , ]. Actual value is onnxruntime::core_impl::::operator ()the tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyTileTile doesn't have an implementation yet for the type: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::Compute!input_tensor.IsDataType()Tile doesn't support string type yet'repeat' input tensor must have the same length as the 'input' tensor'repeat' input tensor must be 1 dimensionalonnxruntime::TransposeBase::TransposeBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: v >= 0 && static_cast(v) <= std::numeric_limits::max()Method IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 0onnxruntime::IncrementIndexAndComputeOffsetSetupD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cc(local_source >= source) && (local_source < source + num_blocks * blocksize)onnxruntime::DoTransposeImplTranspose not implemented for empty tensors.num_axes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)Transpose of element size not supported in this build. Size=onnxruntime::DoTransposeEltWise(local_source >= source) && (local_source < source + num_blocks)Mismatched data types between input and output Tensors. input_tensor_ptr != nullptronnxruntime::Transpose::Compute(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::TypedDoTransposeEltWise. shape= must be 1 instead of Dimension of input input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::Squeeze::ComputesplitInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.h yetSplit operator does not support onnxruntime::Split::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cc NumOutputs= Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=Cannot split using values in 'split' attribute. Axis=An split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1onnxruntime::Split::ComputeImplonnxruntime::SliceBase::SliceBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0Missing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Unexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerSteponnxruntime::SliceBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccStarts must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got Cannot slice scalarsonnxruntime::SliceBase::Computeoutput == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator () got: Ranks of input data are different, cannot concatenate them. expected rank: input_rank == reference_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Data type mismatchonnxruntime::ConcatBase::ComputeImplMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.hnew_axisonnxruntime::ConcatBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccinput != nullptrCannot concatenate scalarssrc and dst types must matchdst.DataType() == src.DataType()onnxruntime::DispatchStridedCopyD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/copy.hlast >= firstonnxruntime::StridedCopy::::operator ()counter.current_offset == lastonnxruntime::StridedCopy::::operator ()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::StridedCopy::::operator ()dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()onnxruntime::StridedCopyonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()X and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeratio input should have a single value.ratio_tensor->Shape().Size() == 1onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]across_channelsinfo.GetAttr("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hnormalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()onnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4LRND:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr("size", &size).IsOK()sizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNsize_ % 2 == 1size_ > 0alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()beta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()biasD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flattengsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisFlattenonnxruntime::Flatten::Computex_zero_point must be null or a scalar or 1D tensor or size 1.onnxruntime::PrepareForQDQzero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccx_zero_point must be null or 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)onnxruntime::DequantizeLinear::Computezero_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 0SizeSignonnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.h K: N:Gemm: Invalid bias shape for broadcastGEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0info.GetAttr("transA", &temp).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.honnxruntime::GemmBase::GemmBaseinfo.GetAttr("transB", &temp).IsOK()c_shape != nullptrc_shape is required if c_data is providedonnxruntime::GemmBroadcastBiasonnxruntime::MatMulComputeHelper::Computeleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hright operand cannot broadcast on dim left operand cannot broadcast on dim onnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.ccM_ == 1 && N_ == 1 was falsenum_dims_with_pad != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computemin_ <= max_D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Basemin should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()min->Shape().IsScalar()max should be a scalar.D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()invalid stoull argumentstoull argument out of rangeinvalid stod argumentstod argument out of rangeinvalid stoll argumentstoll argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.NaN%.8gsnprintf() failed with return value: -INFINFFailed to write value with snprintf().snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1snprintf_result > 0onnxruntime::`anonymous-namespace'::CastToStringinvalid expand shape->info.GetAttr("equation", &equation_).IsOK()onnxruntime::Einsum::EinsumequationMissing 'equation' attributeEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum opD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hEinsum is not supported yetEinsum op: An implementation for the input type onnxruntime::Einsum::DeviceComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.ccUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizepinfo.GetAttr("p", &p_).IsOK()Unsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsInput dimension cannot be less than 3.onnxruntime::PoolBase::Computeonnxruntime::PoolProcessContext::initD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hGlobalLpPoolonnxruntime::PoolAttributes::PoolAttributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()count_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modePad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0kernel_shape num_dims is not compatible with X num_dims.LpPoolD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.onnxruntime::Pool::ComputeUnsupported pooling size : onnxruntime::MaxPoolV8::ComputeImplInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsT3OneHotonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::ComputeDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()konnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.ccsortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestonnxruntime::TopkOpset11ConstructorCommonTopKop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()value of k must not be negativek tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueinput count mismatch, expected 1 input - the tensor to be processedk argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of start in Range operator should be scalar like tensor, yet got shape: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!Got nullptr for sequence input.SequenceLengthSY != nullptrSequenceLength: Got nullptr for output tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceLength::ComputeGot nullptr input for index tensoronnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxSequenceAtSequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceErase::ComputeSequenceEraseViolation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequenceonnxruntime::SequenceConstruct::Computenum_inputs >= 1split_size_sum () != split_dim_size (SplitToSequence operator does not support SplitToSequenceonnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputInvalid data type for split tensor SequenceAt: Got nullptr for output tensorInvalid sequence index () specified for sequence of size (I != nullptrUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSequenceEmpty: Got nullptr for output sequenceSequenceEmptyonnxruntime::SequenceInsert::ComputeS != nullptronnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertSequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (Got nullptr for input tensor.onnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0Split should be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeConcatFromSequence group: Invalid input shape: group count is <= 0output_shapeoutput_paddingauto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes kernel_shape: W: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::ConvAttributesX num_dims does not match W num_dims. X: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeConvTransposeInput channels is not divisible by group.filter number not equal to input channel number. filter_number: num_input_channels: onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. C: kernel channels: onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported.Not enough elements in strides. Expected: Output channels M is not divisible by group. M: Input channels C is not equal to kernel channels * group.onnxruntime::ConvAttributes::InferOutputShapeNot enough elements in pads. Expected: Not enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computeonnxruntime::MaxUnpool::MaxUnpoolinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()strides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hMaxUnpoolShape must be 1 dimensional as it's tensor data of a shapeIndex tensor shape should be same as that of the input data tensor to unpool.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.cconnxruntime::MaxUnpool::Computeoutput_shape is smaller than minimum required. output_shape: inferred output shape:Detonnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccMatrix dimensions are not equal. Square matrix is expectedaxes_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.ccCompressAttempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::Scan<9>::Initscan_output_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directions outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputScanOutput OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was onnxruntime::Scan<9>::SetupSubgraphExecutionInfofeeds_fetches_manager_ && info_onnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initializegsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputs' dimension but input 'Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was dimensions or more but input had shape of Expected Invalid scan input: has length of onnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptrRoundtime_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisbatch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of onnxruntime::ReverseSequenceOp::ReverseSequenceOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK()onnxruntime::ReverseSequenceOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:Unknown tensor type of . Value must be in range [0,Invalid sequence length: RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptravg specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase::RoiAlignBaseSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaleD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthsampling_ratioNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchdetect_negativeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccFailed to obtain detect_negativedetect_positiveIsInfonnxruntime::IsInf::IsInfFailed to obtain detect_positiveIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::Computeprepacked_buffers[0].get() == nullptrT4D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::UseSharedPrePackedBuffersQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)QLinearConv : filter zero point must be constantIsScalarOr1ElementVector(Y_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : filter zero point shape invalidNon per-tensor quantization is not supported now.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_integer.ccIsScalarOr1ElementVector(W_Zero_Point)Must be a scalar or 1D tensor or size 1.ConvIntegeronnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(X_Zero_Point)onnxruntime::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.MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1onnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(a_zero_point)MatmulInteger : B zero point is not validD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1QLinearMatMulIsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.cconnxruntime::QLinearMatMul::Compute*onnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name:en-USD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccConversion ErrorStringNormalizer:Please, install necessary language-pack-XX and configure localesConversion ErrorSingle dimension value must be greater than 0wstr != wconv_errorInput contains invalid utf8 charsInput dimensions are either[C > 0] or [1][C > 0] allowedLOWERattribute case_change_action is not setNONEUPPERattribute is_case_sensitive is not setis_case_sensitivecase_change_actiononnxruntime::StringNormalizer::StringNormalizerDuplicate stopwords not allowed!sw.empty()Stopword contains invalid utf8 charsp.secondlocaleattribute case_change_action has invalid valueEmpty stopwords not allowedstopwordsbad conversion Output dim value: onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeoutput_dims[i] == 0Upsample: input/output value is nullptrResize: input/output value is nullptrDimension: Input dim value: Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Upsample: input/output value's dimension mismatchResize: input/output value's dimension mismatchUpsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensionInvalid roi input index.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.roi_input_idx_ > 0onnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample::ComputeEither scales or sizes MUST be provided as input.sizes == nullptrResize: input tensor's rank does not match the output tensor's rank.sizes != nullptr && sizes->Shape().Size() != 0onnxruntime::Upsample::BaseComputeoutput_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::UpsampleNearestResize: unexpected mode: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.onnxruntime::Upsample::BaseComputeUpsample: unexpected modeUpsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.: '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.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.onnxruntime::Upsample::BaseComputemax_gram_length >= Required min_gram_length must be positive: impl_->min_gram_length_ > 0min_gram_lengthmin_gram_length is requiredmode: impl_->weighting_criteria_ != kNonengram_countsNon-empty ngram_counts is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0max_skip_countmax_skip_count is requiredmin_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_TFIDF is unrecognized, acceptable values are TF,IDF,TFIDFTFonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTfIdfVectorizermode is required-gramsn-gram counts out of bounds for non-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()!pool_strings.empty()pool_int64spool_stringspool_strings must not be empty if specified(items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0.end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole ngram_indexesNon-empty ngram_indexes is requiredmax_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()status.IsOK() && !impl_->ngram_counts_.empty() <= Got weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size() must be of equal size but ngram_indexes size: std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedonnxruntime::ngram_details::PopulateGramsDuplicate ngram detected, size: p.first->second->id_ == 0 id: lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()onnxruntime::Shrink::ShrinkD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()ShrinkEyeLikeEyeLike : Input tensor dimension is not 2onnxruntime::ConstantOfShapeBase >::PrepareComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hUnsupported output datatype with size: Must have a valid input shape.onnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cct_proto_p->dims()[0] == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseMust have a single dimension of 1Must have a single dimensiont_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: !utils::HasExternalData(t_proto)Unsupported value attribute datatype: ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Init but Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::Initialize has batch size of onnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::AllocateOutputTensorssequence_lens length of Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::ValidateInput did not match batch size of onnxruntime::OrtValueTensorSlicer::Iterator::operator *D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_roi_batch_id < batch_sizepooled_shapeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0R->Shape()[1] == 5onnxruntime::RoiPool::ComputeMaxRoiPoolinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0pooled_height_ > 0pooled_width_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2info.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormLpNormalizationp_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormMismatch between input data and B: size of B != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.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::UNDEFINEDInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttr("scale", &scale_).IsOK()scaleD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttr("low", &low_).IsOK()lowonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()highonnxruntime::RandomNormalLike::RandomNormalLikeinfo.GetAttr("mean", &mean_).IsOK()meanCould not infer data type from input tensor with data type D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::RandomNormalLike::ComputeOutput type not supported in this build: Invalid data type of num_samples is < 1num_classes is < 1batch_size is < 1Empty dimensions for input tensoronnxruntime::RandomUniformLike::ComputeONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_strings) must have the same length. However, the number of key is and the number of values is info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_key_field_name, keys).IsOK()values_int64sonnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2num_keys == num_valuesThe attribtues in LabelEncoder (name: Input of tensor(string) must have output of tensor(int64)LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()default_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoderkeys_int64sdefault_floatvalues_floatskeys_strings_Unusedvalues_stringskeys_floatsInput of tensor(int64) must have output of tensor(string)onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.classlabels_stringsclasslabels_int64sZipMapInput features_per_batch[] != number of classlabels[Zipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpSUMSOFTMAX_ZEROSOFTMAXLOGISTICTreeEnsembleRegressorInput shape needs to be at least a single dimension.aggregate_functionbase_valuesn_targetsnodes_falsenodeidspost_transformtarget_idstarget_nodeidstarget_treeidstarget_weightsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuesnodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_featureids.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonn_targets_or_classes > 0- (truenode).Node in tree is already there.target_class_ids.size() == target_class_treeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_values.size()predictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble. (weights). (falsenode).One falsenode is pointing either to itself, either to another tree.Unable to find node onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_TreeEnsembleClassifierclass_treeidsclass_weightsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeX dims is empty.class_idsclass_nodeidsonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScorespredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryPOLYkernel_typeLINEARkernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hSVMRegressorcoefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classnum_features == feature_count_onnxruntime::ml::SVMRegressor::ComputeUnexpected mode:n_supportssupport_vectorsrhoinfo.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMRegressor::SVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccprob_aprob_bonnxruntime::ml::SVMClassifier::SVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0SVMClassifiervectors_per_classD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hcoefficients_.size() > 0onnxruntime::ml::SVMClassifier::ComputeUnsupported data type of scores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of ScaleroffsetEmpty scale in attributesEither both scale and offset can be of feature size (onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()onnxruntime::ml::ScalerOp::ScalerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (Scale size: (scale_.size() == offset_.size()Invalid argument: input has empty dimensions.) or 1OneHotEncoderUnknown Category and zeros = 0.One and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpzeroscats_int64scats_stringsMAXD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hInvalid normalize value of onnxruntime::ml::MakeNormalizenorminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of LinearRegressorinterceptstargetsinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of shape_size == out.length()onnxruntime::ml::CastInputToFloatimputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.Imputerimputed_value_floatsEmpty value of imputed values.Empty input dimensions.inputdimensionsstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer).FeatureVectorizerNumber of inputs () does not match number of inputdimensions values (onnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Invalid input type:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpstring_vocabularyDictVectorizerinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularyD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs("cats_strings", string_categories).IsOK()info.GetAttrs("cats_int64s", int_categories).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hCategoryMappernum_entries == int_categories.size()Input of int64 must have output of string Input of string must have output of int64stof argument out of rangeinvalid stof argument%fUnexpected CAST_TO value of 0.fmax_mapinfo.GetAttr("map_form", &attr).IsOK()max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()CastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0Invalid input type of value: Expected std::map or std::mapInvalid PACK_MAP value of Expected DENSE or SPARSEcast_toonnxruntime::ml::MakePackonnxruntime::ml::CastMap::CastMapinfo.GetAttr("cast_to", &attr).IsOK()map_formD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hTO_STRINGTO_FLOAT Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64onnxruntime::ml::MakeCastInvalid CAST_TO value of SPARSEDENSEonnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccNegative index values are not permitted. First entry in map has index value of Binarizer is NaNthresholdInput data with index: ArrayFeatureExtractorInvalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.] () >=Invalid Y argument: index is out of range: Y[Input initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=reluaffinethresholdedreluleakyreluhardsigmoidscaledtanh}. Actual:Input X must have 3 dimensions only. Actual:Input R must have shape {Input W must have shape {Input sequence_lens must have shape {Input B must have shape {onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU hidden gate activation function: alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fonnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU reset gate activation function: softsigneluExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountweights.quant_para_A + (M * K) <= A_endInput initial_c must have shape {Input P must have shape {onnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < onnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputcandidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: Right shape: Ranks of pair-wise operands must be equal. Right shape: right.Shape().Size() == right_shape_override.Size()left_dim == right_dimEinsum op: Input shapes do not alignleft_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsLeft shape: The override dims are not compatible with given tensor's shape. Left shape override: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessEinsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input . The shape is: is incompatible in the dimension onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleEinsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: in the same dimensionAnother operand has a dim value of Found '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)onnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccRank of the input must match number of subscript labels corresponding to the 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 provideddim_iter == rank...Output subscript contains letters not seen in the inputsFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cc for input shape Cannot parse the diagonal elements along dims onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal 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: onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulData types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;valid did not.'. 0 == forward. 1 == reverse.Invalid values in 'onnxruntime::scan::detail::IterateSequence::::operator ()Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.ccThe subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsdirections.size() == num_entriesonnxruntime::scan::detail::ReadDirections' was Number of entries in ' inputs but Scan was only given onnxruntime::OrtValueTensorSlicer::Iterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocatorExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *iteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::OutputIterator::InitializeFailed to create output tensor for output # is not compatible with Mismatch between expected shape and shape from first outputInverseLast dimension of bias and input does not matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchbeta is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNormepsilon_ >= 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNormLast dimension of gamma and input does not matchgamma is expected to have 1 dimension, got skip is expected to have same shape as inputinput is expected to have 3 dimensions, got onnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::LayerNormop_kernel_info.GetAttr("axis", &axis_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::AffineScaleinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeImageScaleronnxruntime::contrib::ImageScaler::ImageScalerBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceAttribute border needs to be specified with four border elements, got borderInput's height () needs to be greater than or equal to the topBorder () + bottomBorder ( input dimensions instead) + scale_[0] (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::ComputeCrop) + scale_[1] (token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()input_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0ngram_sizescores_dims.size() == 2scores_dims[0] == batch_sizeTidNGramRepeatBlockonnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist::CDistCDistmetricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistInput shape dimensions mismatch:The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hCropAndResizebilinear specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskTransposeMatMulInvalid assumption of output element sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytesMurmurHash3positiveonnxruntime::contrib::MurmurHash3::ComputekeysExpecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesCurrently support only COO and CSR(x64) formatsOuter size must be M + 1 vs inner_B: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccExpecting COO 2-D indices shapeCan not multiply A and B as inner dimension does not match. inner_A: SparseToDenseMatMulonnxruntime::contrib::SparseToDenseMatMul::ComputeCurrently supporting only 2-D matricesonnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO m index: is out of bounds of out_left: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: COO k index: is out of bounds of lhs_right: char_embedding_size attribute: Char embedding size: Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. conv kernal size 1: Conv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: embedding_size attribute: conv filter size: char_embedding_sizeconv_window_sizeembedding_sizeonnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2.WordConvEmbeddingD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccTokenizerMatch contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedtensor(string) expected as inputInput string contains invalid utf8 charsCan not digest tokenexp: Input string contains invalid utf8 chars: mincharnum is too big for char level tokenezation!separators.empty()Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setseparators must not be empty!tokenexp.empty()mincharnum_ > 0attribute mincharnum must have a positive valuetokenexpseparatorsattribute pad_value is not setpad_valueattribute mincharnum is not setmincharnumattribute mark is not setmarkD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizeronnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: found!Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttention mechanism memory shape error! Expected: {}, actural: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpAttnLSTMstatic_cast(activation_func_names.size()) == num_directions_ * 3onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplonnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsExpandDims Axis is Axis must be within range [onnxruntime::contrib::ExpandDims::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hqkv_hidden_sizesinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseonnxruntime::contrib::AttentionBase::GetPresentD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.ccExpect to have present state output when past state input is givenInput '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 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 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'extra_add_qk' is expected to have 4 dimensions, got 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 falseInputs '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 'past' dimension 2 shall have length of Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2qkv_sizes doesn't match the wights dimensionInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes first element should be same as the secondhidden_size should be divisiable by num_heads:hidden_size should be divisiable by num_heads.qkv_hidden_sizes attribute should have 3 elementsInput 'bias' is expected to have 1 dimension, got Input 1 dimension 1 should be 3 times of hidden dimensionInput 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Attention cannot have past sequence and extra add qkInput 'input' is expected to have 3 dimensions, got onnxruntime::contrib::Attention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttention4D mask in attention cpu kernel is not supportedGridSamplegrid_dims[3] == 2, expect 2Last dimension of grid: grid_dims[0] == Nonnxruntime::contrib::GridSample::Compute does not match input batch size Grid batch size padding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"Only 4-D tensor is supported" not supported, expect zeros, border or reflectionpadding_mode "D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\grid_sample.ccreflectionmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"onnxruntime::contrib::GridSample::GridSample" not supported, expect bilinear, nearest or bicubicmode "padding_modebicubicSampleOpQGemm : scale of y must be null or a scalar or 1D tensor of size 1y_scale == nullptr || IsScalarOr1ElementVector(y_scale)QGemm : zero point of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)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 : 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 of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QGemm : zero point of input a must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zp)QGemm : scale of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsonnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.hTYQGemmTCTYZTATBQEmbedLayerNormalizationGamma zero point must be a scalar or 1D tensor of size 1Beta 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 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point 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 1Word embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QEmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\qembed_layer_norm.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::ComputeDynamicQuantizeLSTMRecurrentWeight point must be constantDynamicQuantizeLSTM : Weight zero point must be zeroR_scaleW_scaleR_zero_pointW_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePackonnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommononnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(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.QAttentioninput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.cconnxruntime::contrib::QAttention::ComputeMatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)QLinear Pooling unsupported pooling size!Unsupported 'dtype' in QLinear Pooling:tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)input y_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_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_pool.cconnxruntime::contrib::QLinearAveragePool::ComputeIsScalarOr1ElementVector(tensor_x_scale)Input x_scale must be a scalar or 1D tensor of size 1Input scale is not float for input def @D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcatinput_def_count >= 8 && (input_def_count - 2) % 3 == 0At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!Wrong input type encountered for zero point of quantized input @Input scale is not float for quantized input @onnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 6 && input_count_x3 % 3 == 0tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @tensor_x_scale->IsDataType()onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)MatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countchannels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchonnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::ComputeInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 [seqno= row[.. rows[not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemoryunimplemented activation: activation_params count mismatchInput 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapeword_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1gamma is expected to have size of gamma is expected to have 1 dimensions, got beta is expected to have size of beta is expected to have 1 dimensions, got QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQlinearBuildLookupTable : 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 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1Received invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionsdims[d_i] < d_maxD:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPositionTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType. is not supportedCudaPinnedCudaD:\a\_work\1\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Specified device is not supported.Not implementedArgument is not a tensorthe ort_value must contain a constructed tensor or sparse tensorD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShape`anonymous-namespace'::GetIndicesTensorUnsupported indices_format passedtype_proto is not of type sequence!type_proto is not of type map!D:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0p_type != nullptronnxruntime::Tensor::Tensoronnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Inittensor failed memory size calculationtensor size overflowTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensoronnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())We do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())onnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.h expected to be a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatible(null)floatonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerint8uint8booldoubleint32uint32int16uint16float16bfloat16int64uint64 is not currently registered or supportedMLDataType for: tensor type sparse tensor type Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckeronnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Set dst_size: Must have the same size. Got src_size: Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZerothe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValueonnxruntime::`anonymous-namespace'::CopyDataD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.ccValues size onnxruntime::SparseTensor::AllocateBufferthis tensor already has populated sparse_indices must be less than total buffer size: Use MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataThis method does not expect allocator to be setonnxruntime::SparseTensor::UseCsrIndicesFormat() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::AsBlockSparseonnxruntime::SparseTensor::MakeCsrStringsMust contain BlockSparse format. Got: onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Expecting one index. Got: Expecting to have at lest 3-D shape. Got:Expecting index blocks: Expecting fully sparse tensors to have value shape {0}Indices shape must have dim[0] == 2 to be equal to values blocks: Use MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataExpecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::UseBlockSparseIndicesUnable to find a data transfer for copying from device type: onnxruntime::SparseTensor::Copyonnxruntime::SparseTensor::MakeBlockSparseStrings to device type: Destination must have a CPU allocator setX-device copy of strings not supportedThis instance should not be emptyDestination should be emptySrc and Dst must be of the same typeMust have the same shapeFormat() == SparseFormat::kCooonnxruntime::SparseTensor::AsCooSparseTensor Allocation failed for size: Must contain Coo format. Got: must be equal to or twice the values size: Index size: Expecting to contain one index, got: format_data_.size() == 1USparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesvalues_count == index_sizeonnxruntime::SparseTensor::GetCooIndexDimsonnxruntime::SparseTensor::MakeCooDataExpecting data type to be set as stringNot expecting an allocator setUse MakeCooStringsThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptronnxruntime::SparseTensor::MakeCooStringsFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::AsCsrExpecting two indices. Got: Must contain Csr format. Contains: Format() == SparseFormat::kCsrconnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zeroformat_data_.size() == 2Udense shape must 2-D. Got: rows: Outer index count must be rows + 1 or zero. Got: the same as values size: Expecting inner index size: dimensions. for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dimsdimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Sliceonnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.{}Config key is empty or longer than maximum length 128Config value is longer than maximum length 1024Config with key [onnxruntime::ConfigOptions::AddConfigEntry]. It will be overwritten] already exists with value [D:\a\_work\1\s\onnxruntime\core\framework\config_options.ccReceived nullptr for name.Received nullptr for OrtValue.An OrtValue for this name has already been added.Received OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.InternalTestingExecutionProviderUnsupported OrtValue type.Failed to find allocator for device invalid allocator.D:\a\_work\1\s\onnxruntime\core\framework\utils.ccUnsupported OrtValue type to copy between device.allocator != nullptronnxruntime::utils::BatchOrCopyMLValueonnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feeds expected to be of type: but is of type: No attribute with name: is defined.Attribute: No attribute with this name is defined.Attibute name and type don't matchRequested attribute: is expected to have type: D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Invalid arg_num of . Num args is onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundonnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was nullD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cconnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0 node_version: kernel start version: kernel_end_version: This op has been implemented only for the following types (Op with name ( and type ( Version mismatch.Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible.onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::KernelRegistry::TryCreateKernel but the node in the model has the following type (),kernel def can't be NULLKernel not found kernel is not supported in Encountered following errors: (onnxruntime::KernelRegistry::Register: Conflict with existing kernel def hash.Failed to add kernel for : Conflicting with a registered kernel with op versions. because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeonnxruntime::GetCpuPreferredNodeskernel_info != nullptrORT optimization- Force fallback to CPU execution for node: D:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cconnxruntime::GetCpuPreferredNodes::::operator ()Candidate for fallback CPU execution: cannot find allocatorD:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfo type: `anonymous-namespace'::ReadExternalDataForTensorTensorProto external data size mismatch. Computed size: External data type cannot be UNDEFINED or STRING.D:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc) in protoonnxruntime::utils::UnpackTensorWithExternalDataImplnullptr == p_datadata overflowUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size() does not match the data size(TensorProto type can not be writen into Tensor type TensorProtoToTensor() tensor shape mismatch!onnxruntime::utils::GetFileContentInitialized tensor with unexpected type: tensor can't contain negative dimsstring tensor can not have raw dataonnxruntime::utils::TensorProtoToTensorThe preallocated buffer is too small. Requires , Got string tensor can not use pre-allocated bufferTensorProtoToMLValue() must take a pre-allocated MemBuffer!Sparse Indices raw data size does not match expected.Unsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorSparse indices int32 data size does not match expectedSparse indices int64 data size does not match expectedonnxruntime::utils::CopySparseData`anonymous-namespace'::GetExternalDataInfoTensor does not have external data to read from.UnpackTensor: the pre-allocated size does not match the raw data size, expected cur_index == &*indices_data.cend()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorElement_size of: is not supported.onnxruntime::utils::SparseTensorProtoToDenseTensorProtoInvalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)Must have a valid data typeUnsupported sparse tensor data type of Unsupported type: onnxruntime::utils::UnpackInitializerData data_type: Invalid TensorProtoonnxruntime::CreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.ccduplicated allocatoronnxruntime::AllocatorManager::InsertAllocatorReceived invalid value of arena_extend_strategy onnxruntime::IExecutionProvider::GenerateMetaDefIdonnxruntime::IExecutionProvider::InsertAllocatorIExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_D:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccduplicated allocator: onnxruntime::IExecutionProvider::TryInsertAllocatorIExecutionProvider::Compile with fused Node is not implemented by onnxruntime::sparse_utils::DenseTensorToSparseCsrD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccUnsupported element size: Support 2-D matrices onlyCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::SparseCsrToDenseTensorOuter indices must be M + 1. Got: outer_num == (rows + 1)Input must be of COO formatInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUExpecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::DenseTensorToSparseCooExpecting indices to be equal the number of values or be twice as manyonnxruntime::sparse_utils::SparseCooToDenseTensor > dense_size: Invalid index: data_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsCould not find OrtValue with idx 'onnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be empty doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node +allocator_for_caching.get() != nullptrUnable to write the provided PrePackedWeights instance into the container) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:!op_type.empty() which is of op type: used in the node: Using cached version of pre-packed weight for constant initializer: for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMappingonnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::SaveToOrtFormatMain Graph instance should have populated all subgraphs when being resolved.onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::GetNodeIndexInfosession.disable_prepackingonnxruntime::SessionState::SetupAllocatorsD:\a\_work\1\s\onnxruntime\core\framework\session_state.ccSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfo. Ignoring allocator from Allocator already registered for . Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:onnxruntime::KernelUseSharedPrePackedBuffersDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfo. Invalid ORT format model.Can't find node with index Unable to find compiled kernel hash for node 'onnxruntime::SessionState::FinalizeSessionStatesubgraphonnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::LoadFromOrtFormat::::operator ()onnxruntime::SessionState::LoadFromOrtFormatonnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateImpl Attribute: Index:onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulatorsubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()' OpType:Missing session state for subgraph. Node:'entry != node_to_subgraph_ss.second.cend()p_op_kernelKernel create info node indices are null. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. is missing. Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Kernel create info is null. Invalid ORT format model.Subgraph SessionState entry for is null. Invalid ORT format model.Subgraph SessionState for in KernelRegistryManagerfound duplicated provider ). (node The node is not placed on any Execution Provider. Failed to find kernel for nullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccError mapping feeds: Error mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cc1 == capability.nodes.size()compute_info_->create_state_func(&context, &func_state_) == 0D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PlaceNodeD:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernelonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::InlineNodes has Compile error: onnxruntime::PartitionOrtFormatModelImplMust use Function based fusion when exporting compiled nodes to dll.onnxruntime::PartitionOnnxFormatModelImpl did not return correct number of compiled functionsfusion_style == IExecutionProvider::FusionStyle::FunctionNo provider specified.onnxruntime::GraphPartitioner::PartitionOrtFormatModelCompiled kernel hashes must be providedonnxruntime::GraphPartitioner::Partitionsingle_node_compute_func should have 1 elementsEP: Existing entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model.compiled_kernel_hashes != nullptrp_int >= base_intD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.hp_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::ReserveReserving memory in BFCArena for h != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()Allocated memory at size: (requested) num_bytes: (actual) rounded_bytes:Extending BFCArena for . bin_num:onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytes!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtr The total allocated bytes is now onnxruntime::BFCArena::SplitChunkFailed to find a free memory block despite calling Extend. rounded_bytes=!chunk->in_use()onnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1 BFC Arena shrunk by bytes. onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::Shrink arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionFor initial_growth_chunk_size_bytes: memory limit: entry != regions_.end()Could not find Region for: Could not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion bins of max chunk size D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.ccBinForSize(bin_size) == BinFromIndex(b)Creating with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: onnxruntime::BFCArena::BFCArenaCreating BFCArena for cudaMalloconnxruntime::BFCArena::ChunkFromHandleIncorrect arena extend strategy.hipMallocBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)h < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)Extended allocation by bytes.Total allocated bytes: onnxruntime::BFCArena::Extend is smaller than requested bytes of onnxruntime::BFCArena::Extend::::operator ()Failed to allocate memory for requested buffer of size Available memory of c->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::FreeAndMaybeCoalesceonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBin. Unloading DSO D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoaderonnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: has already been loaded.Caught exception while loading custom ops with message: onnxruntime::ExLibLoader::LoadExternalLibA dso with name onnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_OrtValue shape verification failed. Current shape: Requested shape:onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame entries which doesn't match the number of fetches the frame was initialized with of fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() failed. Error:Fetches vector passed to GetOutputs contains invalid index shape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()Trying to allocate memory for unused optional inputs/outputs returned nullptrTensor shape cannot contain any negative valueonnxruntime::ExecutionFrame::{ctor}::::operator ()Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end()For ort_value with index: , block in memory pattern size is: 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.onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperort_value.Fence() == nullptr but the actually size is: , fall back to default allocation behaviorort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanInvalid allocation kind: Allocation of tensor types requires a shape.mlvalue.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::AllocateSparseTensorort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImpl failed: onnxruntime::ExecutionFrame::GetAllocationPlan does not match actual shape of for output onnxruntime::ExecutionFrame::VerifyOutputSizesExpected shape from model of Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=TraceAllocation for ort_value_idx= size=onnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::TraceAllocatemodel format error! Need a key for the external data infolocationmodel format error! Need a value for the external data infomodel format error! Missing 'location'model format error!parsing failedchecksumlengthonnxruntime::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 onnxruntime::OrtValueTensorSlicer::Createort_value.IsTensor()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on Invalid dim0_offset of . Dimension 0 is OrtValue has not been allocated so can't be sliced.D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cc. Shape:ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Create does not.onnxruntime::ParallelExecutor::ExecuteMultiple errors were found.ParallelExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.ccentry != nullptrAll implicit inputs should have OrtValue instances by now. D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal_fence_beforeop_name node. Name:'' Status Message: onnxruntime::ParallelExecutor::RunNodeAsyncBegin executionGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.Unknown exception was caught by catch-all handler._fence_afterException running nodes starting at '. providerNon-zero status code returned while running _kernel_timethread_scheduling_statsD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::ExecuteSequentialExecutor::Executeoutput_sizeonnxruntime::ReleaseNodeMLValuesexec_plan_indexgraph_indexparameter_sizeactivation_sizeD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.ccCan't use func with null ptrfunc info for node: onnxruntime::FuncManager::GetFuncs not found. already exist.source and destination buffer size mismatchonnxruntime::utils::detail::CopyLittleEndianD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.cc) : onnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()entry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccreused != reused_foronnxruntime::PlannerImpl::Reuseid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefn >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::BufferShould not have entry in kernel create info with nullptr for kernel_defp_kernel_defonnxruntime::PlannerImpl::ComputeUseCounts::::operator ()Can not find the node There is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_mapnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizestarts_.size() == ends_.size()onnxruntime::AllocPlanPerValue::ProgramCounter::AddStartPrevious entry was not terminated.Invalid 'end'. Value is larger than 'start'.end >= starts_.back()starts_.size() == ends_.size() + 1onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndstarts_.empty() || start > ends_.back()No matching 'start' entry.D:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.hInvalid 'start'. Value is smaller than previous 'end'.onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::ComputeReusePlanspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()allocatoronnxruntime::PlannerImpl::GetLocationForNodeInputonnxruntime::PlannerImpl::ComputeUseCountsCan not find the execution provider onnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::VerifyMemoryTimeScheduleAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counterInvalid program_counter entries at index entry.program_counter.HasValidEntries()Only tensors are supported for external outputs for now.!IsNonTensor(*node_output)onnxruntime::PlannerImpl::CreatePlanD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()onnxruntime::PrePackedWeights::GetHashSubgraphGraphDeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires Failed memory size calculationsession.use_device_allocator_for_initializersCannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator () ) is different from what is supplied () because the ORT planned memory location device string tensor is not supported for copying between allocatorsFailed to copy tensor to onnxruntime::session_state_utils::DeserializeTensorProtoD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cc[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name (entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for OrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensors is not used by any node. input with name onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingDone saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator () failed.Deserialize tensor onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: !using_counters_current <= buffer_size_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.h Dimension=Can't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source=' source:Error merging shape info for output. '. Falling back to lenient merge. target:Output:Source and target must both be either tensors or sparse tensorsSparseTensor element type mismatch. ] op_type [Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShape Input=Type mismatch. Current=onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccnode_arg_name cannot be null], could not find NodeArg fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()does not have the graph for key onnxruntime::Node::SaveToOrtFormatNode [Serialization of fused function body is not currently supported, D:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hTypeProto must have shape for this to runonnxruntime::utils::GetShapeNoOpConstantonnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name () does not exist in the graph.This is an invalid model. Graph output (Graph ctor should have created NodeArg for initializer. Missing:node_arggraph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoInvalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous node. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=Argument mismatch when removing edge.onnxruntime::Graph::BuildConnectionsInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Graph attribute inferencing failed: in node onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.Some nodes are not included in the topological sort, graph have a cycle.Invalid model. Node input 'onnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:Node::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for fbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceLoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatgraph_proto != nullptronnxruntime::Graph::GraphThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullinput edgesoutput edgesinput index: onnxruntime::Node::LoadEdgesFromOrtFormat' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'Duplicate sparse_tensor_initializer: '. Please, fix your model.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)' Model is invalid.Duplicate constant node sparse initializer name: 'Initializer Graph state to be loaded into must be empty.This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.Type Error: Shape of initializer This is an invalid model. Error in Node:Type Error: Data in initializer ' does not match. but usage of initializer in graph expects ' has element type ) does not have type information.This is an invalid model. Model input () attribute (Fatal error: ) is required but not specified.onnxruntime::Graph::VerifyNodeAndOpMatch is not a registered function/oponnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitFunctionBodyForNodeError: Duplicate definition-site for (' optype Function body initialization failed for node '. Execution will fail if ORT does not have a specialized kernel for this op. Error message existing->second == &tensoronnxruntime::Graph::AddInitializedTensor but different TensorProto.AddInitializedTensor already has tensor with name Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::ResolveSubgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesSize mismatch validating subgraph inputs. Got Node: inputs and requires inputs but subgraph has No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs.) input arg (This is an invalid model. Node (Node () does not have type information set by parent node.onnxruntime::Graph::UpdateShapeInference) Op (UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()) bound to different types () of Optype (This is an invalid model. Type Error: Type ' in node () of operator (' of input parameter () is invalid.) in node () of output arg (Type Error: Type () does not match expected type () of node () type inference failed) output arg (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatchdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::CreateFusedSubGraphNodeonnxruntime::Graph::FinalizeFuseSubGraphonnxruntime::Graph::AllocateNodenullptr != func_meta_def must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::SetInputsInput to set must exist.input->Exists()onnxruntime::Graph::InlineFunctionDuplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.!foundonnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.sparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0_token_onnxruntime::Graph::SaveToOrtFormatonnxruntime::Graph::ToGraphProtoFailed to convert dense initializer to sparsenode->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode as it still has output edges.Can't remove node onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node.'. It is not used by any node and should be removed from the model.Removing initializer 'Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'was added but does not exist. index < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.hp`, but it its version is higherthan the operator set version , but it its domain is notknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registrynodeargMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GetRootNodesfilter_info_ == nullptrNot supported with filtered graph.onnxruntime::GraphViewer::GetNodesInTopologicalOrderInvalid ExecutionOrdernodeMismatch between Graph and IndexedSubGraph. Node not found: Mismatch between Graph and IndexedSubGraph. Output not found:onnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatProtobuf serialization failed.onnxruntime::Model::Save is less than 0.Graph is null. Invalid ORT format model. is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset onnxruntime::Model::ModelModelProto does not have a graph. is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain onnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Unsupported model IR version: , max supported IR version: Missing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.D:\a\_work\1\s\onnxruntime\core\graph\model.cc less than 0.Protobuf parsing failed.onnxruntime::Model::LoadNo graph was found in the protobuf.Failed to load model with error: onnxruntime::SaveModelsystem error number Load model failed. File doesn't existCAttribute expected to have tensor or sparse tensor typeoptional(tensor(int32))optional(tensor(int16))optional(tensor(int8))optional(tensor(uint64))optional(tensor(uint32))optional(tensor(uint16))optional(tensor(uint8))optional(seq(tensor(complex128)))optional(tensor(complex128))optional(tensor(complex64))optional(tensor(bool))optional(tensor(string))optional(tensor(double))optional(tensor(float))optional(tensor(float16))optional(tensor(int64))Attribute expected to have tensor or sparse tensor type: is null expected to have rank but has rank Incompatible dimensionsNegative values are not allowed in a shape specification should specify a shape expected to have tensor type expected to have tensor or sparse typeoptional(seq(tensor(int16)))optional(seq(tensor(int8)))optional(seq(tensor(uint64)))optional(seq(tensor(uint32)))optional(seq(tensor(uint16)))optional(seq(tensor(uint8))) expected to have rank >Dimension mismatch in unification between optional(seq(tensor(complex64)))optional(seq(tensor(bool)))optional(seq(tensor(string)))optional(seq(tensor(double)))optional(seq(tensor(float)))optional(seq(tensor(float16)))optional(seq(tensor(int64)))optional(seq(tensor(int32)))Inputs 4 shall be 5 dimensionsqkv_hidden_sizes should have 3 elementsInvalid bias shapeInputs 0 shall be 3 dimensionsIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.baX_biasWhether every token can only attend to previous tokens. Default value is 0.Number of attention headsD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccT7T6T5oneMweightHidden layer sizes of Q, K, V paths in Attentiongamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.word_embedding should have 2 dimensions and dimension size is known.segment_ids input shall be 2 dimensionsinput_ids shall be 2 dimensionsPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64segment_idsinput_idsThe epsilon value to use to avoid division by zero.Constrain to integer typesglobalsegment_embeddingposition_embeddingword_embeddingbeta_quantgamma_quantposition_embedding_quantword_embedding_quantConstrain input and output float tensors types.Constrain input and output integer tensors typesgamma_scalesegment_embedding_scaleposition_embedding_scaleword_embedding_scaleConstrain mask index to integer typesConstrain input and output types to float tensors.presentextra_addpastweight_scaleinput_scalewindowOne sided attention windows length W, or half of total window lengthLongformerAttentionConstrain input and output types to int8 tensors.weight_zero_pointinput_zero_pointGglobal_biasglobal_weightmask Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. Constrain scores input and output types to float tensors.Constrain indices to integer typesscores_outscores Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. extra_shapeinput_as_shape Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided.Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. ) needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. Duplicate of FusedMatMul. Going forward FusedMatMul should be used. This OP will be supported for backward compatibility. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html inputs are expected to have tensor type and output type should not be null. Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. Let's assume "separators" is [" "] and consider an example. If input is ["Hello World", "I love computer science !"] whose shape is [2], then the output would be [["Hello", "World", padvalue, padvalue, padvalue], ["I", "love", "computer", "science", "!"]] whose shape is [2, 5] because you can find at most 5 tokens per input string. Note that the input at most can have two axes, so 3-D and higher dimension are not supported. If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings will be broken part into individual characters. For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). This operator searches for the first match starting from the beginning of the considered string, and then launches another search starting from the first remained character after the first matched token. If no match found, this operator will remove the first character from the remained string and do another search. This procedure will be repeated until reaching the end of the considered string. Let's consider another example to illustrate the effect of setting "mark" to true. If input is ["Hello", "World"], then the corresponding output would be [0x02, "Hello", "World", 0x03]. This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. If tokenizer removes the entire content of [C]-input, it will produce [[]]. I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. If the tokenizer receives empty input of [0] then the output is [0] if empty input of [N, 0] then [N, 0]. Input axis is invalid: Second input does not have rank 2First input does not have rank 2) + scale[1] () + scale[0] (segment_embedding_zero_pointposition_embedding_zero_pointword_embedding_zero_pointbeta_scaleConstrain input and output types to float32 tensors.mask_index_outlayernorm_outbeta_zero_pointgamma_zero_pointskipConstrain input and output types to float or half tensors.output tensorinput tensorThe NGram size.Constrain mean and inv_std_var to float tensors.inv_std_varSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationERFCXPlus1ERFCXOneHalfGaussian Error Linear Unit. A high-performing neural network activation function.The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.InvStdDevInvStdDev2Disinf_only Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal. Based on Torch operator Embedding, creates a lookup table of embedding vectors of fixed size, for a dictionary of fixed size. The inner-most 2 dimensions must have the same size (mat_w: != mat_h:Input rank must be >= 2.Bias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. PhiXAttribute 'type' should be a TypeProto and it should specify a type.typeOptional is expected to have an output. Construct an optional type containing either an empty optional of a certain type specified by the attribute, " "or an optional type containing the 'input' element." onnxruntime::contrib::RegisterContribSchemas::::operator ()!(isinf_only && isnan_only)Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.isnan_onlyInput type is null. Input must have Type information.OptionalGetElement must have an input element. Outputs the element in the optional-type input'. It is an error if the input value does not have an element " "and the behavior is undefined in this case." OptionalHasElement is expected to have 1 output.OptionalHasElement is expected to have 1 input. Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns false. Optional is expected to have either an input or the type attribute set.Input type is null. Type information is expected for the input.crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionfirst input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html NumReducedAxesPrefixShapeAxis1DZero1DRankXShapeEpsilonstash_typeSquareOfMeanMeanOfSquareSquareMean2DXUX2DReducedShapeSuffixShapeBiasedScaledNormalizedTNormalizedDeviationStdDevVarPlusEpsilonVarThe new GRU hidden state calculated by this op.The timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevThe previous GRU hidden state. Sample echo operator.Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.The scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.hiddenComplexMulIrfftonesidednormalizedsignal_ndimRfftConstrain input0 and output types to float tensorsFor internal use. The fused convolution operator schema is the same as Conv besides it includes an attribute activation.Constrain input and output types to float tensorsPadsWComplexMulConjinput_1Ainput_0The scale to apply.Bias applied to each channel, same size as C.1D output tensor1D input tensorValue of betaValue of alpha Given an `input` and a flow-field `grid`, computes the `output` using `input` values and pixel locations from `grid`. Currently, only spatial (4-D) inputs are supported. For `input` with shape (N, C, H, W) and `grid` with shape (N, H_out, W_out, 2), the `output` will have shape (N, C, H_out, W_out). For each output location `output[n, :, h, w]`, the size-2 vector `grid[n, h, w]` specifies `input` pixel locations `x` and `y`, which are used to interpolate the output value `output[n, :, h, w]`. The GridSample operator is often used in doing grid generator and sampler in the [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). See also in [torch.nn.functional.grid_sample](https://pytorch.org/docs/master/generated/torch.nn.functional.grid_sample.html#torch-nn-functional-grid-sample). Input must be an optional-type value containing an element with type information.Output tensorInput tensorThreshold valueResult, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Result, has same shape and type as inputInput tensor of shape [N,C,H,W]GivenTensorFillConstrain input and output types to all tensor types.Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`dataTensor of data to extract slices from.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitOutput data after scalingInput data to be scaledvaluesThe filled tensorThe shape of filled tensorsparse_tensor(uint64)sparse_tensor(int32)sparse_tensor(int64)sparse_tensor(double)sparse_tensor(float)N-dimensional dense matrix B2-dimensional sparse matrix A. Either COO or CSR formatMatrix multiply resultsIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.sparse_tensor(uint32)Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]] Constrain indice type to int32 or int64Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].indicesTensor of rank q >= 1.Tensor of rank r >= 1.Constrain to tensor(int32).Specify embedding vector of charSpecify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Whether A should be transposedConstrain input and output types to float/int tensors.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.ZTokenized stringsStrings to tokenizeExpandDims echo operator.Specified axis to insert a dimensionactivation_gammaScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedN-dimensional matrix A Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationScalar multiplier for the product of the input tensors.Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BN-dimensional matrix BBias tensor.Scale tensor.Input data tensor from the previous layer.type used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.Constrain types to int tensors.Constrain types to float tensors.The output.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.Type of Mean and InvStdDev tensors.Constrain input types and output Y type to float tensors.Saved inverse standard deviation used during training to speed up gradient computation.Output data tensor.A 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.Long tensor containing the indices to extract from embedding matrix.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeTorchEmbeddingOutput tensor of the same type and shape as the input tensor.Input tensor. Every matrix in the batch must be invertible.The bias input data that is a 1D tensor.The normal input data.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Input tensor of rank 2 or higher.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Constrain input and output types to all numeric tensors.Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.scale_grad_by_freqA 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``padding_idx Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Tensor after padding.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Input tensor.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayThe WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Input can be of any tensor type.countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'xA 1-D input tensor that is to be processed.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". Finds all the unique values (deduped list) present in the given input tensor. This operator returns 3 outputs. The first output tensor 'uniques' contains all of the unique elements of the input, sorted in the same order that they occur in the input. The second output tensor 'idx' is the same size as the input and it contains the index of each value of the input in 'uniques'. The third output tensor 'counts' contains the count of each element of 'uniques' in the input. Example: input_x = [2, 1, 1, 3, 4, 3] output_uniques = [2, 1, 3, 4] output_idx = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.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.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.Three interpolation modes: bilinear (default), nearest and bicubic.Constrain output type to all tensor or sequence types.Output element in the optional input.OptionalGetElementConstrains output to a boolean tensor.Constrains input type to optional tensor and optional sequence types.Constrain output types to float tensors.Constrain input types to all tensor types.4-D tensor of shape (N, C, H_out, W_out).GridInput offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.The bias input, a vector with the same shape as last dim of data(Optional) Seed to the random generator, if not specified we will auto generate one.output, dropout_mask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.The bias (or mask) as Tensor.broadcast bias across input for dimensions broadcast_axis to softmax_axis-1apply softmax to elements for dimensions softmax_axis or higherY = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.Constrain input and output types to all numeric tensors and bool tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.The output mask of dropout.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.ratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of input, which is typically the case during training.residualThe residual input, must have the same shape as dataThe input element.OptionalThe output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Input tensors to check.Constrain the output to a boolean tensor.If true, check only for NaN.If true, check only for Inf, -Inf.IsAllFiniteA scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.The optional input.OptionalHasElementConstrains output type to all optional tensor or optional sequence types.Constrains input type to all tensor and sequence types.The optional output enclosing the input element.OType of the element in the optional output̼+̼+itr != node_args.end()Attempting to get index by a name which does not exist:for node: onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetIndexFromNameFailed since multiple edges matched:onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index:onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::FindPath cannot be safely updated to in one of the subgraphs.D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0INTSFLOATSGRAPHSTRINGINTFLOATUNDEFINEDTENSORonnxruntime::experimental::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::experimental::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dims for sparse initializer: Missing indicies for sparse initializer: Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelSPARSE_TENSORSSPARSE_TENSORGRAPHSTENSORSSTRINGSonnxruntime::experimental::utils::SaveAttributeOrtFormatonnxruntime::experimental::utils::SaveSparseInitializerOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveInitializerOrtFormat8a0a,a$aDaaa aЯaȯaaaa does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.onnxruntime::ViewerFunctionImpl::MutableBodyD:\a\_work\1\s\onnxruntime\core/graph/function_impl.honnxruntime::ViewerFunctionImpl::Bodydomain_version != -1No opset registered for domain in function opset imports.Cannot infer type and shape for function. No opset import for domain referenced by function body node optype '. Error message ToutoutTinD:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::IOTypeConstraintHelperonnxruntime::FunctionImpl::FunctionImplfused_function_subgraphonnxruntime::CreateSchemainput_arg->Type() != nullptronnxruntime::UpdateSubgraphsWithinFunctionBodyA node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::InitNestedModelLocalFunctionFunction body initialization failed for Function 'Not supportednode_in_parent_graph->OutputDefs().size() == function_body_graph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.node_in_parent_graph->InputDefs().size() == function_body_graph.GetInputsIncludingInitializers().size()'s number of inputs is different from function body graph's number of input.Resolve subgraph failed:_dummyInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarA_zero_pointB_scaleB_zero_pointC_scaleinputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}A_scaleConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.b_scaleb_zero_pointConstrain input A, b_scale and output Y data type as float tensor.reducedConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.MulIntegeradditionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationQLinearReduceMeandata_scaleConstrain input B data type to 8-bit integer tensor.a_scalea_zero_pointConstrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointaxis must be in [-rank, rank)All inputs to Concat must have same rankD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccThe 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.y_scaleC_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.axis must be in [-rank, rank-1]. input rank was Required attribute axis is missingx_zero_pointConstrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.ReduceSumIntegery_zero_pointConstrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.x_scaleY_hY_cConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Which axis to concat oninitial_cPSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Constrain scale types to float tensors.T8TVinputsConstrain scale types to any float tensor type.X_zero_pointY_scaleY_zero_pointConstrain input and output types to 8 bit tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.data_zero_pointreduced_scalereduced_zero_pointCoefficient of leakage.X_scaleRsequence_lensinitial_hNumber 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.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.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.Constrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Can not get shape initializer data!Unsupported type:Constrain input and output types.limitdeltaUnsupported non-raw-data data type!Constrain input and output types to singed/unsigned int8 tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccww_scalew_zero_pointStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Input tensor must have at least 2 dimensionsOutput tensor must have at least 2 dimensionsinput and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.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.tensor rank too smallinvalid scales dimensioninvalid scales valueD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.AWQWMWmemory_seq_lensrB¥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?@@@ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!??    [ONNXRuntimeError]FAILSUCCESSGENERAL ERROREP_FAILonnxruntime::common::Status::Statuscode != static_cast(common::OK)SystemErrorD:\a\_work\1\s\onnxruntime\core\common\status.ccNO_MODELNO_SUCHFILERUNTIME_EXCEPTIONENGINE_ERRORMODEL_LOADEDINVALID_PROTOBUFINVALID_GRAPHNOT_IMPLEMENTEDD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc/\SetThreadDescriptionkernel32.dll[%hs] [%hs(%hs)] kernelbase.dll RaiseFailFastException%wsInvalid fd was supplied: Received negative size from stat callonnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferfile_path == nullptrlength > buffer.size()offset < 0Fatal error: 0 count processors from GetSystemInfoonnxruntimeFatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCores fail, errcode = - GetFileSizeEx open file genericsystem%hs(%u)\%hs!%p: FailFast(caller: %p) %hs!%p: %hs(%d) tid(%x) %08X %wsCallContext:[%hs] Msg:[%ws] ExceptionLogHrReturnHr\*D:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDirGetFinalPathNameByHandle() failed: RemoveDirectory() failed - path: UNC\onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPath" when trying to load ""LoadLibrary failed with error "ReadFile SetFilePointerEx MapFileIntoMemory is not implemented on Windows. fail: unexpected end, error code: \/onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: Failed to find symbol in library, error code: FreeLibrary failed with error onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameunknown erroronnxruntimeISink must be provided.D:\a\_work\1\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::Defaultonnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. Failed to parse path root: D:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRootonnxruntime::Path::Parse/\unnamed_thread_poolProfiler not started yetD:\a\_work\1\s\onnxruntime\core\common\threadpool.cc}}enabled_onnxruntime::concurrency::ThreadPoolProfiler::StopD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionn <= num_threads_+1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelpoints_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart], "core": ": ", "block_size": ["thread_id": ""thread_pool_name": "{"main_thread": {}, "sub_threads": {", LogStart must pair with LogEnd!points_.empty()!current_parallel_sectiononnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection": {Nested parallelism not supportedonnxruntime::concurrency::ThreadPool::ParallelForMore work items than threadsn >= 0RunWaitDistributionDistributionEnqueue"core": "num_run": WaitRevokeUnknownEventD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideStringlength overflowonnxruntime::ToMBString"tid" :"dur" :{"cat" : ""pid" :"name" :""args" : {"ts" :"ph" : "X",Maximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEvent[ ",Writing profiler data to file onnxruntime::profiling::Profiler::EndProfiling} }, " : " : "] onnxruntime::profiling::Profiler::StartTimeonnxruntime::profiling::Profiler::InitializeD:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptrNodeSessionaaunexpected failureillegal input path:api-ms-win-eventing-provider-l1-1-0.dlladvapi32.dllEventSetInformation is not a valid daypm is not a valid year is not a valid dateam [kernel32.dllGetSystemTimePreciseAsFileTime _Pa`a@amap_typetensor_typesequence_typedim_param value with no name. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.Null entry in dimensions. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorShapeOrtFormatonnxruntime::experimental::utils::SaveMapTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::experimental::utils::SaveSequenceTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.ccWe do not support type [onnxruntime::experimental::utils::SaveValueInfoOrtFormatonnxruntime::experimental::utils::SaveTypeInfoOrtFormat] for nowonnxruntime::experimental::utils::LoadValueInfoOrtFormatModel must have opset imports. Invalid ORT format model.Type:Null type info for opset import domain is null. Invalid ORT format model.onnxruntime::experimental::utils::LoadOpsetImportOrtFormatopset id is null. Invalid ORT format model.onnxruntime::experimental::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Null map type info. Invalid ORT format model. is not supported currentlyonnxruntime::experimental::utils::LoadTypeInfoOrtFormatNull sequence type info. Invalid ORT format model.) has input size not in allowed input sizes., max= not in range [min=has output size ) in op definition.) has output size not in allowed output sizes., has unsupported type: typestr: ' has been deprecated since version Operator ' has inconsistent type has unsupported type Mismatched attribute type in '' is expected to have field 't' for operator Unrecognized attribute: ' is expected to have field 'type_proto'' is expected to have field 'floats'' is expected to have field 'sparse_tensor'' is expected to have field 'g' is marked single but has an empty string in the graph)'s input ) than declared () has more inputs (' appeared multiple times.Attribute ') has more outputs ()'s output (inputs_.size() - 1) == i: failed validating the check: Duplicate type constraint name!(it.GetName().empty())ONNX Schema (outputs_.size() - 1) == i' is expected to have field 'tensors'' is expected to have field 'graphs'' is expected to have field 'ints'' is expected to have field 'strings'' is missing.Required attribute '' is expected to have field 'type_protos' has unknown expected typeInvalid tensor data type optionalsparse_tensor(Unsuported type proto value case.DataTypeUtils::FromDataTypeString - Received invalid data type string complex64mapopaqueInvalid data type optional(map(tensor(complex128The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.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.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.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)The output is a 1-D tensor of string, float, or integer.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.An integer vocabulary array.
One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type.The size of each input in the input listmap(string, int64)map(int64, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The 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.A string vocabulary array.
One and only one of the vocabularies must be defined.map(string, float)map(string, double)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 an integer map to either string or float.map(int64, string)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.ccWeights of the model(s).Weights of the intercepts, if used.The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The total number of regression targets, 1 if not defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'The input must be a tensor of a numeric type, either [C] or [N,C].The 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.One of 'MAX,' 'L1,' 'L2'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, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.Input type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setThe input type is a tensor of any shape.Only one of values_*'s can be set in label encoder.Value(s) to change toA value that needs replacing.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Label encoder has only one input.Label encoder has only one output.Value(s) to change to.Input's shape should be 1D or 2DThe output will be a tensor of strings or integers.A collection of weights of the model(s).The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]A list of ints.A list of floats.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.An integer.A float.A list of strings. One and only one of 'value_*'s should be set.A string.The node id of each weightThe index of the target that each weight is forFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The weight for each targetThe total number of targets.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)The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Child node if expression is trueChild node if expression is falseNode id for each node. Node ids must restart at zero for each tree and increase sequentially.The output will be a sequence of string or integer maps to float.seq(map(string, float))The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.The 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.'Support vector coefficients.The number of support vectors.First set of probability coefficients.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.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Child node if expression is true.Child node if expression is false.Popularity of each node, used for performance and may be omitted.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'node id that this weight is for.The index of the class list that each weight is for.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.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.Thresholds to do the splitting on for each node.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.The input type must be a tensor of integers or strings, of any shape.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.cctype: expected to have tensor or sparse tensor type and Output does not match type of output: Input: should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute axis must be in [-rank, rank-1].concat_resultConstrain output types to any tensor type.Invalid value of attribute 'axis'. Rank=) and outputs ( Value=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.Constrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Input axes has incorrect lengthInput steps has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsMismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (outputsThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoD:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.(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 shapesreshapedCastLiketarget_typeInvalid position of 0.Invalid dimension value: Target shape may not have multiple -1 dimensions.expandedList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Blocks of [blocksize, blocksize] are moved.Blocksize must be positiveInput tensor must be 4-dimensionalList of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapesqueezedConstrain repeat's type to int64 tensors.repeatsThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {}, input shape = {Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedInput rank for starts and ends should be the same: (Input axes has invalid data) vs (stepsWhich 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]Input and output types can be of any tensor type.updates(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.Constrain input types to float tensors.Constrain output types to boolean tensors.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.depth(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Constrain to any tensor type.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).Constrain to boolean tensors.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.roiWhen coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.Constrain roi type to float or double.sizes This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Constrain input 'X' and output 'Y' to all tensor types.If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".OneHot node must have three inputs.Constrains to boolean tensors.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Indices tensor must have rank >= 1Constrain input and output types to all tensor and sequence types.condition'axis' must be in [-rank(indices), rank(indices)-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).inverse_indicesconstant_valueD:\a\_work\1\s\cmake\external\onnx\onnx\defs\optional\defs.cc broadcasting: (Invalid rank for Wrong op_type name for running propagation: Constrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.]. Its actual value is: 'axis' must be in [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.ccsumConstrain input and output types to numeric tensors.Value of beta.Value of alpha.List of tensors for data_0HS_XHardSwishX_ExpX_ReduceSumX_ReduceMaxX_SubX_LogCoefficient of ELU.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Elu_ResultThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.X_alphaConstrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors.slope 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. input and zero_point pair is expected to have same type.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.IndicesConstrain index tensor to int64ValuesWhether to return the elements in sorted order.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.K input must be of type int64.Axis has less than the requested k elements.K input must be a one-dimensional tensor of size 1.KConstrain input and output types to all tensors.'shape' input must be 1D tensor of type INT64Weight rank must be 1.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.const_one_floatconst_one_castedinput_gather_element_transformsqueeze_maskTarget rank must be 1 less than the input rank.Input and target dimension value mismatch.weight_gather_tempweight_gather_temp_1Constrain input and output types to all numerical tensor types.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.InputsX_NCDX_NDCShape3DNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Constrain target to integer typesNegativeLogLikelihoodLossEinsum expression string. does not match the equation indices.Rank of input reductionconst_zeroConstrain input and output types to floating-point tensors.expanded_targetignore_indexconst_onetargetIf set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.axis tensor can be int32 or int64 onlyconst_ignore_indexconst_zero_target_typedloss_sumweight_gather_sumconst_zero_floatconst_zero_castedexpanded_target_int64transform_targetsloss_N1ddnoneinput_gather_elementloss_NCddweight_gatherloss_unweightedlossloss_NddSoftmaxCrossEntropyLossX_shapelog_probX_LogSMX_LogSM_NCDlabelsAttribute expected to have a one-dim sparse tensorAttribute expected to have a one-dim tensor expected to have: or UNDEFINED. Got: Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Upper boundary of the output values.Lower boundary of the output values.Constrain output types to be numerics.Constrain input types.(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'.(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.Input tensor must be 2-dimensional(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64The mean of the normal distribution.The shape of the output tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Attribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_stringsvalue_stringAttribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.Attribute 'value_float' expect a float.Attribute 'value_ints' expect a list of integers.sparse_valuevalue_floatsvalue_floatvalue_intsvalue_intD:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Invalid shape value: Shape input must be a one-dimensional tensor.The value for the elements of the output tensor in sparse format.The value for the elements of the output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, int64, output tensor.BernoulliConstrain output types to all numeric tensors and bool tensors.sub_resultrangecurrentcond_outceil_resultdiv_resultdelta_castedsub_result_castedConstrain output types to integral tensors.prevcondiloop_body_attributeX_randomConstrain input types to common numeric type 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_greatervariadic_outputceil_result_relu_boolceil_result_relu_intceil_result_reluAll inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)Attribute dilations has incorrect sizeInput 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] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Attribute pads has incorrect sizeAttribute kernel_shape must be specifiedAttribute kernel_shape 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. Attribute pads has incorrect size.Input tensor X must have atleast 2 dimensions.MaxUnpool op must have either two or three inputs.'output_shape' must be rank 1 tensor.Attribute kernel_shape must be specified.Attribute kernel_shape has incorrect size.Attribute strides has incorrect size.Constrain input and output types to float and 8 bit tensors.Second input tensor has wrong dimensionDilation 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.ccnumber of groups input channels and output channels are divided into. default is 1.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain output y data type to 32-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.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.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain bias type to 32-bit integer tensor.This number of op outputs should be 1 when Training_mode = False, but it is not.This number of op outputs should be 3 when Training_mode = True, 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. If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).The pads attribute cannot be used simultaneously with auto_pad attributeStride along each spatial axis. If not present, the stride defaults to 1 along each axis.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 padsAttribute pooled_shape must be specifiedAttribute pooled_shape has incorrect lengthRoIs tensor must have 2 dimensionsMultiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).'output_shape' must have same number of elements as the shape of input tensor X.p value of the Lp norm used to pool over the input data.number of groups input channels and output channels are divided into.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input W.ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.Input is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain input and output to all tensor types.The exponent.Scaling parameter.The number of channels to sum overInput shape must have either [C] or [1,C] dimensions where C > 0The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falsestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"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=1Minimum 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 n-gram length. If this value is 3, 3-grams will be used to generate the output.1-D tensor of floatslist 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.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 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.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.Constrain mean and variance types to float tensors.Constrain scale and bias types to float tensors.running_varinput_meanrunning_meaninput_varThe lambd value for the Shrink formulation. Default is 0.5.Invalid value() for attribute 'axis'The bias value added to output. Default is 0.The axis on which to apply normalization, -1 mean last axis.training_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.X_varianceSTDVarianceE_XsquaredProcessed_STDA 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.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.List of stop words. If not set, no word would be removed from X.X_squaredEX_squaredX_RMExponentD:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccDilation value along each spatial axis of filter.Stride along each spatial axis.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The epsilon value to use to avoid division by zero, default is 1e-5f.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.Stride along each axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output 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.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.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch. 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_meanconsumed_inputslegacy optimization attribute.varThe ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.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. 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. (float, default 0.5) the ratio of random dropoutConstrain mean and variance types to float tensors. It allows all float type for U.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. 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) 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. 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 D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\defs.ccThe 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].First input tensor must have rank 3When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output was 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.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. was notGraph attribute inferencing returned type information for outputs. Expected Scan input If node has but subgraphs produce then_branch and else_branch produce different number of outputs. Scan 'body' subgraph outputs should all be tensors but output tensor of int64, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.v_final_and_scan_outputsinitial_state_and_scan_inputstensor of bool, which should be a scalar.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccOnly boolAll Tensor and Sequence typesv_initialInput 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 unknown) is not equal to number of scan inputs ( axis value is invalid for a tensor of rank was not a tensor.Number of scan output axes specified () is not equal to number of scan outputs (Number of scan input axes specified (Input was expected to have either tensor or sequence type. Got Element type of input was unknownOutput was expected to have tensor type. Got All Tensor typesInt64 tensorAn attribute specifying the number of scan_inputs M. The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.final_state_and_scan_outputsAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float or int32 tensor.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)X_RangeX_Max_AdjustedX_MaxX_Min_AdjustedRounded_ZeroPoint_FPClipped_ZeroPoint_FPInitial_ZeroPoint_FPMin_ScaledConstrain 'x' to float tensor.X_MinQ_MaxQ_MinConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Zeropointtileswidth_scaleThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.Three modes: constant(default), reflect, edgepaddingsList 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`.New shapeOne float, indicates the value to be filled, default is 0Attribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute axes has incorrect lengthIncorrect or missing attribute value for starts and endsTwo interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.Ranks inferred () is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.'step' cannot be 0length of each output. Values should be >= 0.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.ccInvalid Target shape product of 0Invalid position of 0Target shape may not have multiple -1 dimensionsrank must be greater than axisWhich axis to concat on. Default value is 1.length of each outputWhich axis to split onoutputs... This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.Constrains input and output to only numeric types.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Ending indices (exclusive) of corresponding axis in axes`Starting indices 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].Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]One float, indicates the value to be filled.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`.Attribute pads has incorrect lengthAttribute value for pads is required(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.'axis' must be in [-rank(indices)-1, rank(indices)]Which axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccConstrain input and output types to high-precision and 8 bit numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).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.axes as an input and attribute cannot be specified at the same time.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Value of beta default to 0.5Value of alpha default to 0.2If set, defines the broadcast dimensions. See doc for details.broadcastCoefficient of leakage default to 0.01.Maximum value, above which element is replaced by maxMinimum value, under which element is replaced by minThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Pass 1 to enable broadcasting If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeDescribes 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.ccScalar multiplier for input tensor C, the default value is 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedDimension on which to do the sort.Number of top elements to retrieveInvalid value for attribute kDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.O2O1Constrains input types to all numeric tensors.Constrains input/output to boolean tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains output to boolean tensor.Constrains input to boolean tensor.One 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.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\old.ccThe axis in which to compute the arg indices.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\old.cc 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.default 1; Pooled output Y's height.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. The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.default 1; Pooled output Y's width.score_thresholdiou_thresholdInteger 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_indicesD:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccmax_output_boxes_per_classboxesLoop 'body' subgraph outputs should all be tensors but output D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.Mismatched tensor element type for output Mismatched type for output then= else= is null. Type info is expected.SequenceConstruct is expected to have at least 1 input.Element type of inputs are expected to be the same.Input type for input at index 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.positionInput 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).Input Sequence and Tensor are expected to have type info. Current type is null.Constrain input types to any tensor type.output_sequenceinput_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= 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.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output types to all tensor types.Constrain split size to integral tensor.Only supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).], 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.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=[Constrains input to float tensors.Constrains input to integral tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccEnable broadcastingIf set, defines the broadcast dimensions.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.ccfowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.source sequence type missing element type.target sequence type missing element type. source=Mismatched type:Mismatched tensor element type:Mismatched sparse tensor element type: target= is required to be non-empty. ==> Context: values, but NNZ is ) has ' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: ) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values (] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (setting data_type field (tensor name: float_dataint32_datastring_data): Unrecognized type value case (value_info name: data_type) to UNDEFINED is not allowed is required but missing.elem_typekey_typevalue_typevalue_info' of nameField ') should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: ' instead of ') is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!TensorProto (tensor name: ) is stored externally and should not have data field.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.) should be stored in int64_dataraw_datadouble_datauint64_dataConstantFill with domain_version of No Op registered for is deprecated in domain_version of Op registered for , type: NodeProto (name: Warning: Checker does not support models with experimental ops: No opset import for domain 'Attribute (name: ) should refer to attribute in parent node.op_type) has zero input and zero output.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field.name: ' of node: Nodes in a graph must be topologically sorted, however input '' has been used as output names multiple times. in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers is not output of any previous nodes.Graph must be in single static assignment (SSA) form, however 'initTensor initializers must have a non-empty name initializer name is not unique OpType: Bad node spec for node. Name: graph' has been used as graph input names multiple times.optional_type already exists.Data for input Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Container for generated shape data cannot be nullptr when enable_data_propagation option is set. were provided inputs but Warning: Shape inference does not support. No schema registered for this operator.Warning: Unsupported operator Shape inference error(s): type case mismatch. existing=type case unsupported. existing=type case unsupported for symbolic shape inference. inferred= models with experimental operators: opaque_typesparse_tensor_typeNOT_SET inferred=Graph has , node name: (op_type:: (Inferred shape and existing shape differ in dimension unk__Input 'scales' must have float element type.Dimension value inferred (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X') is not equal to the existing dim value (onnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.ModelProtoonnx.AttributeProtoonnx.FunctionProtoonnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.OptionalFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   Error compiling 'pattern too large - compile failedError reverse compiling 'Unknown encoding D:\a\_work\1\s\cmake\external\re2\re2\re2.cc': Error parsing 'SearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyprogram size pattern length DFA out of memory: SearchDFA inconsistencyRE2: invalid startpos, endpos pair. [Unexpected re_anchor value: bytemap range list count Invalid RE2: text size: endpos: startpos: unexpected )trailing \no argument for repetition operatorinvalid repetition sizeinvalid character classinvalid character class rangemissing ]missing )Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceRegexp not destroyed.D:\a\_work\1\s\cmake\external\re2\re2\regexp.ccBad reference count NumCapturesWalker::ShortVisit calledbad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupWalk NULLStack not empty.D:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hcc(ccccc c0c@chcccccCompiler::Copy called!No ranges in char classMissing case in Compiler: should never happenD:\a\_work\1\s\cmake\external\re2\re2\compile.ccunknown round: RE2: unexpected op: Bad hex digit D:\a\_work\1\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of AddFoldedRange recurses too much.AnyBad call to ParseState::ParsePerlFlagsUnhandled in step in AddToThreadqD:\a\_work\1\s\cmake\external\re2\re2\nfa.ccunhandled Unexpected opcode in short circuit: Bad args: nsubmatch=context does not contain textFailed to analyze start state.StateSaver failed to restore state.DeadState in RunStateOnByteUnexpected special state in RunStateOnByteNULL state in RunStateOnByteRunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetD:\a\_work\1\s\cmake\external\re2\re2\dfa.ccunhandled opcode: |Unexpected opcode: njob_ = job_.size() = D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccGrowStack() failed: 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.ccSimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is Malformed repeat Simplify case not handled: D:\a\_work\1\s\cmake\external\re2\re2\simplify.ccCase not handled in ComputeSimple: DoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit called,c,c,c,c,c,c,c,c,c(-c,c(-c.c,c.c,c0.c@.c .c@.c-c,c-c,c-cH.c-cH.c-c-c-c-c-c,c-c,cP-c,c@-c,cp-c,c`-c,c.c8-c.c8-c/c-c/c-c.c,c.c,c.c<.c.c<.cp.c-c`.c-c.cP.c.cP.c\S\s\W\w\D\d09az 09AZaz09 !~!/:@[`{~09AZ__az09AZ__az ~[:^graph:][:graph:][:^lower:][:lower:][:^cntrl:][:cntrl:][:^digit:][:digit:][:^ascii:][:ascii:][:^blank:][:blank:][:^alnum:][:alnum:][:^alpha:][:alpha:]AZAZaz 09AFaf[:^word:][:word:][:^xdigit:][:xdigit:][:^space:][:space:][:^upper:][:upper:][:^print:][:print:][:^punct:][:punct:]ScXzcScЛcSccScc0c#xSccSccScc\ScctcPScscpSc chScȏc0ScHc SccHScc@ScAZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! ! !!!!!!!$!$!&!&!(!(!*!-!0!3!>!?!E!E!!!,.,`,`,b,d,g,g,i,i,k,k,m,p,r,r,u,u,~,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjll""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~§§ħǧɧɧ!:0.40484SoSmSora_SompengSogdianSignWritingSiddhamSkSinhalaScSaurashtraShavianSharadaRejangPsalter_PahlaviSamaritanRunicThaanaTeluguTibetanThaiTakriTai_VietTangutTamilTagbanwaTagalogTai_ThamTai_LeSundaneseSoyomboSyriacSyloti_NagriP  &(:<=?MP]-@BIPuP'0c6@U`g 578<<?U`v   9            5 ` |        5 @ U ` r    H  # ''0E7&DDGGPrvv+ (*02359==PP]a4GJ_a/DD+   /??AA 2::PP\ .@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/kckwk}kkoJoPoPopՌPRdgpjp|,NN!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~ݦ4@ Jvz      8 : ? ?   $ ' FP8F'+-4ss/14467>>;<@@flpt8?BDFF^^3:==?@"%'+/79:;<>>CC 38;>GGQVY[068=??16::<=?EGGjj0k6kOoOooooogi{BD6;luu!#$&*06DJ0Y68<>>@ACDFOjjjjPz`eghj@nn(O  `nn3NTVg7Rk";Vo6NPUp"Ch8j      ;;gi{(([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bb  ( ) / / _ _ 00@w$$++<>^^``||~~uu   p p   O O y y ??446688mm@@ajt|D D R R z | !!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!@!D!J!M!O!O!!!!# #(#+#&$@$J$$$%g''''''))))))s+v++++,,P.Q.....////0000 0 06070>0?00011111122*2G2P2P2`222223MMƤ !(+69wy[[jk))bbdfii >>@@\\^^))]]}};;==F F ~ ~ # # # #*#*#i'i'k'k'm'm'o'o'q'q's's'u'u'''''''''''''))))))))))))))))))))))))))))#.#.%.%.'.'.).). 0 0 0 0 0 000000000000000>>6688::<<>>@@BBDDHHZZ\\^^ ==]]``cc      9 9 .... . . . ... . .( ( PmptKPY^_AZaz%,\bekwyq q   *!+!2!2!N!N!`!!`,,"§ʧ0Z\dfi!:AZO--  ....:.;.@.@.0000000012XXcc 3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq-/= ooWW  ? ? 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