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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 %I64u^^^^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.trainingai.onnx.mlai.onnx.preview.trainingVIWEF!=D:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.hD:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)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 != nullptrSpecified provider is not supported.location 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: 1.10.0Tensor 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 is not implementedai.onnxonnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughUnsupported version '' in custom op 'D:\a\_work\1\s\onnxruntime\core\session\custom_ops.cconnxruntime::CustomOpKernel::CustomOpKernelTcustom op registered at runtimeall typesInputtype_id_counter == 1There 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.T0onnxruntime::CreateCustomRegistryOutputAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.htype == dtype_onnxruntime::Tensor::MutableDataRawTensor type mismatch.Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::DataRaw) != new size (TensorSeq: tensor to be added has a different data type.IsSameDataType(tensor)onnxruntime::Tensor::Reshapeonnxruntime::TensorSeq::Add is not present.Required output at index D:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.output_ptronnxruntime::OpKernelContext::RequiredOutputp_ml_valueonnxruntime::OpKernelContext::Outputonnxruntime::ProviderSharedLibrary::EnsureD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime_providers_shared.dllonnxruntime::ProviderLibrary::GetGetProviderProvider_SetHostonnxruntime::ProviderSharedLibrary::Unloadonnxruntime_providers_rocm.dllonnxruntime_providers_dnnl.dllonnxruntime::ProviderLibrary::Unloadonnxruntime_providers_cuda.dllonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryTensorRT execution provider is not enabled in this build.invalid unordered_map keyMissing Input: input_ptronnxruntime::OpKernelContext::RequiredInputonnxruntime::OpKernelContext::InputRequired input at index No requested allocator availableProvided allocator is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Env is nullOrtMemoryInfo is nullProvided OrtMemoryInfo is nullD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccenv_ptr == p_instance_OrtEnv::Releaseonnxruntime::SyncProvidersonnxruntime::IOBinding::SynchronizeInputsonnxruntime::IOBinding::BindInputD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::SynchronizeOutputsp_providerbad cast MemoryType: DeviceId:] OrtMemType: OrtAllocatorType:Device:[DeviceType:OrtMemoryInfo:[name: id:CUDAExecutionProviderDmlExecutionProviderTensorrtExecutionProviderValidating no unexpected access using an invalid node_index. Got: Max:D:\a\_work\1\s\include\onnxruntime\core/graph/graph.h has already been registered.onnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider D:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoRelease_State_Create_State_Compute_'4IsOptionalTensor(type)Provided type is not an optional tensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorORTM[json.exception.] MemcpyTransformerother_errorsession.use_env_allocatorssession.set_denormal_as_zerosession.save_model_formatsession.load_model_formatsession.intra_op.allow_spinningsession.inter_op.allow_spinningmemory.enable_memory_arena_shrinkageoptimization.save_runtime_optimizationssession.use_ort_model_bytes_directly() 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.ccThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: Provider: [is_model_proto_parsedModelProto needs to be parsed to check for ORT config within itonnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::InferenceSession::ConstructorCommon::::operator ()Flush-to-zero and denormal-as-zero are offongraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()onnxruntime::InferenceSession::ConstructorCommonstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: session--intra-op-Creating and using per session threadpools since use_per_session_threads_ is truecustom join thread function not set for inter op thread pool-inter-opto.custom_join_thread_fncustom join thread function not set for intra op thread poolsession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.Using global/env threadpools since use_per_session_threads_ is falseFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: CastFloat16Transformeronnxruntime::InferenceSession::{dtor}::::operator ()Error during EndProfiling(): resultExecution providers must be registered before the session is initialized. Received nullptr for exec provideronnxruntime::InferenceSession::~InferenceSessionUnknown error during EndProfiling()Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized.onnxruntime::InferenceSession::RegisterExecutionProviderParallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsException during loading: onnxruntime::InferenceSession::SaveToOrtFormatEncountered unknown exception in Load()Unknown exception in Load()onnxruntime::InferenceSession::LoadThis session already contains a loaded model.model_loading_arrayFailed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().model_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.onnxruntime::InferenceSession::LoadOrtModelonnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::TransformGraphSerialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.ORT model verification failed.This session has already been initialized.SessionState is null. Invalid ORT format model.Missing Model. Invalid ORT format model.The ORT format model version [] is not supported this build onnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesImplonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelThe session already has a PrePackedWeightsContainer instanceThe provided PrePackedWeightsContainer instance to be added to the session is nullonnxruntime::`anonymous-namespace'::AssignNodesToEpsFromHashesFailed to find kernel def hash () in kernel registries for '.Initializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: Session has already been initialized.Model was not loaded.Model was not loadedonnxruntime::InferenceSession::InitializeUnable 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.Adding default CPU execution provider.Encountered unknown exception in Initialize()Session successfully initialized.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.NchwcTransformer Got: Expected: Please fix either the inputs or the model.session_initialization index: Got invalid dimensions for input: for the following indices Invalid rank for input: elements.)))) , expected: (Unexpected input data type. Actual: ( is not expected to be of type tensor.Invalid Feed Input Name:Size mismatch: feed_names has elements, but feeds has is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputstensorInput with name: Output vector pointer is NULLseq is not expected to be of type tensor sequence.sparse_tensorInvalid Output Name:Output vector incorrectly sized: output_names.size(): p_fetches->size(): At least one output should be requested.Running with tag: Session not initialized.onnxruntime::InferenceSession::RunSession was not initializedonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelMetadatamodel_runEncountered unknown exception in Run()Profiler is disabled.onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetOverridableInitializersCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingDid not find an arena based allocator registered for device-id combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Unsupported device specified in the memory arena shrink list: Unable to shrink arena: error message: The registered allocator for device-id combination is not an arena based allocator: onnxruntime::InferenceSession::CreateLoggerForRunrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasonnxruntime::InferenceSession::AddPredefinedTransformersonnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: /961c151d2e87f2686a955a9be24d316f1362bf21 3.9.1model_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.microsoft[TypeInferenceError] Input type was nullElement type of input unknownInput expected to have tensor or sparse tensor type. Got: expected to have optional typeElement type of sequence input expected to have sequence typeOutput expected to have type but instead is nullElement type of optional input [ShapeInferenceError] Mismatch between source and target type. Source= Target=Unsupported Source/Target type=tensor(uint64)tensor(uint32)unknowntensor(float)tensor(float16)tensor(int64)tensor(int32)tensor(uint16)tensor(uint8)tensor(bfloat16)tensor(double)seq(tensor(uint16))seq(tensor(uint8))tensor(int16)tensor(int8)seq(tensor(int16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(uint32))seq(tensor(float))seq(tensor(float16))seq(tensor(int64))seq(tensor(int32))tensor(complex64)tensor(bool)tensor(string)seq(tensor(double))seq(tensor(complex64))seq(tensor(bool))seq(tensor(string))tensor(complex128)seq(tensor(complex128))Schema error: version: ) from file , but it is already registered from file line , but its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h)., but its version is not in the inclusive range [Only CPU devices are supported for now.An allocator for this device has already been registered for sharing.Only CPU allocators can be shared between multiple sessions for now.stringNo allocator for this device has been registered for sharing.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.inter-opintra-opException caught: seq(tensor(bfloat16))XinputMemcpyFromHostD:\a\_work\1\s\onnxruntime\core\session\environment.ccConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.YoutputMemcpyToHost: value, column parse_errorparse errorout_of_rangetype_errorinvalid_iterator at line onnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: session_optionsort_configonnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting intra_op_num_threads to D:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.ccSequential modeonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: Setting inter_op_num_threads to onnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to Parallel modeUnsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASICJson stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to onnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()onnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.ORT config json from the model: inter_op_num_threads option in the model file must be an integerinter_op_num_threadsintra_op_num_threads option in the model file must be an integerintra_op_num_threadsgraph_optimization_level option in the model file must be an integergraph_optimization_levelexecution_mode option in the model file must be an integerexecution_modeIgnoring unsupported session option in ORT config: enable_profiling option in the model file must be an integerenable_profilingcannot use at() with key '' not foundcannot use key() for non-object iteratorsarrayobjectnullinvalid map keynumberdiscardedbinarybooleancannot compare iterators of different containerscannot get valuewhile parsing syntax error ; expected unexpected ; last read: '- invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if givenstring literalnull literalfalse literaltrue literal']''{''['number literal','':''}'invalid number; expected digit after '-'unknown token'[', '{', or a literalend of inputinvalid comment; missing closing '*/'invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid comment; expecting '/' or '*' after '/'invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid 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 \u001Evector too longobject separatorobject keynumber overflow parsing 'cannot use erase() with iterator out of rangeiterator does not fit current valuetype must be number, but is onnxruntime::GraphTransformer::RecurseCastcast node to cast from float16 to float32 on cpuInsertedCast_D:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::RemoveDuplicateCastTransformer::ApplyImplRemoveDuplicateCastTransformertodtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtypeonnxruntime::InsertCastTransformer::ApplyImplROCMExecutionProviderArmNNExecutionProviderACLExecutionProviderBiasDropoutFusionAttentionFusionCommonSubexpressionEliminationCastEliminationBiasSoftmaxFusionBiasGeluFusionConvActivationFusionConvMulFusionConvBNFusionConvConvAddFusionDropoutEliminateDropoutDivDivMulFusionExpandExpandEliminationEmbedLayerNormFusionDynamicQuantizeMatMulFusionGemmActivationFusionGeluFusionGeluApproximationFastGeluFusionEliminateIdentityGemmTransposeFusionGemmGemmSumFusionMatMulAddFusionSimplifiedLayerNormFusionLayerNormFusionIdentityNhwcTransformerMatmulTransposeFusionMatMulScaleFusionMatMulIntegerToFloatFusionWhereNotWhereFusionAddNoopEliminationReluReluQuantRewriteQDQS8ToU8TransformerQDQPropagationTransformerReshapeFusionFuseReluClipTransposeOptimizerSliceEliminateSliceSkipLayerNormFusionsession.disable_quant_qdqUnsqueezeUnsqueezeElimination_RuleBasedTransformeroptimization.enable_gelu_approximationD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelLevelonnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformeronnxruntime::optimizer_utils::GenerateTransformersUnsupported optimization level: onnxruntime::optimizer_utils::GenerateTransformersForRuntimeOptimizationsVitisAIExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::MemcpyTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefsdup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializers' doesn't support memcpy Execution type 'MemcpyCopy from/to host memoryonnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered unsupported data type: ReadExternalRawData() failed: onnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeCould not find OrtValue with name 'startendDequantizeLinearonnxruntime::ConstantFolding::ApplyImplConstantFoldingCould not find a CPU kernel and hence fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of D:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccShape node 'can't constant fold Unsupported data type: shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccdiv_inputs.size() == 2Constant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesMatMulonnxruntime::`anonymous-namespace'::GetScaleFromNodeMulmul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2onnxruntime::MatMulScaleFusion::ApplyImplFusedMatMulalphaFused MatMul and Scale_FusedMatMulAndScaleonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeConcatSqueezeSlice ends is less than INT_MAXExpected value:Slice parameter is not expected. Input index:Start MatchGemmSubgraphGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathaxesunsqueeze_after_gather axes value not expectedFaild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueGatherStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedaxisgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerunidir mask is not constantSubFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphSoftmaxFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Softmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.mask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertEqualStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphTransposeFailed to find path for past_kFailed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpermPass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphv_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedStart CheckNodesInPathQOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizePass CheckNodesInPathQStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose has not perm attributeonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedCast mask from int64 to int32Start FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32MatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeFaild to find path to qkv_matmulSplitFaild to find path v to SplitCheckNodesInPathV return falsek and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs CheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kqkv_weightsqkv_biasMask shape is unknown or not 2D, or data type unknownonnxruntime::ConvertMaskToInt32Attentionnum_headsunidirectionalFused an attention node for GPT.shape of layer norm bias tensor not expectedTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplq root should be layer normalizationD:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplLayerNormalizationFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32onnxruntime::FuseSubGraphQKFused an attention node.q_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed in match v_matmul and v_add input shapeFailed in match input mask subgraphonnxruntime::FuseSubGraphQKDistilBertFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vepsilononnxruntime::ConvBNFusion::Applybn_scale_tensor_protoonnxruntime::Initializer::ToProtodata type is not supportedconv_B_tensor_protoconv_W_tensor_protoConvBnFusion_BN_B_ConvBnFusion_W_bn_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_var_tensor_protobn_mean_tensor_protoBatchNormalizationqdq_s8_to_u8_zp_conversionQuantizeLinearqdq_s8_to_u8_quantD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.cconnxruntime::QDQS8ToU8Transformer::ApplyImplMaxPooldropQDQSelectorActionTransformer*DQ1DQResize2DQAveragePoolonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of ClipD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccSigmoidactivationLeakyReluTanhmaxminFusedConvonnxruntime::ConvActivationFusion::ApplyImplactivation_paramsfused betaHardSigmoidfused Conv with activation D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination_data 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_constantD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.cconnxruntime::GeluApproximation::ApplyImplFastGeluGelu approximationGeluBiasGeluTotal Gelu Approximation (FastGelu) node count: _^X_onnxruntime::ReshapeFusion::ApplyImplallowzeroD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::Fuse_SubgraphCannot replace concat node with initializer:Total fused reshape node count: Fused reshape node: endsstartsonnxruntime::GemmSumFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.cctransBnew_gemm_input_defs.size() == 3transAgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0sum_output_edge.src_arg_index == 0!sum_input_moved_sum_transformedgemm_input_edge.src_arg_index < 2new_gemm_output_defs.size() == 1Fused Gemm with Sumother_sum_input != nullptronnxruntime::GemmSumFusion::SatisfyConditiongraph.RemoveNode(sum_node.Index())Sumup_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::ApplyImpl_CastInput shape is unknown or not 2D, or data type unknown_Int32Cast Input from int64 to int32Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Input data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::CheckInputD:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccConstantOfShapeRangetwo paths share the same shapeNonZeroSecond input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Second input of Gather should be a constant with value 1. Failed to match Shape node. The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Output edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherWord embedding shape not expected.onnxruntime::FuseSubGraphfused EmbedLayerNorm subgraphs EmbedLayerNormalizationposition_embeddingsmask_indexOptional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.Position embedding shape is not expected.Input id is not valid. Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape not matched.Position embedding data type shall be float or float16.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplGamma should be of shape (hidden_size). Beta should be of shape (hidden_size). Segment id is not valid. Input_ids and segment id should have the same shape. ̼+DynamicQuantizeMatMulMatMulIntegerToFloatDynamicQuantizeLinearonnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccD:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.Invalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::ApplyImpl' The model has input 'which does not equal the specified override of with a fixed dimension size fused Matmul and Add gemmonnxruntime::MatMulAddFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccSoftplusSeluEluFusedGemmfused Gemm D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplThresholdedReluParametricSoftplusScaledTanhSoftsignactivation__transformedFused Gemm with TransposeCreated a new Cast node to interchange Cast and Transpose nodesonnxruntime::ReorderCastAndTransposecast != nullptronnxruntime::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 nodesdeque too longD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplyConvMulFusion_Mul_B_ConvMulFusion_W_mul_B_tensor_protofused 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_Scaleonnxruntime::SimplifiedLayerNormFusion::ApplyImpl'7onnxruntime::SkipLayerNormFusion::ApplyImplSkipLayerNormalizationfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cc̼+_^X_reorderNOTSETauto_padkernel_shapedilationsstridespadschannels_lastReorderInputspatialreshape_nchwcgroupVALIDSAME_LOWERSAME_UPPERcoordinate_transformation_modelinearnearestmodechannelsReorderOutput_bn_nchwcGlobalMaxPoolscalesUpsamplefloornearest_modehalf_pixelalign_cornersasymmetricD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImplGlobalAveragePool and is not in valid range [-D:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1axis D:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImplbroadcast_axissoftmax_axisBiasSoftmax into softmax(input + bias)onnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.[ of node Not eliminating output Notonnxruntime::TransposeOptimizer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.cconnxruntime::NhwcTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccQLinearConvseedonnxruntime::BiasDropoutFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBiasDropoutfused Add-Dropout-(Add) for onnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.ccMatMulIntegeronnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::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 GeluD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImplfused GPT2Gelu subgraphs fast_gelu_outputGPT2Gelu^_^X_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_B_add_B_tensor_protoConvAddFusion_Add_B_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: TensorProto external data size mismatch. RandomNormalRandomUniformRandomNormalLikeRandomUniformLikeMultinomialD:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()Tried to allocate without valid type information, ort_value index=allocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::Infostatic_cast(index) < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8b33f88f7-c464-43e3-8692-97ac832bb14aonnxruntime::NodesToOptimize::GetNodeD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hgenerated at runtimeQLinearonnxruntime::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::SelectorActionTransformer::ApplyImplonnxruntime::SelectorActionTransformer::MatchAndProcessSaving runtime optimizations is not enabled in this build.onnxruntime::MergeIntoTarget::RunD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::ReplaceWithNew::RunForSaveFailed to set node op schema.onnxruntime::CreateReplacementNodeonnxruntime::ReplaceWithNew::RunFailed to remove node.CeilFloorReciprocalAbsIsInfIsNaNNegRoundkeepdimsnoop_with_empty_axesExpLogNhwcMaxPoolstorage_orderAcosAtanCoshAsinAtanhMaxAsinhAcoshCeluHardSwishShrinkSignTanSinhSinCosLessOrEqualMeanLessGreaterOrEqualReduceSumReduceLogSumCastLikePadOrXorMinAndBitShiftGreaterModPReluHardmaxLogSoftmaxArgMaxTilecom.microsoft.QLinearLeakyRelucom.microsoft.QLinearConcatcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoidReduceMinReduceProdReduceLogSumExpReduceMaxReduceL2ArgMinReduceSumSquareReduceL1com.microsoft.QLinearAveragePoolcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAddcom.microsoft.QLinearMulcom.microsoft.onnxruntime::utils::GetShapeD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hTypeProto must have shape for this to runonnxruntime::ApiTensor::NumElementsonnxruntime::ApiTensor::DataFailed to get size of TensorProtosize >= 0onnxruntime::ApiGraph::GetValueInfoFailed to find initializer for name: No NodeArg found for name node_arg_ != nullptrperm.size() == gsl::narrow_cast(shape_proto->dim_size())onnxruntime::ApiValueInfo::PermuteDims does not match rank Permutation length Permutation entry 0 <= p && p_int < shape_proto->dim_size()D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\api_impl.cc out of bounds for shape onnxruntime::ApiGraph::CopyValueInfoAdded in transpose optimizerconst_transpose_optimizerFailed to find initializer to reshape with name onnxruntime::ApiGraph::ReshapeInitializersuccessonnxruntime::ApiGraph::TransposeInitializernew_num_elts == old_num_elts to have different number of elementsCannot reshape initializer onnxruntime::MoveInputOutputonnxruntime::NodesToOptimizeIndicesBuilder::BuildD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.ccNode index value is too large to save to ORT format model: Index out of rangeonnxruntime::`anonymous-namespace'::MoveInputOutputImplA target node must be set.target_node != NodesToOptimizeIndices::kEmptyNodeIndexnode_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices::::operator ()RknpuExecutionProviderNnapiExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderCoreMLExecutionProviderMIGraphXExecutionProviderDnnlExecutionProvider^p^_P"___p_@(_X__h"_"_^@_^D:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsperm: is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max() does not align with rank of input data: [ is repeated.Attribute perm of Transpose has an invalid value. Value (local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0Transpose not implemented for empty tensors.onnxruntime::DoTransposeImplMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.(local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=onnxruntime::Transpose::Computeinput_tensor_ptr != nullptrMismatched data types between input and output Tensors. onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)center_point_boxboxes and scores should have same num_batches.scores must be a 3D tensor.boxes must be a 3D tensor.scores_tensoronnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].The most inner dimension in boxes must have 4 data.boxes and scores should have same spatial_dimension.D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeboxes_tensorNonMaxSuppressionoutput != nullptronnxruntime::NonMaxSuppression::ComputeGather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeMissing/Invalid 'axis' attribute valueTindD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.honnxruntime::GatherBase::GatherBaseinfo.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [info.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBaseaxes_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 axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareComputeX != nullptrI inputs. Found:LoopVBbodyInconsistent shape in loop output for output. Expected: Got:info_ == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.onnxruntime::Loop::Initinfo.GetAttr("body", &proto).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has All scan outputs MUST be tensorsLoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()onnxruntime::Loop::Computesession_stateSubgraph SessionState was not found for 'body' attribute.onnxruntime::Loop::SetupSubgraphExecutionInfo'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Executethen_branchinfo.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::Initelse_branch'If' node has num_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.ccIf outputs. outputs which doesn't match the subgraph's Failed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for If output onnxruntime::IfImpl::Executeonnxruntime::If::Compute' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::Initializeinfo.GetAttr("else_branch", &proto).IsOK()info == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::IdentityOp<1>::ComputeT1onnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatortypeOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()onnxruntime::Optional::OptionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.hTrying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::OptionalGetElement::Computeonnxruntime::PropagateInputOrtValueToFirstOutputD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableonnxruntime::Optional::ComputeOOptionalOptionalHasElementOptionalGetElementbad allocationInvalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input scale: NumDimensions() != UT2Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormtraining_modemomentumTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))called_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceonnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:Invalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenlargest <= 1onnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!offset % span_size_ == 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid.Can broadcast 0 by 0 or 1. by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::BroadcastIterator::Initonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Broadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0data_1.Shape() == shapeExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ExpandBroadcastLooperAll inputs must have the same shapedata_n.Shape() == shapeonnxruntime::Max_6::Computeonnxruntime::Mean_6::ComputeMust have 1 or more inputsinputCount >= 1onnxruntime::Min_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccUnsupported X type: onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::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.hfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfmod attribute must be true for floating point typesTensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandfmodonnxruntime::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 >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()RIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::Sum_6::Computedirectiononnxruntime::BitShift::BitShiftLEFTonnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfObidirectionalreverseforwardactivationsactivation_betahidden_sizeinfo.GetAttrs("activations", activations_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNactivation_alphaallowed_directions.find(direction_) != allowed_directions.end()clipAffineinfo.GetAttr("direction", &direction_).IsOK()layoutRNND:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::Computelayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::MakeDirectionD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::PrePackInvalid data type for LSTM operator of LSTM operator does not support double yettanhsigmoidLSTMactivation_func_names.size() == static_cast(num_directions_) * 3info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinput_forgetclip_ > 0.fonnxruntime::LSTMBase::LSTMBaseinfo.GetAttr("direction", &direction).IsOK()onnxruntime::DeepCpuLstmOp::Computeonnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()GRU operator does not support double yetGRUonnxruntime::DeepCpuGruOp::DeepCpuGruOplinear_before_resetD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::Tensor::MutableDataAsSpanC + (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::ComputeGemmonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())D:\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() == 1info.GetAttr("upper", &temp).IsOK()upperUnsupported input data type of onnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)Input tensor should have a rank of at least 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::Triluk should be a 1-D or 0-D tensor.TriluNo attribute with name:''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 >::ComputeAttribute name and type don't match for '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::functors::HardSigmoid::Initonnxruntime::functors::Selu::InitgammaD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::ElementWiseRangedTransform::Createunknown kernel typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ScaledTanh::Initonnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.hAxis tensor should be of type `int32_t` or `int64_t`CumSumAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1Ddims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::Initonnxruntime::SliceSkips::SliceSkipsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()exclusiveonnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.ccextents.size()=dims.size() == extents_.size()dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initstarts.size()=dims.size()=onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initsteps.size()=dims.size() == steps.size()onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13. 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::Computeinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valueonnxruntime::UpsampleBase::UpsampleBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsample.hcubicinfo.GetAttr("mode", &mode).IsOK()tf_crop_and_resize] is not supportted!pytorch_half_pixeltf_half_pixel_for_nnmode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outside`tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floor model uses the deprecated attributeyet this opset 'Linear' mode only support: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || (scales.size() == 4 && scales[0] == 1 && scales[3] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)Resize operatorUpsample operatorScale value should be greater than 0.scale > 0scale >= 1onnxruntime::UpsampleBase::ScalesValidationonnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.] is not supported!nearest_mode:[round_prefer_ceilceilcoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeonnxruntime::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)last_loop_red_size > 0onnxruntime::ValidateFastReduceKRKfast_shape[0] * fast_shape[2] == output.Shape().Size()fast_shape[1] == output.Shape().Size()fast_shape.size() == 3fast_shape[0] == output.Shape().Size()onnxruntime::ValidateFastReduceRKonnxruntime::ValidateFastReduceKROutput size mismatch.Only works on matrices with two dimensions.fast_shape.size() == 2must be overloaded.onnxruntime::ValidateMustBeOverloadedlast_loop_size > 0projected_index.size() > 0onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyD:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateKeepDimsAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1onnxruntime::ValidateNoTransposeReduceonnxruntime::ValidateCommonFastReduceReduction on all axes, output size should be 1.count == 1info.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.hselect_last_indexD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valueInvalid 'mode' attribute valueonnxruntime::PadBase::PadBasereflectedgeconstantpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count onnxruntime::Pad::ComputePads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]Pads tensor should be an INT64 tensorpads_tensor.IsDataType()onnxruntime::PadValueFromFloatD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensordata_rank * 2 == pads.size()onnxruntime::PadImpl'pads' has wrong number of valuesInput tensor has no dimensionsdata_rank > 0onnxruntime::PadInputWithDimValueOfZeroonnxruntime::GatherND::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrbatch_dimsGatherNDlast dimension of indices must not be larger than rank of input tensorindices tensor data type not supportedindices tensor must has rank larger than 0invalid index found, index = onnxruntime::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!onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::ComputeNull input ptronnxruntime::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_nd.ccD:\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 typeonnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCRDepthToSpace 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.blocksizeinput count mismatchDepthToSpaceSpaceToDepthUnsupported 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::Computeonnxruntime::GatherElements::GatherElementsGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_impl::::operator ()GatherElements op: Value in indices must be within bounds [ , ]. Actual value is Tile 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 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyonnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1Dimension of input must be 1 instead of . shape=splitInvalid 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 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= yetSplit operator does not support onnxruntime::Split::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.ccAn split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1onnxruntime::Split::ComputeImplD:\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()onnxruntime::SliceBase::SliceBaseUnexpected 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 ()Must have valid 'axis' attributeNon concat axis dimensions must match: Axis Data type mismatchonnxruntime::ConcatBase::ComputeImplonnxruntime::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 scalars got: Ranks of input data are different, cannot concatenate them. expected rank: input_rank == reference_rank has mismatched dimensions of src 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.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 ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeX and mask should have the same shaperatio 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]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()across_channelsinfo.GetAttr("across_channels", &across_channels_).IsOK()X->Shape().NumDimensions() == 4onnxruntime::LRN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccLRNsizeinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRND:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fbiasonnxruntime::Flatten::FlattenD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeFlattenx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccscale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)DequantizeLinear with type int32 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })onnxruntime::DequantizeLinear::ComputeSizeleft.NumDimensions() == 2 || left.NumDimensions() == 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.honnxruntime::GemmHelper::GemmHelperonnxruntime::GemmBase::GemmBaseinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.h N:right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: M_ >= 0 && K_ > 0 && N_ >= 0Gemm: Invalid bias shape for broadcastc_shape != nullptrc_shape is required if c_data is providedonnxruntime::GemmBroadcastBiasonnxruntime::MatMul::Computeonnxruntime::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 M_ == 1 && N_ == 1 was falsenum_dims_with_pad != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::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 ()stoll argument out of rangeinvalid stoll argumentstoull argument out of rangeinvalid stoull argumentstod argument out of rangeinvalid stod argumentonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorAttribute to is not set.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::Cast::Castsnprintf() failed with return value: %.8gonnxruntime::`anonymous-namespace'::CastToStringsnprintf_result > 0snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().NaNINF-INFinvalid expand shapeEinsum op: An implementation for the input type Missing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()->equationEinsumEinsum op: There must be atleast one inputonnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputePad 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] > 0dilations.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::ComputeSizePadDilationsUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypeGlobalLpPoolonnxruntime::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_modeInput dimension cannot be less than 3.onnxruntime::PoolBase::Computeonnxruntime::PoolProcessContext::initD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hkernel_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>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::TopkOpset9ConstructorCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cckop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommononnxruntime::TopkOpset11ConstructorCommonk_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()TopKinput count mismatch, expected 1 input - the tensor to be processedvalue of k must not be negativeinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1k argument [output count mismatch, expected 2 outputs to be present for TopK operator] should not be greater than specified axis dim value [UniqueUnsupported tensor type of delta in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!onnxruntime::SequenceErase::ComputeSequenceConstructonnxruntime::SequenceInsert::ComputeSequenceEraseViolation of the requirment that all input tensors must have the same data type.SplitToSequencenum_inputs >= 1onnxruntime::SequenceConstruct::Computesplit_size_sum (Invalid data type for split tensor SplitToSequence operator does not support ) != split_dim_size (onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputSSequenceLengthD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cc) specified for sequence of size (SequenceAtonnxruntime::GetSeqIdxUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeInvalid sequence index (SequenceEmpty), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsertonnxruntime::CreateCopyAndAppendCpuTensorInvalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })split_scalar > 0onnxruntime::SplitToSequence::ComputeImplSplit should be > 0ConcatFromSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccGot nullptr for sequence input.onnxruntime::ConcatFromSequence::Computeonnxruntime::ConvAttributes::ConvAttributesauto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributeskernel_shape num_dims is not compatible with W num_dims. kernel_shape: W: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h group: X num_dims does not match W num_dims. X: kernel_shape is not compatible with W shape.*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeInvalid input shape: num_input_channels: group count is <= 0output_shapeoutput_paddingonnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.filter number not equal to input channel number. filter_number: D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeConvTransposeNot enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: Not enough elements in strides. Expected: Output channels M is not divisible by group.onnxruntime::ConvAttributes::InferOutputShapeNot enough elements in pads. Expected: onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. M: Input channels C is not equal to kernel channels * group. C: kernel channels: D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computeinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolIndex 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::ComputeMaxUnpooloutput_shape is smaller than minimum required. output_shape: inferred output shape:Shape must be 1 dimensional as it's tensor data of a shapestrides_.size() == kernel_shape_.size()DetMatrix dimensions are not equal. Square matrix is expectedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()CompressD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::ComputeAttempt to retrieve final output before it was set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_gsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_onnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::Scan<9>::SetupSubgraphExecutionInfo has length of Invalid scan input: Expected dimensions or more but input had shape of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension onnxruntime::Scan<9>::Initscan_input_directionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()num_scan_inputsD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccNumber of entries in 'scan_input_axes' was but expected scan_input_axesscan_output_directionsonnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input of onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsSubgraph in 'body' produces outputs but Scan expects . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueOutput OrtValue has not been created for loop state variable output Scanonnxruntime::ScanImpl::TransposeOutputOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK(). Must be 0 or 1batch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOptime_axis and batch_axis must have different values but both are batch_axis != time_axisInvalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2. batch_size=sequence_lens shape must be {batch_size}. Got:Unknown tensor type of onnxruntime::ReverseSequenceOp::ComputeReverseSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cc. Value must be in range [0,Invalid sequence length: onnxruntime::RoiAlignBase::RoiAlignBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthavg specified. It should be either avg or maxInvalid mode of value sampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaleFirst dimension (num_rois) of batch_indices and rois don't matchNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly RoiAlignNull input X ptrNull rois_ptrdetect_positiveFailed to obtain detect_negativeFailed to obtain detect_positiveonnxruntime::IsInf::IsInfD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativeQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeOffsetIsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be sameW_zero_point_data[i] == W_zero_point_valueQLinearConv : input scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\qlinearconv.ccQLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidQLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)T4prepacked_buffers[0].get() == nullptrIsScalarOr1ElementVector(X_scale)onnxruntime::QLinearConv::ComputeOutputScaleQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)onnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)ConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)onnxruntime::MatMulInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.onnxruntime::MatMulComputeHelper::Compute::::operator ()IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)IsScalarOr1ElementVector(a_offset)onnxruntime::QLinearMatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1QLinearMatMulQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1*Conversion ErrorConversion Error:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:StringNormalizerattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizercase_change_actionattribute case_change_action is not setonnxruntime::string_normalizer::Locale::LocaleD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USis_case_sensitivelocalestopwordsEmpty stopwords not allowed!sw.empty()LOWERUPPERNONEattribute case_change_action has invalid valueSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 charsDuplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_errorbad conversionInput dim is zero but required output dim is non-zero. output_dims[i] == 0onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeResize: input/output value is nullptr Output dim value: Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Upsample: input shape needs to be at least a single dimension.Upsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionResize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeOnly one of scales or sizes must be provided as input.sizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.roi_input_idx_ > 0output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.onnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseCompute: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeUpsample: unexpected modeonnxruntime::UpsampleNearestTfIdfVectorizerngram_countsimpl_->max_skip_count_ >= 0status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredmax_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: max_skip_count is requiredngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()status.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredmin_gram_length must be inbounds of ngram_counts: <= max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()TFIDFmode: is unrecognized, acceptable values are TF,IDF,TFIDFonnxruntime::TfIdfVectorizer::TfIdfVectorizermode is requiredTFD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccmax_gram_lengthimpl_->min_gram_length_ > 0min_gram_length >= max_gram_length required: >= min_gram_lengthimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length is required(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.Got weights of size: but ngram_indexes size: pool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowed must be of equal sizeweights-gramsstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for !pool_strings.empty()pool_strings must not be empty if specifiednon-empty pool_int64s is required if pool_strings not providedpool_int64sDuplicate ngram detected, size: id: onnxruntime::ngram_details::PopulateGramsp.first->second->id_ == 0onnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()EyeLikeEyeLike : Input tensor dimension is not 2onnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.Must have a single dimensionD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.honnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeUnsupported output datatype with size: D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1Must have a single dimension of 1onnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: Unsupported value attribute datatype: !utils::HasExternalData(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoutils::HasDataType(t_proto)Tensor proto with external data for value attribute is not supported.ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was p_mlvalueD:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Initonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Compute but has batch size of pooled_height_ > 0pooled_shape.size() == 2info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_width_ > 0info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPoolspatial_scale_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::Computeroi_batch_id < batch_sizeroi_batch_id >= 0R->Shape()[1] == 5MaxRoiPoolp_ == 1 || p_ == 2op_kernel_info.GetAttr("p", &p_).IsOK()onnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()LpNormalizationD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormInvalid input scale: number of dimensions is not 1: vs. Invalid input data: number of dimensions is less than 3: Mismatch between input data and B: size of B != input channel count Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: onnxruntime::InstanceNorm::InstanceNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()InstanceNormalizationonnxruntime::InstanceNorm::Computeinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hscaleinfo.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalmeanonnxruntime::RandomUniform::RandomUniformlowhighinfo.GetAttr("high", &high_).IsOK()info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomNormalLike::RandomNormalLikeInvalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDnum_classes is < 1num_samples is < 1Empty dimensions for input tensorbatch_size is < 1Could not infer data type from input tensor with data type onnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDsample_sizeinfo.GetAttr("sample_size", &num_samples_).IsOK()info.GetAttr("low", &low_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikeInvalid data type of Output type not supported in this build: onnxruntime::MultinomialComputeonnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_stringinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderclasses_stringskeys_floatsvalues_stringsInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)info.GetAttr("default_int64", &default_int_).IsOK()LabelEncoderinfo.GetAttr("default_string", &default_string_).IsOK()default_int64onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2num_keys == num_valuesonnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2info.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2keys_int64svalues_int64svalues_floatsdefault_float_Unusedkeys_strings attribtues in LabelEncoder The ) must have the same length. (name: and the number of However, the number of key is info.GetAttrs(_value_field_name, values).IsOK()values is Input features_per_batch[Zipmap only supports 1D or 2D input tensors] != number of classlabels[D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_stringsMust provide classlabels_strings or classlabels_int64s but not both.ZipMapclasslabels_int64sSUMSOFTMAX_ZEROLOGISTICSOFTMAXTreeEnsembleRegressornodes_modesnodes_missing_value_tracks_truenodes_treeidsnodes_nodeidsnodes_valuesnodes_truenodeidstarget_idspost_transformInput shape needs to be at least a single dimension.base_valuesaggregate_functionnodes_falsenodeidsn_targetsnodes_hitratesnodes_featureidstarget_treeidstarget_nodeidstarget_weightsnodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_falsenodeids.size() == nodes_featureids.size()n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::compute in tree Node target_class_ids.size() == target_class_treeids.size() is already there.nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()Unknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::compute (falsenode). (weights).Unable to find node One falsenode is pointing either to itself, either to another tree. (truenode).-onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hpredictions.size() == predictions2.size()TreeEnsembleClassifierclass_weightsclass_treeidsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeX dims is empty.class_nodeidsclass_idsonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binarySVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramsLINEARkernel_typePOLYn_supports!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()coefficientsD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressorinfo.GetAttrs("rho", rho_).IsOK()rhosupport_vectorsUnexpected mode:onnxruntime::ml::SVMRegressor::Computenum_features == feature_count_one_classonnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classSVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hUnsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccUnexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Scaler) != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetonnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.scale_.size() == offset_.size()Scale size: (Unknown Category and zeros = 0.OneHotEncodernum_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64szerosonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpMAXD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hNormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of Unexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got targetsinterceptsLinearRegressorInput shape had more than 2 dimension. Dims=D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()multi_classLinearClassifierUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifieronnxruntime::ml::CastInputToFloatshape_size == out.length()Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpEmpty input dimensions.Empty value of imputed values.).FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsInvalid input type:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (DictVectorizerstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string %finvalid stof argumentstof argument out of rangeonnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of TO_FLOATTO_STRINGinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapInvalid input type of value: 0.fUnexpected CAST_TO value of Negative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccBinarizerthreshold is NaNInput data with index: ArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[Unsupported typeInput B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluleakyreluthresholdedreluInput X must have 3 dimensions only. Actual:}. Actual:Input W must have shape {Input R must have shape {Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameA + (M * K) <= A_endweights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNamescaledtanhhardsigmoidelusoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInput 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 < Ranks of pair-wise operands must be equal. Right shape: Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_rank == right_rankEinsum op: Input shapes do not alignleft_dim == right_dimcandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputThe override dims are not compatible with given tensor's shape. Left shape: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessleft.Shape().Size() == left_shape_override.Size() Right shape override: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.ccright.Shape().Size() == right_shape_override.Size()Right shape: Left shape override: onnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: candidate_output_dims[iter] == 1onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumComputePreprocessor::RunNumber of subscripts in the input equation does not match number of input tensorsD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccEinsum subscripts string contains too many subscript labels when compared to the rank of the inputFound a '.' not part of an ellipsis in input: Found '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsOutput subscript contains letters not seen in the inputsOutput subscript contains repeated lettersRank of the input must match number of subscript labels corresponding to the inputonnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsnum_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rankThe broadcasted dimensions of the inputs are incompatibleInputs have ellipses in them but the provided output subscript does not contain an ellipsis...Found '.' not part of an ellipsis in the output subscript providedFound a '.' not part of an ellipsis in the output subscript provided in the same dimensionThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z). The shape is: Another operand has a dim value of Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Einsum subscripts string contains too many subscript labels when compared to the rank of the input input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsCannot parse the diagonal elements along dims for input shape onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalrank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyinput_rank == permutation.size()The rank of the input must match permutation size for TransposeLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::IsTransposeRequiredEinsum op: Transpose failed: onnxruntime::EinsumOp::TransposeIncompatible matrix dimensions for matMulinput_shape_1_override[0] == input_shape_2_override[0]Einsum op: Exception during MatMul operation: input_shape_1_override[2] == input_shape_2_override[1]input_1.DataType() == input_2.DataType()Data types of the inputs must match for MatMulOnly 1 batch dimension is allowed for MatMulonnxruntime::EinsumOp::MatMulBatch dimension should match for MatMul;input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesInvalid values in ''. 0 == forward. 1 == reverse. did not.validonnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoNumber of entries in '' was Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputInverseonnxruntime::contrib::SkipLayerNorm::SkipLayerNormskip is expected to have same shape as inputinput is expected to have 3 dimensions, got Last dimension of gamma and input does not matchgamma is expected to have 1 dimension, got Last dimension of beta and input does not matchbeta is expected to have 1 dimension, got Last dimension of bias and input does not matchbias 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::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.ccinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale::ScaleScaleonnxruntime::contrib::Affine::Affineinfo.GetAttr("beta", &beta_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernel) does not match the number of channels (Bias size (ImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs("bias", bias_).IsOK()onnxruntime::contrib::ImageScaler::ImageScalerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceonnxruntime::contrib::Crop::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) + rightBorder () needs to be greater than or equal to the leftBorder () needs to be greater than or equal to the topBorder (Input's height () + scale_[1] (CropInput's width () + scale_[0] ( input dimensions instead) + bottomBorder (borderAttribute border needs to be specified with four border elements, got BifurcationDetectoronnxruntime::contrib::BifurcationDetector::Computepred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)min_ngram_size_ > 0max_ngram_sizeonnxruntime::contrib::BifurcationDetector::BifurcationDetectorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmax_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_datainfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0min_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()TidNGramRepeatBlocktoken_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0scores_dims.size() == 2scores_dims[0] == batch_sizeinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockngram_sizeonnxruntime::contrib::BiasGelu::Computeuse_approximationD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::ComputeeuclideansqeuclideanD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDistonnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()metricCDistThe first input of CDist kernel has wrong shape: Input shape dimensions mismatch:The second input of CDist kernel has wrong shape: D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestbilinearCropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskTransposeMatMulinput_num_bytes % 4 == 0keysMurmurHash3positivesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::ComputeOuter size must be M + 1Currently support only COO and CSR(x64) formatsCan not multiply A and B as inner dimension does not match. inner_A: Expecting COO 2-D indices shapeExpecting 2xValues == indicesExpecting the same number NNZ == size of Inner indicesCurrently supporting only 2-D matricesonnxruntime::contrib::SparseToDenseMatMul::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc vs inner_B: SparseToDenseMatMulonnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO m index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator () is out of bounds of lhs_right: COO k index: is out of bounds of out_left: COO indices must be 2-D, got: Char embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbedding Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Conv filter size does not match embedding_size attribute. conv kernal size 1: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute . conv filter size: embedding_size attribute: char_embedding_sizeembedding_sizeconv_window_sizemincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Expecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valueattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueTokenizermarktensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedInput string contains invalid utf8 charsInput string contains invalid utf8 chars: Match contains invalid utf8 chars: Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention query layer weight shape error! Expected:{}. Got: Attention v weight shape error! Expected:{, aw_attn_size}. Got:Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:Attention memory layer weight shape error! Expected:{}, Got: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape { found!], while AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccExpandDimsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseinput index out of rangeonnxruntime::contrib::EmbedLayerNorm::ComputeAttention cannot have past sequence and extra add qkInput 'input' is expected to have 3 dimensions, got onnxruntime::contrib::AttentionBase::AttentionBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hqkv_hidden_sizesinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionBase::GetPresentInput 'extra_add_qk' dimension 3 should be same as sequence_length, got Input 'extra_add_qk' dimension 2 should be same as sequence_length, got Expect to have present state output when past state input is givenInput 'extra_add_qk' is expected to have 4 dimensions, got Input 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'extra_add_qk' dimension 1 should be same as number of heads, got Input 'extra_add_qk' dimension 0 should be same as batch_size, got Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional_ set to falseInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'past' dimension 2 shall have length of Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_sizes doesn't match the wights dimensionInputs 'past' dimension 0 shall have length of 2Input 'past' is expected to have 5 dimension, got qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisiable by num_heads.hidden_size should be divisiable by num_heads:qkv_hidden_sizes first element should be same as the secondInput 1 dimension 0 should have same length as dimension 2 of input 0Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 1 should be 3 times of hidden dimensionInput 'bias' is expected to have 1 dimension, got onnxruntime::contrib::Attention::Compute4D mask in attention cpu kernel is not supportedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttentionGridSamplegrid_dims[3] == 2Last dimension of grid: grid_dims[0] == NGrid batch size , expect 2onnxruntime::contrib::GridSample::Computepadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"padding_mode " does not match input batch size Only 4-D tensor is supportedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\grid_sample.cconnxruntime::contrib::GridSample::GridSample" not supported, expect zeros, border or reflectionreflection" not supported, expect bilinear, nearest or bicubicbicubicmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"mode "padding_modeSampleOpQGemm : scale of input a must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : zero point of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsonnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.hQGemmTCTBTYTYZQGemm : scale of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)TAy_scale == nullptr || IsScalarOr1ElementVector(y_scale)QGemm : zero point and scale of input b should have same shape sizeb_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : zero point of y must be null or a scalar or 1D tensor of size 1b_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NIsScalarOr1ElementVector(a_zp)QGemm : scale of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QEmbedLayerNormalizationBeta zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\qembed_layer_norm.cconnxruntime::contrib::QEmbedLayerNorm::Computeinput_shape.Size() > 0 || N == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cconnxruntime::contrib::NhwcMaxPool::ComputeRecurrentWeight point must be constantDynamicQuantizeLSTMR_scaleW_scaleDynamicQuantizeLSTM : Weight zero point must be zero must have shape {} for per-tensor/layer quantization or shape {R_zero_pointW_zero_pointD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePack, 4*} for per-channel quantization. Actual:MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::DynamicQuantizeMatMul::Computeonnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validQAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.cconnxruntime::contrib::QAttention::Computeinput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1QLinearMulQLinearAddD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.ccMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_a_scale)onnxruntime::contrib::`anonymous-namespace'::QLinearImplMatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1QLinearSigmoidQLinearLeakyReluIsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if giventensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearAveragePool::ComputeInput x_scale must be a scalar or 1D tensor of size 1QLinear Pooling unsupported pooling size!QLinearAveragePooltensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)Unsupported 'dtype' in QLinear Pooling:Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Input scale is not float for input def @tensor_x_scale->IsDataType()onnxruntime::contrib::QLinearConcat::QLinearConcatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_concat.ccAt least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!input_def_count >= 8 && (input_def_count - 2) % 3 == 0QLinearConcatInput scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @input_count_x3 >= 6 && input_count_x3 % 3 == 0onnxruntime::contrib::QLinearConcat::ComputeQLinearGlobalAveragePoolIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_global_average_pool.cconnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptr(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0X_rank == 4onnxruntime::contrib::ReorderInput::Computetransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode '' for NCHWc UpsampleUnsupported transformation mode 'scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2invalid channel countchannels_ > 0info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputonnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::NchwcUpsample::Compute(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4output and sum shape must matchonnxruntime::contrib::NchwcPoolBase::NchwcPoolInput 1 dimension 0 should have same length as the last dimension of input 0Input 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got rows[.. row[ [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_not support normalize yet.onnxruntime::contrib::BahdanauAttention::PrepareMemoryactivation_params count mismatchunimplemented activation: beta is expected to have size of beta is expected to have 1 dimensions, got gamma is expected to have size of gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapeinput_ids and position_ids shall have same shapeQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1Received invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionsD:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setD:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPositiondims[d_i] < d_maxOrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled already is not supportedCudaCudaPinnedDMLOpenVINO_GPUonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccSpecified device is not supported.Not implementedthe ort_value must contain a constructed tensor or sparse tensorArgument is not a tensorOrtApis::GetTensorTypeAndShapeD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccUnsupported indices_format passed`anonymous-namespace'::GetIndicesTensortype_proto is not of type sequence!type_proto is not of type map!p_type != nullptronnxruntime::Tensor::TensorD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0tensor failed memory size calculationtensor size overflowonnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensorD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatibleutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainer(null)floatbooldoubleint8uint8int16expected a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleuint16int32uint32int64uint64float16bfloat16(unknown type)tensor type sparse tensor type is not currently registered or supportedMLDataType for: Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerValues size onnxruntime::SparseTensor::AllocateBufferSparseTensor Allocation failed for size: Must contain Coo format. Got: Format() == SparseFormat::kCooonnxruntime::SparseTensor::AsCooExpecting to contain one index, got: format_data_.size() == 1U must be equal to or twice the values size: Index size: values_count == index_sizeonnxruntime::SparseTensor::GetCooIndexDimsSparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesNot expecting an allocator setUse MakeCooStringsonnxruntime::SparseTensor::MakeCooDataExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooStringsFormat() == SparseFormat::kUndefinedThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptrMust contain Csr format. Contains: Format() == SparseFormat::kCsrconnxruntime::SparseTensor::AsCsrExpecting two indices. Got: format_data_.size() == 2Udense shape must 2-D. Got: onnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zero the same as values size: Expecting inner index size: Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZero dst_size: Must have the same size. Got src_size: onnxruntime::`anonymous-namespace'::CopyDataD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.ccthe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValuethis tensor already has populated sparse_indices must be less than total buffer size: rows: Outer index count must be rows + 1 or zero. Got: This method does not expect allocator to be setonnxruntime::SparseTensor::UseCsrIndicesUse MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataonnxruntime::SparseTensor::MakeCsrStringsMust contain BlockSparse format. Got: Format() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::AsBlockSparseExpecting one index. Got: Expecting to have at lest 3-D shape. Got:onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2 to be equal to values blocks: Expecting index blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::UseBlockSparseIndicesUse MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataonnxruntime::SparseTensor::MakeBlockSparseStrings to device type: Unable to find a data transfer for copying from device type: onnxruntime::SparseTensor::CopyThis instance should not be emptyDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeMust have the same shape dimensions.Invalid dimension of for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeToDimensiondimension <= num_dimsonnxruntime::TensorShape::SizeFromDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccdimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.{onnxruntime::TensorShape::Slice}Config key is empty or longer than maximum length 128]. It will be overwrittenConfig value is longer than maximum length 1024Config with key [] already exists with value [D:\a\_work\1\s\onnxruntime\core\framework\config_options.cconnxruntime::ConfigOptions::AddConfigEntryReceived nullptr for OrtValue.Received nullptr for name.Buffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.An OrtValue for this name has already been added.Attibute name and type don't matchNo attribute with this name is defined. is defined. but is of type: No attribute with name: Attribute: expected to be of type: is expected to have type: Requested attribute: Execution frame was nullonnxruntime::OpKernelContext::OpKernelContextframe != nullptrOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OutputMLValuekernel != nullptrInvalid arg_num of . Num args is onnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundonnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.hcannot find allocatorD:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfoonnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0D:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cc kernel start version: kernel_end_version: Version mismatch. node_version: Op with name ( and type ( However the types are incompatible. This op has been implemented only for the following types ( in the supported version range (node_version: ),Found kernel for Op with name (onnxruntime::KernelRegistry::TryCreateKernel but the node in the model has the following type ( Encountered following errors: (D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.ccKernel not found kernel is not supported in : Conflicting with a registered kernel with op versions.kernel def can't be NULL: Conflict with existing kernel def hash.Failed to add kernel for onnxruntime::KernelRegistry::Registeronnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()Candidate for fallback CPU execution: D:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cconnxruntime::GetCpuPreferredNodes::::operator ()onnxruntime::GetCpuPreferredNodeskernel_info != nullptr because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeORT optimization- Force fallback to CPU execution for node: type: data overflowonnxruntime::utils::GetFileContentcorrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protoTensorProto type onnxruntime::utils::TensorProtoToTensorTensorProtoToTensor() tensor shape mismatch! can not be writen into Tensor type Initialized tensor with unexpected type: TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not have raw datatensor can't contain negative dimsThe preallocated buffer is too small. Requires onnxruntime::utils::TensorProtoToMLValuestring tensor can not use pre-allocated buffer, Got Unsupported attribute value type of Sparse Indices raw data size does not match expected.onnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Sparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensoronnxruntime::utils::CopySparseDataSparse indices int64 data size does not match expectedindices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == &*indices_data.cend()Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64 is not supported.Element_size of: Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::SparseTensorProtoToDenseTensorProto`anonymous-namespace'::GetExternalDataInfoD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccUnpackTensor: the pre-allocated size does not match the raw data size, expected Tensor does not have external data to read from.`anonymous-namespace'::ReadExternalDataForTensornullptr == p_dataExternal data type cannot be UNDEFINED or STRING.TensorProto external data size mismatch. Computed size: ) in proto) does not match the data size(onnxruntime::utils::UnpackTensorWithExternalDataImplInvalid TensorProtoHasDataType(dense_proto)onnxruntime::utils::DenseTensorToSparseTensorProtoUnsupported sparse tensor data type of Must have a valid data typeUnsupported type: data_type: onnxruntime::utils::UnpackInitializerDataReceived invalid value of arena_extend_strategy duplicated allocatoronnxruntime::AllocatorManager::InsertAllocatoronnxruntime::CreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cconnxruntime::IExecutionProvider::InsertAllocatoronnxruntime::IExecutionProvider::TryInsertAllocatorIExecutionProvider::Compile with fused Node is not implemented by D:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccduplicated allocator: IExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by onnxruntime::IExecutionProvider::GenerateMetaDefIdUnable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::DenseTensorToSparseCsrCurrently do not support dims higher than 2 dimensions: Support 2-D matrices onlyInput must be of CSR formatD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccUnsupported element size: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::SparseCsrToDenseTensorUnable to convert strings tensor to a sparse tensor that is not on CPUExpecting inner indices to be same as nnz. Got: Input must be of COO formatExpecting indices to be equal the number of values or be twice as manyOuter indices must be M + 1. Got: outer_num == (rows + 1)Invalid index: onnxruntime::sparse_utils::DenseTensorToSparseCooonnxruntime::sparse_utils::SparseCooToDenseTensor > dense_size: There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsTensor size mismatch to D:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsdata_transfer registered is nullptr.InternalTestingExecutionProviderinvalid allocator.Unsupported OrtValue type.onnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccFailed to find allocator for device allocator != nullptrexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedUnsupported OrtValue type to copy between device.onnxruntime::utils::FindMemoryInfoForValuefeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfocopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphsession.disable_prepackingonnxruntime::SessionState::SetupAllocatorsD:\a\_work\1\s\onnxruntime\core\framework\session_state.cc. Ignoring allocator from Allocator already registered for Done saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfoCould not find OrtValue with idx 'onnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::GetSubGraphSessionStatesOrtFormatnode_index_info_onnxruntime::SessionState::GetNodeIndexInfosubgraphonnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::SaveToOrtFormatMain Graph instance should have populated all subgraphs when being resolved.. Invalid ORT format model.Can't find node with index onnxruntime::SessionState::LoadFromOrtFormat::::operator ()onnxruntime::SessionState::LoadFromOrtFormatonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()Unable to find compiled kernel hash for node 'onnxruntime::SessionState::FinalizeSessionStateonnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::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:' Attribute: Index:entry != node_to_subgraph_ss.second.cend()p_op_kernel. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:entry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfo doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node onnxruntime::KernelUseSharedPrePackedBuffersonnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weights+allocator_for_caching.get() != nullptr!op_type.empty() which is of op type: weights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be emptyUnable to write the provided PrePackedWeights instance into the container) and node used in the node: Using cached version of pre-packed weight for constant initializer: Failed to find input name in the mapping: Only one node should produce an output. Existing entry for is used by node Using an input in multiple nodes on different devices is not supported currently. Input: for attribute output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMappingonnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()Kernel create info node indices are null. Invalid ORT format model.Kernel create info hashes are null. 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.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. is missing. Invalid ORT format model.Subgraph SessionState for found duplicated provider ). in KernelRegistryManagerFailed to find kernel for (node The node is not placed on any Execution Provider. onnxruntime::utils::GetMLDataTypeD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccnullptr != type_protoError mapping feeds: D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: compute_info_->create_state_func(&context, &func_state_) == 0D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernelonnxruntime::PartitionOnnxFormatModelImplD:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PlaceNode1 == capability.nodes.size()onnxruntime::InlineNodes did not return correct number of compiled functionsfusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll.EP: has Compile error: onnxruntime::PartitionOrtFormatModelImplonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::GraphPartitioner::PartitionOrtFormatModelExisting entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model.single_node_compute_func should have 1 elementscompiled_kernel_hashes != nullptrCompiled kernel hashes must be providedonnxruntime::GraphPartitioner::PartitionNo provider specified.onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionentry != regions_.end()Could not find Region for: p_int < base_int + memory_size_ memory limit: arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for Creating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: Creating bins of max chunk size D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaBinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)hipMalloccudaMalloconnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()Available memory of is smaller than requested bytes of onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.onnxruntime::BFCArena::ExtendExtended allocation by bytes.Failed to allocate memory for requested buffer of size 0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_intD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegiononnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinonnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in bin. Allocated memory at Total allocated bytes: reserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::ReserveReserving memory in BFCArena for size: tried to allocate 0 bytesh != kInvalidChunkHandle. bin_num: (requested) num_bytes: (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternalFailed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for onnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtr!chunk->in_use()onnxruntime::BFCArena::Shrink BFC Arena shrunk by bytes. The total allocated bytes is now c2->prev == h1onnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::DeallocateRawInternalCaught exception while loading custom ops with message: Failed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoaderUnloading DSO onnxruntime::ExLibLoader::LoadExternalLibA dso with name has already been loaded.D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: node_index_info and ort_value_idx_map are out of sync and cannot be used Requested shape:D:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()shape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape:fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()invalid index Allocation of memory pattern buffer for failed. Error:Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of returned nullptronnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::{ctor}::::operator ()ort_value.Fence() == nullptrTensor shape cannot contain any negative valueTrying to allocate memory for unused optional inputs/outputs, block in memory pattern size is: but the actually size is: , fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperonnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueonnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.For ort_value with index: ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::AllocateSparseTensormlvalue.Fence() == nullptrInvalid allocation kind: Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanonnxruntime::ExecutionFrame::VerifyOutputSizesExpected shape from model of does not match actual shape of for output failed: onnxruntime::ExecutionFrame::GetAllocationPlanort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx= size=Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx= failedlocationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infomodel format error!checksumlengthparsing model format error! Missing 'location'D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Createort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was Insufficient dimensions to slice on . Shape:ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensiononnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iterator not found.Can't use func with null ptrfunc info for node: already exist.D:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncsD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.cconnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchonnxruntime::ParallelExecutor::ExecuteMultiple errors were found.D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalonnxruntime::ParallelExecutor::RunNodeAsyncBegin executionParallelExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.cc_fence_beforeop_nameGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.providerNon-zero status code returned while running node. Name:'' Status Message: Unknown exception was caught by catch-all handler._fence_after_kernel_timethread_scheduling_statsException running nodes starting at '. entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.onnxruntime::SequentialExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.ccgraph_indexSequentialExecutor::Executeonnxruntime::ReleaseNodeMLValuesexec_plan_indexactivation_sizeparameter_sizeoutput_sizeonnxruntime::AllocPlanPerValue::ProgramCounter::AddStartD:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.hInvalid 'start'. Value is smaller than previous 'end'.starts_.empty() || start > ends_.back()Previous entry was not terminated.starts_.size() == ends_.size()end >= starts_.back()No matching 'start' entry.starts_.size() == ends_.size() + 1onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndInvalid 'end'. Value is larger than 'start'.onnxruntime::PlannerImpl::GetElementSizeThere is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_maponnxruntime::PlannerImpl::ComputeUseCounts::::operator ()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reusenullptr != tensor_type_baseCan not find the execution provider allocatoronnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the node Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defonnxruntime::PlannerImpl::ComputeUseCounts!IsNonTensor(*node_output)Invalid program_counter entries at index entry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::VerifyMemoryTimeSchedulespecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::ComputeReusePlanOnly tensors are supported for external outputs for now.AllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::CreatePlan : ) SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cconnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::GetKernelCreateInfoonnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCountbuffers_.size() == buffer_sizes_.size()onnxruntime::PrePackedWeights::GetHashD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.ccsession.use_device_allocator_for_initializersFailed memory size calculationstring tensor is not supported for copying between allocatorsFailed to copy tensor to ) is different from what is supplied () because the ORT planned memory location device DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::DeserializeTensorProtoD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.ccOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsDone saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( failed.Deserialize tensor is not used by any node. input with name SubgraphGraphD:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorcurrent <= buffer_size_!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.hCan't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source= Dimension=NoOpConstantALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.honnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:input index: onnxruntime::Node::LoadEdgesFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for graph_proto != nullptronnxruntime::Graph::Graph' Model is invalid.Duplicate constant node sparse initializer name: 'input edgesoutput edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be null' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: '. Please, fix your model.Initializer Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoby either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Output:Source and target must both be tensors , or optional typed entitiesError merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccTensor element type mismatch. , or sparse tensors. Falling back to lenient merge. target:' source:Optional Type mismatch. Expected: ] op_type [Node [Serialization of fused function body is not currently supported, Input=Type mismatch. Current=onnxruntime::NodeArg::UpdateTypeAndShape . Got: node_arg_name cannot be null], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatdoes not have the graph for key onnxruntime::Node::SaveToOrtFormatfbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty() inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:) input arg (This is an invalid model. Node () is invalid.) in node (onnxruntime::Graph::UpdateShapeInference) Op (Node () does not have type information set by parent node.) bound to different types () of Optype (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) of operator (' of input parameter (This is an invalid model. Type Error: Type ' in node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input () type inference failed) output arg () does not match expected type () of node (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.Graph ctor should have created NodeArg for initializer. Missing:node_arg) does not exist in the graph.This is an invalid model. Graph output (Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch 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.Invalid model. Node input 'Argument mismatch when removing edge.onnxruntime::Graph::BuildConnections 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=Graph attribute inferencing failed: in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs.Some nodes are not included in the topological sort, graph have a cycle.onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.sparse_tensor_names_.count(tensor_name) == 0onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!found_token_onnxruntime::Graph::SaveToOrtFormat as it still has output edges.Failed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProtoCan't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodeinitializer_node_arg != nullptronnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is not used by any node and should be removed from the model.Removing initializer ''was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternalCannot find NodeArgs for [Type Error: Shape of initializer , Error This is an invalid model. In Node, onnxruntime::Graph::VerifyNodeAndOpMatch but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. . Execution will fail if ORT does not have a specialized kernel for this op is not a registered function/opFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphs. Error message ' optype Function body initialization failed for node 'onnxruntime::Graph::InitFunctionBodyForNodeAddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorsparse_tensor_names_ not in sync with name_to_initial_tensor_onnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.index < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.hnodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != func_meta_defonnxruntime::Graph::CreateFusedSubGraphNode'. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg ' must be either specified in graph inputs or graph initializers.Input to set must exist.input->Exists()onnxruntime::Graph::SetInputsonnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::InlineFunction) -> (") : (("""Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Paonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cc, but it its domain is notknown by the checker., but it its version is higherthan the operator set version Mismatch between Graph and IndexedSubGraph. Output not found:nodeargnodeMismatch between Graph and IndexedSubGraph. Node not found: onnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GetRootNodesonnxruntime::GraphViewer::GetNodesInTopologicalOrderInvalid ExecutionOrderfilter_info_ == nullptrNot supported with filtered graph.IndexedSubGraph contains values not present in the Graph 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 ModelProto does not have a graph.D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::Modelonnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with 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.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.Unsupported model IR version: , max supported IR version: Protobuf parsing failed. less than 0.Failed to load model with error: onnxruntime::Model::LoadNo graph was found in the protobuf.onnxruntime::Model::SaveToOrtFormatonnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::Save is less than 0.Protobuf serialization failed.Graph is null. Invalid ORT format model.Load model failed. File doesn't existsystem error number onnxruntime::SaveModelCAttribute is null expected to have tensor or sparse tensor type: but has rank Incompatible dimensionsDimension mismatch in unification between expected to have rank expected to have tensor type expected to have tensor or sparse typeNegative values are not allowed in a shape specification should specify a shape expected to have rank >Attribute expected to have tensor or sparse tensor typeInputs 4 shall be 5 dimensionsqkv_hidden_sizes should have 3 elementskey and value cache dimensions value shall not be nullkey and value cache shall be 4 dimensionsIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).Invalid bias shapeInputs 0 shall be 3 dimensionsba T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cconeX_bias = Identity (X)X_bias = Add (X, bias)weightMWhether every token can only attend to previous tokens. Default value is 0.Number of attention headsHidden layer sizes of Q, K, V paths in AttentionError unexpected extra input in node:Error parsing node:'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64 = Constant()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.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.input_ids shall be 2 dimensionsPads has incorrect number of valuesword_embedding should have 2 dimensions and dimension size is known.segment_ids input shall be 2 dimensionsquerykeyglobalDecoderAttentionConstrain to integer typeskey_padding_maskkey_cacheq_weightkv_weightuse_pasthas_layer_statevalue_cachestatic_kvConstrain input and output types to float and float16 tensors.new_value_cacheThe epsilon value to use to avoid division by zero.Constrain key_padding_mask to bool tensors.has_key_padding_masknew_key_cacheextra_addpresentpastinput_scaleweight_scaleConstrain mask index to integer typesConstrain input and output types to float tensors.Constrain input and output types to int8 tensors.weight_zero_pointOne sided attention windows length W, or half of total window lengthLongformerAttentioninput_zero_pointglobal_weightmaskGglobal_biaswindowposition_embedding_zero_pointsegment_embedding_zero_pointbeta_scaleword_embedding_zero_pointlayernorm_outmask_index_outgamma_zero_pointbeta_zero_pointConstrain input and output types to float or half tensors.input tensorConstrain input and output types to float32 tensors.output tensorSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationConstrain mean and inv_std_var to float tensors.inv_std_varskipword_embeddingposition_embeddinginput_idssegment_idsposition_idssegment_embeddinggamma_quantposition_embedding_quantbeta_quantConstrain input and output integer tensors typesembedding_sumword_embedding_quantConstrain input and output float tensors types.segment_embedding_scalegamma_scaleword_embedding_scaleposition_embedding_scale) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border (Input's shape must be 4-D GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. 'Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 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: 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.) + scale[1] () + scale[0] (Second input does not have rank 2First input does not have rank 2rois input tensor has wrong dimensionfirst input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionboth 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 '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 tensorAxis1D = Constant()Zero1D = Constant()NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)Epsilonstash_typeRank = Size (XShape)XShape = Shape (X)scores_outConstrain scores input and output types to float tensors.Constrain indices to integer typesThe NGram size.scorescur_tokensprev_suffix_match_idxThe maximum NGram size for suffix matching.The minimum NGram size for suffix matching.src_tokenssuffix_match_idx 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 to integer types.pred_tokenstokensinput_as_shape Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. extra_shapeScale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided.!(isinf_only && isnan_only)Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions. 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). onnxruntime::contrib::RegisterContribSchemas::::operator () 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. isnan_onlyisinf_onlyThe scale to apply.Bias applied to each channel, same size as C.Result, has same shape and type as inputInput tensor of shape [N,C,H,W]Value of betaValue of alpha1D output tensor1D input tensorOutput tensorInput tensordataTensor of data to extract slices from.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Threshold valueResult, has same type as input, with H and W dimensions reduced.GivenTensorFillConstrain input and output types to all tensor types.The filled tensorThe shape of filled tensor1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.Mean2D = ReduceMean (XU)XU = Cast (X2D)MeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)Scale2D = Flatten (Scale)NormalizedT = Cast (Normalized)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)B2D = Flatten (B)Scaled = Mul (NormalizedT, Scale2D)Biased = Identity (Scaled)Biased = Add (Scaled, B2D)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. The inner-most 2 dimensions must have the same size (mat_w: != mat_h:Gaussian Error Linear Unit. A high-performing neural network activation function.The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs. CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) One The fused convolution operator schema is the same as Conv besides it includes an attribute activation.Constrain input and output types to float tensors The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.ZComplexMulConjinput_1PadsWWhether A should be transposedConstrain input and output types to float/int tensors.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedInput 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.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Tokenized stringsStrings to tokenizeBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensoractivation_gammaScalar multiplier for input tensor C.ExpandDims echo operator.Specified axis to insert a dimensionN-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.Matrix multiply results from A * BN-dimensional matrix BAn 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.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.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.Input data to be scaledvaluesGRUUnitOutput data after scalingThe new GRU hidden state calculated by this op.The timestep for this operation.Perform mean variance normalization.hiddengatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N. Sample echo operator.Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain input0 and output types to float tensorsFor internal use.If 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.The scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueComplexMulIrfftAinput_0signal_ndimRfftonesidednormalizedConstrain to tensor(int32).Specify embedding vector of charThe WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).SequenceSpecify batchs of sequence words to embeddingSpecify bias of convSpecify weights of conv 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.xA 1-D input tensor that is to be processed.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 array(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 can be of any tensor type.countsThe 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] A 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'A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxValue used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.RoIs (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.2D matrix with shape (K,N)2D matrix with shape (M,N)Constrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplication2-dimensional sparse matrix A. Either COO or CSR formatMatrix multiply resultsConstrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorScalar 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).sparse_tensor(uint64)sparse_tensor(int32)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)sparse_tensor(float)N-dimensional dense matrix Bsparse_tensor(int64)sparse_tensor(double)If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Tensor of rank q >= 1.Tensor of rank r >= 1.32-bit hash value.An input tensor to hash.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.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]]] Integer 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.Tensor of rank q-1+r-indices[-1].indicesConstrain indice type to int32 or int64Constrain input and output types to any tensor type.Input tensor of rank 2 or higher.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Constrain input and output types to all numeric tensors and bool tensors.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.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``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.(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 residual input, must have the same shape as dataThe bias input, a vector with the same shape as last dim of data OR same shape with dataapply softmax to elements for dimensions softmax_axis or higherY = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.The bias (or mask) as Tensor.broadcast bias across input for dimensions broadcast_axis to softmax_axis-1Constrain input 'ratio' types to float tensors.The output mask of dropout.IsAllFiniteConstrain output 'mask' types to boolean tensors.The 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.residualIf 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.ratioThree interpolation modes: bilinear (default), nearest and bicubic.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.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.If true, check only for NaN.If true, check only for Inf, -Inf.Input tensors to check.Constrain the output to a boolean tensor.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_sizeConstrain types to int tensors.Constrain types to float tensors.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.rois1-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_indicesBias tensor.Scale tensor.Saved inverse standard deviation used during training to speed up gradient computation.Output data tensor.The 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.Input data tensor from the previous layer.type used for stash mean/inv_std_varThe input data as Tensor.Constrain mean and inv_std_var to be float tensors.The normal input data.The output.Constrain input types and output Y type to float tensors.InvStdDevConstrain input and output types (except mean and inv_std_var) to float tensors.Type of Mean and InvStdDev tensors.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 sizepadding_idxA 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.Input tensor. Every matrix in the batch must be invertible.The bias input data that is a 1D tensor.TorchEmbeddingOutput tensor of the same type and shape as the input tensor.Constrain output types to float tensors.Constrain input types to all tensor types.Input 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.4-D tensor of shape (N, C, H_out, W_out).Grid̼+̼+!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name: ImplicitInputs:onnxruntime::graph_utils::AddInitializeronnxruntime::graph_utils::GetIndexFromNameitr != node_args.end()onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.num_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::AddNodeInput. Index: ExplicitInputs:onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameonnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::FindPathFailed since multiple edges matched:index >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 in one of the subgraphs.D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Attempting to get index by a name which does not exist:for node: Implicit input name cannot be safely updated to Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsTENSORGRAPHSTRINGINTSFLOATSUNDEFINEDINTFLOATSPARSE_TENSORSTENSORSSTRINGSSPARSE_TENSORGRAPHSNull string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.onnxruntime::fbs::utils::SaveInitializerOrtFormatonnxruntime::fbs::utils::SaveSparseInitializerOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.ccMissing string data for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Graph attribute value was null. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatMissing dimensions for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Null string attribute. Invalid ORT format model.Missing dims for sparse initializer: Null tensor attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing indicies for sparse initializer: Invalid ORT format model.hbxbtbPb@bHb`bXbbbbbbResolve subgraph failed:_dummynode_in_parent_graph->InputDefs().size() == function_body_graph.GetInputsIncludingInitializers().size()'s number of inputs is different from function body graph's number of input.onnxruntime::CreateSchemainput_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImplfused_function_subgraphnode_in_parent_graph->OutputDefs().size() == function_body_graph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.D:\a\_work\1\s\onnxruntime\core/graph/function_impl.honnxruntime::ViewerFunctionImpl::Body is out of bounds.onnxruntime::ViewerFunctionImpl::MutableBodyNot supported referenced by function body node optype Cannot infer type and shape for function. No opset import for domain does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.D:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::IOTypeConstraintHelperTin in function opset imports.domain_version != -1No opset registered for domain onnxruntime::InitNestedModelLocalFunctionFunction body initialization failed for Function 'onnxruntime::UpdateSubgraphsWithinFunctionBodyA node with a function body within a subgraph within another function body is currently not supported in ORTToutout'. Error message Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}inputs are expected to have tensor type.Scale and Zero-point must be a scalarInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is A_zero_pointA_scale{additionalDocumentation}C_zero_pointC_scaleB_zero_pointB_scaleAll inputs to Concat must have same rankD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccRequired attribute axis is missingaxis must be in [-rank, rank)axis must be in [-rank, rank-1]. input rank was Constrain input and output types to 8 bit signed and unsigned tensors.a_scaleConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.b_zero_pointb_scaleC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionConstrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.a_zero_pointreduced_scalereduced_zero_pointdata_zero_pointdata_scaleQLinearReduceMeanY_scaleX_zero_pointX_scaleCoefficient of leakage.Constrain 'x', 'y_scale' to float tensors.y_zero_pointy_scaleThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.x_zero_pointx_scaleConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).reducedConstrain 'y', 'x_scale' to float tensors.ReduceSumIntegerConstrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.MulIntegerA 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.T8Constrain weights types to 8 bit tensors.Which axis to concat onY_cConstrain seq_lens to integer tensor.Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).inputsTVConstrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerConstrain input and output types to 8 bit tensors.Y_zero_pointRCouple the input and forget gates if 1.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Pinitial_cinitial_hsequence_lensY_hUnsupported type:Constrain input and output types.Can not get shape initializer data!deltalimitUnsupported non-raw-data data type!Output tensor must have at least 2 dimensionsinput and zero_point pair is expected to have be same type.Input tensor must have at least 2 dimensionsw_zero_pointww_scaleauto_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.ccweight 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.Constrain input and output types to singed/unsigned int8 tensors.Works on NHWC layout or not? Default not.Stride 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.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.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales dimensioninvalid scales valuetensor rank too smallNumber of neurons in the hidden layer.Constrain seq_lens to integral tensors.Couple the input and forget gates if 1, default 0.MWQWAWmemory_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?@@@QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!ElementSize must be power of 2 and less or equal than 16!??    FAILSUCCESSINVALID_GRAPHNOT_IMPLEMENTEDMODEL_LOADEDINVALID_PROTOBUFRUNTIME_EXCEPTIONENGINE_ERRORNO_MODELNO_SUCHFILESystemErrorD:\a\_work\1\s\onnxruntime\core\common\status.cconnxruntime::common::Status::Statuscode != static_cast(common::OK)GENERAL ERROREP_FAIL[ONNXRuntimeError](caller: %p) %hs!%p: %hs(%u)\%hs!%p: FailFastLogHrReturnHrExceptionkernelbase.dll [%hs] [%hs(%hs)] CallContext:[%hs] Msg:[%ws] %hs(%d) tid(%x) %08X %ws%wsRaiseFailFastExceptionsystemgenericonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameFailed to find symbol in library, error code: FreeLibrary failed with error LoadLibrary failed with error "" when trying to load "UNC\onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathD:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDir\*Fatal error: 0 count processors from GetSystemInfoonnxruntimeSetThreadDescriptionkernel32.dllD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cconnxruntime::`anonymous-namespace'::WindowsThread::WindowsThreadcustom_create_thread_fn returned invalid handle./\Invalid fd was supplied: GetFileSizeEx open file fail, errcode = - Fatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresReadFile SetFilePointerEx length > buffer.size()offset < 0onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferfile_path == nullptrReceived negative size from stat callGetFinalPathNameByHandle() failed: RemoveDirectory() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: , error code: \/MapFileIntoMemory is not implemented on Windows. fail: unexpected endunknown errorISink must be provided.onnxruntimeonnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. Only 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::DefaultD:\a\_work\1\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerFailed to parse path root: onnxruntime::Path::ParseD:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRoot/\!current_parallel_sectionNested parallelism not supported": {"num_run": "core": UnknownEventWaitRevokeWaitD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParalleln <= num_threads_+1More work items than threadsonnxruntime::concurrency::ThreadPool::ParallelForn >= 0onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectiononnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionunnamed_thread_pool"thread_pool_name": "", }, "sub_threads": {}}D:\a\_work\1\s\onnxruntime\core\common\threadpool.cconnxruntime::concurrency::ThreadPoolProfiler::Stopenabled_Profiler not started yetpoints_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd!points_.empty()LogStart must pair with LogEnd{"main_thread": {RunDistributionEnqueueDistribution": ], "core": "thread_id": "", "block_size": [onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetlength overflowonnxruntime::ToMBStringD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideString" : "} }, ] "pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : SessionNodeKernelMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ ",{"cat" : "onnxruntime::profiling::Profiler::StartD:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::Initialize@bHbPbunexpected failureillegal input path:EventSetInformationapi-ms-win-eventing-provider-l1-1-0.dlladvapi32.dll [ is not a valid dateampm is not a valid year is not a valid daykernel32.dllGetSystemTimePreciseAsFileTimesequence_typemap_typetensor_typeonnxruntime::fbs::utils::SaveValueInfoOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for dim_param value with no name. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatNull entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveMapTypeOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveTypeInfoOrtFormat] for nowWe do not support type [ is not supported currentlyType:Null type info for onnxruntime::fbs::utils::LoadValueInfoOrtFormatModel must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatopset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.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.The output is a 1-D tensor of string, float, or integer.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'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The input must be a tensor of strings or integers, either [N,C] or [C].The input type must be a tensor of a numeric type.The size of each input in the input listThe input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, int64)map(int64, double)map(string, float)map(string, double)The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.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, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.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.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.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.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'Child node if expression is true.Child node if expression is false.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.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.One of 'MAX,' 'L1,' 'L2'Indicates whether to do OvR or multinomial (0=OvR is the default).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.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.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.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.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.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor 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 output will be a sequence of string or integer maps to float.seq(map(string, float))seq(map(int64, float))The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccThe input type must be a tensor of integers or strings, of any shape.type: does not match type of output: Input: expected to have tensor or sparse tensor type and Output not specifiedValue of attribute should be of integer type and specify a type.optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64))) does not specify a valid type.axis must be in [-rank, rank-1].Constrain output types. Casting to complex is not supported.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.cctarget_typeTarget shape may not have multiple -1 dimensions.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain input types. Casting from complex is not supported.(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.Input tensor can be of arbitrary type.Constrain output to int64 tensor.Invalid position of 0.Invalid dimension value: Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapes(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.reshapedoptional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(tensor(complex64))optional(tensor(complex128))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).updatesInput and output types can be of any tensor type.nonestepsInvalid attribute perm {}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedList 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).Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.reductiondata tensor must have rank >= 1axis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Invalid value of attribute 'axis'. Rank=) and outputs (Mismatch between number of splits () and the split dimension of the input (Mismatch between the sum of 'split' (The input is not evenly splittableConstrain 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)..concat_resultConstrain output types to any tensor type. Value=Input axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data) vs (Input rank for starts and ends should be the same: (outputsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endssizesConstrain roi type to float or double.Constrain input and output types to all tensor, sequence, and optional types. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.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".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.roiOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Indices tensor must have rank >= 1'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).conditionConstrains to boolean tensors.Blocksize must be positiveInput tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.squeezed'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).expandedConstrain repeat's type to int64 tensors.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Constrain input 'X' and output 'Y' to all tensor types.'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.repeatsThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`constant_valueinverse_indicesBoth `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` tensor(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.Constrain to boolean tensors.depthConstrain to any tensor type.Constrain input types to float tensors.Constrain output types to boolean tensors.Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) 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).Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.'input' must have rank >= 2'sequence_lens' must have rank of 1(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.) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified ( was not a tensor.Scan input outputs. Expected Graph attribute inferencing returned type information for is invalid for a tensor of rank axis value condGraph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence, and optional typesOnly bool was notScan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. but subgraphs produce If node has was Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output Loop 'body' subgraph scan outputs should all be tensors but output 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.All Tensor and Sequence typestensor of int64, which should be a scalar.tensor of bool, which should be a scalar.initial_state_and_scan_inputsD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccv_initialv_final_and_scan_outputsAll Tensor typesfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Int64 tensordefault 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccMultiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. score_thresholdselected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.boxesmax_output_boxes_per_classiou_thresholdInput type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.Type of the element in the optional outputConstrains input type to all tensor and sequence types.Constrains output type to all optional tensor or optional sequence types.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\optional\defs.ccOptional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Input must be an optional-type value containing an element with type information.Constrain output type to all tensor or sequence types.OptionalHasElement is expected to have 1 input.OptionalHasElement is expected to have 1 output.Constrains input type to optional tensor and optional sequence types.Constrains output to a boolean tensor.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 [D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentCoefficient 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).Coefficient of ELU.X_alphaElu_ResultThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Wrong op_type name for running propagation: broadcasting: (Invalid rank for X_SubX_ExpX_ReduceSumX_LogIndicesConstrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.K input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.KValuesConstrain input X and output types to float/int tensors.Constrain input Y 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. Constrain input and output types to numeric tensors.sumX_ReduceMaxValue of alpha.Value of beta. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } List of tensors for data_0Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Constrain input a and its zero point data type to 8-bit integer tensor.const_zeroconst_onetargetexpanded_targetignore_indexinput_gather_elementloss_NCddIf set to 1 will perform the sums in reverse direction.axis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.'shape' input must be 1D tensor of type INT64Constrain input and output types to all tensors.input and zero_point pair is expected to have same type.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.Shape3DX_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeEllipsis represents incompatible dimensions. does not match the equation indices.Rank of input Einsum expression string.InputsConstrain input and output types to all numerical tensor types.log_problabelsSoftmaxCrossEntropyLossconst_zero_target_typedexpanded_target_int64transform_targetsconst_zero_floatconst_zero_castedinput_gather_element_transformsqueeze_maskconst_one_floatloss_N1ddlossloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_ignore_indexType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.const_one_castedweight_gather_tempweight_gather_temp_1Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Constrain output types to be numerics.Constrain input types.Input tensor must be 2-dimensional(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.The 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.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccShape input must be a one-dimensional 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: The shape of the output tensor.The mean of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.Lower boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Upper boundary of the output values.sparse_valuevalue_intsvalue_intvalue_floatsvalue_float expected to have: or UNDEFINED. Got: Attribute expected to have a one-dim sparse tensorAttribute expected to have a one-dim tensorTypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.The value for the elements of the output tensor in sparse format.The value for the elements of the 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.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.value_stringsvalue_stringAttribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_float' expect a float.Attribute 'value_ints' expect a list of integers.Attribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)Constrain output types to integral tensors.All inputs to 'Range' op must be of the same type(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor must have rank 2Output type must be int32 or int64(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Constrain output types to all numeric tensors and bool tensors.BernoulliConstrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } X_greaterX_randomThe data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.'output_shape' must have same number of elements as the shape of input tensor X.'output_shape' must be rank 1 tensor.p value of the Lp norm used to pool over the input data.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Input tensor X must have atleast 2 dimensions.MaxUnpool op must have either two or three inputs.Attribute strides has incorrect size.Attribute pads has incorrect size.Attribute kernel_shape must be specified.Attribute kernel_shape has incorrect size.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.Attribute pooled_shape has incorrect lengthRoIs tensor must have 2 dimensionsROI pool output shape (height, width).Attribute pooled_shape must be specifiedMultiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Second input tensor has wrong dimensionAttribute pads has incorrect sizeConstrain input and output types to float and 8 bit tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\defs.ccThe storage order of the tensor. 0 is row major, and 1 is column major.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.Input tensor must have atleast 2 dimensionsauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Attribute strides has incorrect sizeAttribute dilations has incorrect sizeAttribute kernel_shape must be specifiedAttribute kernel_shape has incorrect sizeinput_meaninput_varAdditional 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. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This number of op outputs should be 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.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 axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.running_meanrunning_varConstrain mean and variance types to float tensors.Constrain scale and bias types to float tensors.Constrain output type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain bias type to 32-bit integer tensor.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 to 1 along each axis.Constrain output y data type to 32-bit integer tensor.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.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 padsPadding 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.number of groups input channels and output channels are divided into. default is 1.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.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"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.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.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.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of 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.Input shape must have either [C] or [1,C] dimensions where C > 0Invalid value() for attribute 'axis'Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain input and output to all tensor types.The number of channels to sum overtraining_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Input is ether string UTF-8 or int32/int64Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.1-D tensor of floatsMaximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=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.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.The exponent.Scaling parameter.Constrain input and output types to float tensors.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Invalid position of 0Target shape may not have multiple -1 dimensionsInvalid Target shape product of 0length of each output. Values should be >= 0.'step' cannot be 0D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.ccTwo interpolation modes: nearest(default), bilinearheight_scale) is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.Number of elements of attribute 'scales' must be same as rank of input 'X'Ranks inferred (tilesThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.The scale along height dimension. It takes value greater than or equal to 1.width_scaleEnding indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Attribute 'scales' is required.Attribute 'scales' must have floats type.Incorrect or missing attribute value for starts and endsAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Attribute axes has incorrect lengthConstrains input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.rank must be greater than axisWhich axis to concat on. Default value is 1. 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).One float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgeNew shapeconsumed_inputslegacy optimization attribute.Which axis to split onoutputs...length of each outputpaddingsList 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`.(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.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Which axis to split on. Attribute value for pads is requiredWhich 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 squeeze.List of non-negative integers, indicate the dimensions to be inserted'axis' must be in [-rank(indices)-1, rank(indices)]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 lengthOne float, indicates the value to be filled.Constrain input and output types to all tensor and sequence types.ExponentD:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccEX_squaredX_RME_XsquaredX_squaredauto_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.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.dilation value along each spatial axis of the filter.STDVarianceProcessed_STDX_varianceStride along each spatial axis.Dilation value along each spatial axis of filter.The ratio of random dropoutConstrain output mask types to boolean tensors.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). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.varThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.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. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain mean and variance types to float tensors. It allows all float type for U.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.saved_mean 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_varA 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 When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size]. Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].First input tensor must have rank 3One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\defs.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.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. then= else=Mismatched tensor element type for output Mismatched type for output Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Constrain 'x' to float or int32 tensor.(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.axes as an input and attribute cannot be specified at the same time.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Constrain 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).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\old.ccThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).If set, defines the broadcast dimensions. See doc for details.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01. 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_sizebroadcastPass 1 to enable broadcastingCoefficient of ELU default to 1.0.Coefficient of SELU default to 1.0507.Value of alpha default to 0.2Value of beta default to 0.5Scalar 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.Number of top elements to retrieveInvalid value for attribute kDimension on which to do the sort.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxWhether C should be broadcastedConstrains output to boolean tensor.Constrains input to boolean tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input types to all numeric tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } O1O2Constrains input/output to boolean tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.The 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.(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.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.ccConstrain input types to any tensor type.output_sequenceInput Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=input_sequence 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 Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitpositionConstrain position to integral tensor. It must be a scalar(tensor of empty shape).Input type for input at index 0 is null. Type info is expected.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.], Value=new_axis must be either 0 or 1Which 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=[Insert and concatenate on a new axis or not, default 0 means do not insert new axis. sum of split values=Input 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain split size to integral tensor.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.If set, defines the broadcast dimensions.Constrains input to float tensors.Constrains input to integral tensors.Enable broadcastingD:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\old.ccOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.fowardThe sequence output for the hidden is optional if 0. Default 0.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\old.ccParseData type mismatch for tensor: load external data into raw data for tensor: Actual:. Expected: expected size Data size mismatch. Tensor: Cannot parse data from external tensors. Please does not match the actual size is undefined so it cannot be parsed.The type of tensor: Mismatched sparse tensor element type:source sequence type missing element type.Mismatched type:Mismatched tensor element type:target optional type missing element type.Element type of tensor or sparse tensor input was unknowntarget sequence type missing element type.source optional type missing element type. target= source=Input element type of Output was expected to have tensor type. Got Input was expected to have tensor or sparse tensor type. Got does not match existing output type of Input was expected to have optional type. Got Input was expected to have either tensor, sequence, or optional type. Got Input was expected to have sequence type. Got Element type of input was unknown ==> Context: ' instead of '' should be stored in field ') should not be stored in raw_data fieldSTRING data (tensor name: values of data_type 'Unrecognized data_type (tensor name: ) should be stored in ) is stored externally but doesn't have a location.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.TensorProto (tensor name: ) should contain one and only one value field.TensorProto ( tensor name: ) is 0-element but contains data!) has Sparse tensor indices ( values, but NNZ is Sparse tensor (] not in sorted order.] out of range [0, ) index value at position [ is required to be non-empty.value_infoField ' is required but missing.' of namestring_dataint64_datafloat_dataint32_datauint64_data) is stored externally and should not have data field.raw_datadouble_datavalue_type): elem_typekey_type) to UNDEFINED is not allowedsetting data_type field (tensor name: Unrecognized type value case (value_info name: data_type' has been used as output names multiple times.' of node: Nodes in a graph must be topologically sorted, however input ' initializer name is not unique in initializer but not in graph inputinitTensor initializers must have a non-empty name is not output of any previous nodes.name: Sparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers) must have rank 1 or 2.) has no index values.) dimensions are not positive.) must have INT64 type.) should not contain more than one value field.Attribute (name: attrtype field and data field mismatch in attribute ] out of range.] not in lexicographic sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.Sparse tensor values () must have a dense-rank > 0sparse_tensor_proto) must have rank 1.Op registered for OpType: No Op registered for is deprecated in domain_version of ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'Bad node spec for node. Name: graph) has zero input and zero output., type: ) should refer to attribute in parent node.op_typeNo opset import for domain ' with domain_version of NodeProto (name: Warning: Checker does not support models with experimental ops: ConstantFillinvalid stol argumentinttype_proto column: (line: stringstensorsfloatstype_protosgraphssparse_tensorsstol argument out of rangeUnhandled type: %dUnexpected attribute type.Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Integer value expected, but not found.Unexpected literal type.Expected character Value expected but not found.Unexpected type.Error parsing TensorProto (expected a tensor type).String value expected, but not found.Identifier expected but not found.[ParseError at position Error context: Data for input opaque_typesparse_tensor_typeoptional_type already exists.Warning: Unsupported operator Shape inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set.type case mismatch. existing=type case unsupported. existing=NOT_SET inferred=Warning: Shape inference does not support. No schema registered for this operator.type case unsupported for symbolic shape inference. inferred= models with experimental operators: Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (unk__: (Inferred shape and existing shape differ in dimension (op_type:Cannot find missing input: , node name: Graph has Cannot use the same name as both a subgraph initializer and subgraph input: were provided inputs but Graph initializer names must appear after the actual inputs: in initializers. The number of graph input cannot be smaller than the number of node input were provided.Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.) is not equal to the existing dim value (Dimension value inferred (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.onnx.TypeProto.Opaqueonnx.TypeProtoonnx.TypeProto.Optionalonnx.TypeProto.SparseTensoronnx.OperatorSetIdProtoonnx.FunctionProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TensorProto.Segmentonnx.TensorProtoonnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.NodeProtoonnx.TrainingInfoProtoonnx.AttributeProtoonnx.ValueInfoProtoonnx.TensorAnnotationonnx.GraphProtoonnx.ModelProtoonnx.StringStringEntryProto   FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDInvalid RE2: text size: Error reverse compiling 'RE2: invalid startpos, endpos pair. [Unexpected re_anchor value: endpos: startpos: Unknown encoding D:\a\_work\1\s\cmake\external\re2\re2\re2.ccError compiling 'pattern too large - compile failed': Error parsing 'SearchNFA inconsistencyprogram size pattern length bytemap range list count SearchOnePass inconsistencySearchBitState inconsistencyDFA out of memory: SearchDFA inconsistencyinvalid named capture groupNumCapturesWalker::ShortVisit calledtrailing \no argument for repetition operatormissing )unexpected )invalid perl operatorinvalid UTF-8invalid repetition sizebad repetition operatorno errorunexpected errorBad reference count Unexpected op in Regexp::Equal: invalid character class rangemissing ]invalid escape sequenceinvalid character classRegexp not destroyed.D:\a\_work\1\s\cmake\external\re2\re2\regexp.ccD:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hWalk NULLStack not empty.x%d%d&d(&d%d&d%d%d$d$dH%d`%d %d8%d$dD:\a\_work\1\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!should never happenNo ranges in char classMissing case in Compiler: AddFoldedRange recurses too much.D:\a\_work\1\s\cmake\external\re2\re2\parse.ccAnyBad call to ParseState::ParsePerlFlagsunknown round: RepetitionWalker::ShortVisit calledConcat of Bad hex digit RE2: unexpected op: context does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: in stepD:\a\_work\1\s\cmake\external\re2\re2\nfa.ccUnhandled in AddToThreadqunhandled RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCache|StateSaver failed to restore state.Failed to analyze start state.unhandled opcode: D:\a\_work\1\s\cmake\external\re2\re2\dfa.ccNULL state in RunStateOnByteUnexpected special state in RunStateOnByteDeadState in RunStateOnByteUnexpected opcode: GrowStack() failed: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccjob_.size() = njob_ = D:\a\_work\1\s\cmake\external\re2\re2\onepass.ccCannot use SearchOnePass for unanchored matches.D:\a\_work\1\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: Malformed repeat Simplify case not handled: SimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit calledD:\a\_work\1\s\cmake\external\re2\re2\simplify.ccCase not handled in ComputeSimple: t5d4d5d4d5dH4d|5dH4dx5dX4d45dX4d(5d4d5d4d5d4dh5d4dX5d5dH5d5d85d84d86d84d(6d@4d6d@4d6d5dx6d5dh6dT4dX6dT4dH6d4d5d4d5d 4d5d 4d5d4d5d4d5d4d5d4d5d4d4d4d4d(4dx4d(4dh4d4d4d4d ~09AZ__az  !~09AZ__az[:xdigit:][:^word:][:word:][:^upper:]09AFafAZAZaz[:^xdigit:] 09AZaz!/:@[`{~09az09[:alpha:][:^alnum:][:alnum:]\W[:blank:][:^ascii:][:ascii:][:^alpha:]\d\w\S\s\D[:punct:][:^print:][:print:][:^lower:][:upper:][:^space:][:space:][:^punct:][:digit:][:^cntrl:][:cntrl:][:^blank:][:lower:][:^graph:][:graph:][:^digit:]ZdxwdZdpdZddZdvdd#ZdHd ]dpd]dd]d\dpd\dhpdP]ddH]d0d8]d d(]dd\dd\dd\dHd\dDdb d wd \dd\dpd\dd\dPd]dd 8d]d d]dXd]dd]dd]d@d(ld]dd[dR]dd|]dpdp]dȨdh]ddX]dd]d~d]dd]dd]dxdX[dpd@[dkd8[dd0[dhd[dd [d@d p[dЉdȢdd[ddZddZdad!dZddZd~d [dd[dd d[dkd[dd0\dd$\d~d\dȂd \dvddx\d dh\dddP\dd8\dvd[dd[dd[dd [d0dXd\d`d[dpd[dhd[d0d_dȎd_d8bd_dpd|d_dHd _dwd _dd_dd_dxdx_ddl_ddPdh_ddcmd"d_dd7(d_dd"xd_dd _dPbdYd%_d@dt`ddb0ddgh`dd``dvdP`d(d`d0d`dad`dd`d8d`ddm`dB_dd_dmd_dd@`dpd(`dhmd`dd`ddL^ddduD^dvd8^dpdd0^ddh^d~d`^dd\^dpdCdBP^dd^dd ^dd%pd^dd]dЃd,^dd(^ddd$^d0dPd*^dPd_dd^dXd^d`d^dXdX_dd@_dpd0_dxpd _dhwd^dd^dhdx^ddp^dPad^dvd^dhdbdpd5^dd^dhdlddldXdldddld@dHldXd ldtdlddldP~d \ld@dd4XlddKHldd@ld8dlddPN`ddQxldadhldd`ldddHmd~d@mdad8mdxd(mdd`md`d `dXmdpddTmdd5d PmdndpdFld~dld`dlddldhdmd8dmdІdmd؎dldXd$adH~dadHdadȉd`d@dDadPdd    Ppsuwz}&*]afj EHMPWYY[[]]_}&!&!eeAZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! 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