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[[Z[?@>[[ [p[?@>[[P[[?@>[[[p[?@>@k\@k\0k\ k\?@>p\p\Xp\?@>m\m\@Xpm\?@>k\k\+Ok\?@>pl\pl\`l\Pl\?@>`k\`k\0k\Pk\?@>k\k\Xpk\?@>n\n\m\m\?@>o\o\o\o\?@>k\k\Xk\?@>k\k\`Xk\?@>`m\`m\ m\m\?@>@l\@l\0l\ l\?@>o\o\Xo\?@>k\k\Xk\?@>l\l\l\k\?@>m\m\m\m\?@>n\n\n\n\?@>l\l\l\l\?@>m\m\l\l\?@>@\@\0\ \?@>\\Op\?@>`\`\P\@\?@>@\@\\\?@>]]]]?@>0]0] ]]?@>`]`]P]@]?@>]]]p]?@>^^Wp^?@> ^ ^^^?@>^^Ѕ^^?@>^^^Ѝ^?@>@^@^Ћ^^?@>P^P^W@^?@> ^ ^W^?@>^^p^`^?@>^^^^?@>P^P^P^@^?@>^^W^?@>^^^^?@>`^`^^^?@>^^W^?@>`^`^P^@^?@>p^p^W`^?@>^^W^?@>^^W^?@>Њ^Њ^^^?@>p^p^W`^?@> ^ ^^^?@>0^0^W ^?@>^^W^?@>^^W^?@>P^P^@^0^?@>^^^^?@>p^p^`^P^?@>^^^p^?@>б^б^pY^?@>^^pY^?@>^^^p^?@>^^`^P^?@>`^`^Ю^^?@>^^Z^?@>0^0^^п^?@>^^^^?@>p^p^P^@^?@>____?@>____?@>__P]_?@>____?@>p_p_`_P_?@>__P]_?@>0_0_ __?@>____?@>p_p_`_P_?@>____?@>__rZ_?@>____?@>__\p_?@>`_`_OP_?@>E_E_E_E_?@>E_E_E_E_?@>E_E_E_pE_?@>````@````@```aa08a?@>`p``P``0`P`P`ddddd`dd`p*aa'a'aaqPO aa(aapa0OJ`ap'a@'a`aqPOaa$aaa0<@~a%a%anaqPO`{a`xa$aoa`ta0@a)a)aaqPOaa$a aa0@<paap%a@%a WaqPO`_a]a$aWaZa0 a0'a'a`aqPOa`a$a`aa0`a&a&aaqPO`aa$aЎaa0;La$a$a5aqPOGaDa$ap6a >a0pLTa0%a%aNaqPORaQa$aNaPa0pbP*a *abqPOPb b$abpb0`*a a&a&aaqPOa@a$aaa0+aa)a(aaqPOa0a$aa`a0+a aP)a )aaqPOaPa$a@a0a0 ,a@a)a`)a0aqPOaa$aa@a0 +aa(a`(aaqPOa0a$aa`a0,apa0&a&a0aqPOaa$aa@a0`,aja%a%a caqPOia0ha$aca@fa0`-ab*a)a`aqPO0ba$aaa0,aap&a@&aaqPOa a$aaa0`+aPa(a(aaqPOa a$aaa0*aaP(a (aaqPOaPa$a@a0a0 -a a'a'aaqPOaa$a@aa0b b0b@bdobpnb0b@bnb0bЯbPbbbbbbbdbbb@bb Gc0Gc@GcPGc`GcpGcGcGccc cPcpccccpdcccPc`cobc@c@b0Unknown exceptionbad array new lengthstring too longD:\a\_work\1\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s kkkkCan't happenD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): falsetrue\\x%02xty10invalid string position00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899...\\?\\/INVALID_ARGUMENT:, map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.Stacktrace: Cpuai.onnx.mlai.onnx.preview.trainingai.onnx.trainingVIWEFD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)CPUExecutionProviderTensor sequence must contain only primitive typesonnxruntime::TensorSeq::SetTypeelem_type_ != nullptri < tensors_.size()D:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::GetIsTensor()Trying to get a Tensor, but got: D:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableTrying to get a SparseTensor, but got: IsTensorSequence()IsSparseTensor()ORT_LOAD_CONFIG_FROM_MODELSafeIntExceptionHandler::SafeIntOnOverflowInteger overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hsize overflowtried creating tensor with negative value in shapenot enough space: expected , got `anonymous-namespace'::GetDataTransferNot able to find appropriate IDataTransfer to copy sparse dataStrings can only reside in CPU memoryD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shape`anonymous-namespace'::ValidateFillInputArgsOrtApis::FillSparseTensorCsrOrtApis::FillSparseTensorCooOrtApis::FillSparseTensorBlockSparsetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::UseCooIndicesCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::UseBlockSparseIndicesOrtApis::UseCsrIndicesUse GetStringTensor*() API to retrieve stringsthe ort_value must contain a constructed tensorRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyonnxruntime_profile_lengths allocation failedoutput name cannot be emptyOutput buffer allocation failedstring buffer allocation failedelement index is out of boundsinput array doesn't equal tensor sizeSparse Tensor does not contain sparse dataOrtValue should contain a Tensor or a Sparse Tensorshape is invalidThis API supports Tensors or SparseTensorsoffsets buffer is not equal to tensor sizeindex is out of boundsbuffer size is too small for string elementoutput buffer is too small. Use GetStringTensorDataLength.internal errorout of indexInput is not of one of the supported sequence types.index out of rangeinput array is too shortInput is not of type sequence or map.Expecting all elements to be tensors. Got: Input is not of one of the supported map types.OrtCreateValueImplSeqHelperin[idx]->IsTensor()Each element of the sequence should be either tensor or map.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported input typeAt least one element in the sequence is of a type different from others.Either the key tensor or the value tensor has NumDimensions > 1For map type num_values MUST be 2Key type is not supported yet.Key and value tensors have unequal number of elements.opaque(Number of values should be at least 1.Specified domain and type names combination does not refer to a registered opaque type,OrtApis::CreateOpaqueValueml_type != nullptrnon_tensor_base != nullptrOpaque type is not a non_tensor type!!!Specified provider is not supported.OrtApis::GetOpaqueValuethis API does not support stringsinvalid location rangelocation dimensions do not match shape sizearena_extend_strategymax_memmax_dead_bytes_per_chunkinitial_chunk_size_bytesInvalid key found: initial_growth_chunk_size_bytesThe given version [%u] is not supported, only version 1 to %u is supported in this build. Training APIs are not supported with this build. Please build onnxruntime from source with the build flags enable_training and enable_training_on_device to retrieve the training APIs. 1.13.1Tensor type mismatch. T onnxruntime::Tensor::MutableDatautils::IsPrimitiveDataType(dtype_)Invalid index requested for map type.onnxruntime::Tensor::DataAsSpanvalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelperMap is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::check++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscriptonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::Tensor::Datanot implementedexecution_mode is not valid contains null pointersgraph_optimization_level is not validInput index: Provider options key/value cannot be empty execution provider is not supported in this build. Maximum string length for a provider options key/value is 1024.XNNPACKSNPEUnknown provider name. Currently supported values are 'SNPE' and 'XNNPACK' is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'custom op registered at runtimeD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInputTThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.all typesonnxruntime::CreateCustomRegistrytype_id_counter == 1OutputT0Attempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLoggerD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hTensor type mismatch.onnxruntime::Tensor::DataRaw) != new size (type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::ReshapeTensorSeq: tensor to be added has a different data type.Tensor size (shape_.Size() == new_shape.Size()IsSameDataType(tensor)onnxruntime::TensorSeq::Addoutput_ptronnxruntime::OpKernelContext::RequiredOutput is not present.Required output at index p_ml_valueonnxruntime::OpKernelContext::OutputD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.D:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.ccProvider_SetHostonnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::EnsureGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime_providers_rocm.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_cuda.dllonnxruntime_providers_cann.dllonnxruntime_providers_migraphx.dllonnxruntime::TryGetProviderInfo_CUDAonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllonnxruntime::TryGetProviderInfo_ROCMSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared librarySessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.invalid unordered_map keyRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputCreate_State_Release_State_Compute_kernel already mapped to existing nodematching node is missinginvalid node output countinvalid arg_num., expect: invalid node input count: D:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.ccAttribute type not supported yet.arg_num < static_cast(input_count_)onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsonnxruntime::standalone::InvokeOponnxruntime::standalone::CreateOpstandalone_Env is nullNo requested allocator availableProvided OrtMemoryInfo is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided allocator is nullOrtMemoryInfo is nullD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::Releaseenv_ptr == p_instance_.get() it.second=D:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputonnxruntime::SyncProvidersmapped_feed_names_.size() == feed_names_.size()Size mismatch: index=mapped_output_names_.size() == output_names_.size()Size mismatchonnxruntime::IOBinding::SynchronizeOutputsonnxruntime::IOBinding::SynchronizeInputsp_provideronnxruntime::IOBinding::BindOutputImplbad cast: stoi argument out of rangeinvalid stoi argument MemoryType: DeviceId:] OrtMemType: OrtAllocatorType:Device:[DeviceType:OrtMemoryInfo:[name: id:CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProviderCANNExecutionProvider has already been registered.D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider D:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoCached EP instance for graph replay is not set yet before calling ReplayGraph()onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraphIsGraphCaptured()D:\a\_work\1\s\onnxruntime\core/session/inference_session.h5D:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensorIsOptionalTensor(type)Provided type is not an optional tensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorMemcpyTransformerORTM[json.exception.] /session.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.set_denormal_as_zerosession.use_ort_model_bytes_directlysession.intra_op.allow_spinningsession.inter_op.allow_spinningoptimization.minimal_build_optimizationssession.use_ort_model_bytes_for_initializerssession.strict_shape_type_inferencesession.force_spinning_stopsession.dynamic_block_basesave%Y-%m-%d_%H-%M-%Smemory.enable_memory_arena_shrinkageThe environment variable contained the value: Invalid value for apply value of 'save' is only valid when saving an ORT format model.onnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configThe only supported values for the environment variable are '0' and '1'. onis_model_proto_parsedModelProto needs to be parsed to check for ORT config within itD:\a\_work\1\s\onnxruntime\core\session\inference_session.ccCould not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()Flush-to-zero and denormal-as-zero are offCreating and using per session threadpools since use_per_session_threads_ is truegraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()onnxruntime::InferenceSession::ConstructorCommonstatus.IsOK()Dynamic block base set to session--intra-op-custom 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::AddCustomOpDomainsParallel execution mode does not support the CANN Execution Provider. So making the execution mode sequential for this session since it uses the CANN Execution Provider.This session already contains a loaded model.Exception during loading: Failed to save ORT format model to file: onnxruntime::InferenceSession::SaveToOrtFormat failed:model_loading_uriEncountered unknown exception in LoadWithLoader()onnxruntime::InferenceSession::LoadWithLoaderFailed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Load model from ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_array bytes were able to be read.onnxruntime::LoadOrtModelBytesonnxruntime::InferenceSession::TransformGraphmodel_loading_from_saved_protoThis session has already been initialized.onnxruntime::InferenceSession::LoadOrtModelWithLoaderonnxruntime::InferenceSession::LoadOrtModel::::operator () failed. Only This build doesn't support ORT format models older than version 5. See: https://github.com/microsoft/onnxruntime/blob/rel-1.13.0/docs/ORT_Format_Update_in_1.13.mdSerialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.ORT model verification failed.Missing Model. Invalid ORT format model.The ORT format model version [] is not supported in this build . onnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelThe session already has a PrePackedWeightsContainer instanceThe provided PrePackedWeightsContainer instance to be added to the session is nullInitializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: Session has already been initialized.Model was not loaded.Model was not loadedonnxruntime::InferenceSession::InitializeThis session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session will use the allocator registered with the environment.Adding default CPU execution provider.This session will use the CUDA Graph feature as requested by the user.This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been partitioned to the CUDA EP. as all the graph nodes have not been partitioned to the CUDA EP.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.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.NchwcTransformerUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Expected: Please fix either the inputs or the model.session_initializationEncountered unknown exception in Initialize()Got invalid dimensions for input: for the following indices Invalid rank for input: Got: )) , expected: ((Unexpected input data type. Actual: ( index: Size mismatch: feed_names has elements, but feeds has elements.))tensorInput with name: is not expected to be of type tensor.Invalid Feed Input Name: is not expected to be of type tensor sequence.sparse_tensor is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputsp_fetches->size(): At least one output should be requested.Output vector pointer is NULLseqReplaying the captured CUDA Graph for this model with tag: Invalid Output Name:Output vector incorrectly sized: output_names.size(): Running with tag: Session not initialized.Session was not initializedonnxruntime::InferenceSession::Runonnxruntime::InferenceSession::GetModelMetadataStart the second Run() to capture the graph. The first one is for necessary memory allocation;The second one is for capturing the graph.model_runEncountered unknown exception in Run()onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingProfiler is disabled. combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Unsupported device specified in the memory arena shrink list: error message: The registered allocator for device-id combination is not an arena based allocator: Did not find an arena based allocator registered for device-id run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformers961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5_.jsonlist too longother_errorlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringlcom.microsoftorg.pytorch.atencom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwc Target=Mismatch between source and target type. Source=[ShapeInferenceError] Unsupported Source/Target type=tensor(uint32)tensor(uint64)tensor(int32)tensor(int64)unknowntensor(uint8)tensor(uint16)tensor(int8)tensor(int16)tensor(float16)tensor(float)tensor(double)tensor(bfloat16)seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))tensor(bool)tensor(complex64)tensor(complex128)seq(tensor(string))seq(tensor(float16))seq(tensor(float))seq(tensor(double))tensor(string)seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16))Schema error: line , but it is already registered from file ) from file , but its domain is notin onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but version: (domain: Trying to register schema with name known by the checker.] (usually, this means you in the inclusive range [, but its version is not Only CPU allocators can be shared between multiple sessions for now.An allocator for this device has already been registered for sharing.stringException caught: intra-opinter-opOnly CPU devices are supported for now.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.No allocator for this device has been registered for sharing.outputYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.D:\a\_work\1\s\onnxruntime\core\session\environment.ccMemcpyFromHostinputXMemcpyToHostvalue', column onnxruntime::SetIntraOpNumThreadsD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: at line ort_configsession_optionsUnsupported value for intra_op_num_threads: Sequential modeParallel modeSetting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting enable_profiling to onnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoORT config json from the model: The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelJson stored in the `ort_config` key cannot be parsed. Error message: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()The Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threads' not foundkey 'cannot use at() with graph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: cannot use key() for non-object iteratorsinvalid map keynullobjectnumbercannot get valuearraybooleanbinarydiscardedout_of_rangetype_errorparse errorparse_errorinvalid_iteratorsyntax error ; expected invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literalwhile parsing - ; last read: 'unexpected string literalnumber literal'[''{'true literalfalse literalnull literalend of input'[', '{', or a literalunknown token']''}'':'','invalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 bytecannot compare iterators of different containersobject keyvector too longobject separatornumber overflow parsing 'iterator does not fit current valueiterator out of rangecannot use erase() with type must be number, but is Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplD:\a\_work\1\s\include\onnxruntime\core/graph/graph.h Max:onnxruntime::GraphTransformer::RecursetoRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hInsertedCast_cast node to cast from float16 to float32 on cpuCastonnxruntime::InsertCastTransformer::ApplyImpldtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()ACLExecutionProviderArmNNExecutionProviderROCMExecutionProviderQDQFinalCleanupTransformersession.disable_quant_qdqsession.enable_quant_qdq_cleanupNhwcTransformerAttentionFusionAvx2WeightS8ToU8TransformerBiasDropoutFusionoptimization.enable_gelu_approximationsession.qdqisint8allowedsession.x64quantprecisionConvAddFusionConvConvBNFusionConvMulFusionBiasGeluFusionBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationDynamicQuantizeMatMulFusionEmbedLayerNormFusionExpandEliminationExpandDivMulFusionDivEliminateDropoutDropoutGemmSumFusionGemmGemmTransposeFusionEliminateIdentityFastGeluFusionGeluApproximationGeluFusionGemmActivationFusionMatMulIntegerToFloatFusionMatMulScaleFusionMatmulTransposeFusionNoopEliminationIdentityLayerNormFusionSimplifiedLayerNormFusionMatMulAddFusionClipQDQPropagationTransformerQDQS8ToU8TransformerReluQuantRewriteAddNotWhereFusionWhereClipQuantRewriteEliminateSliceSliceTransposeOptimizerUnsqueezeEliminationReluFuseReluClipReshapeFusionSkipLayerNormFusiononnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformeronnxruntime::optimizer_utils::GenerateTransformersUnsqueeze_RuleBasedTransformerLevelUnsupported optimization level: onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildVitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::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 memoryD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::Applykqlkql kql kilkil kilkilkqlkql kql kql kql khkl khkl khkl 8Al ql 8Al ql Bad optional accessSqueezeGatherNhwcMaxPoolTransposeQLinearConvPermutation length does not match rank 0 <= p && p_int < shape_proto->dim_size()Permutation entry size >= 0Failed to get size of TensorProtoonnxruntime::ApiValueInfo::PermuteDimsperm.size() == gsl::narrow_cast(shape_proto->dim_size()) out of bounds for shape D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\optimizer_api_impl.ccFailed to find initializer for name: onnxruntime::ApiGraph::GetValueInfoonnxruntime::ApiGraph::TransposeInitializersuccessonnxruntime::ApiTensor::Dataonnxruntime::ApiTensor::NumElementsnode_arg_ != nullptrNo NodeArg found for name _outnew_num_elts == old_num_elts provided for op: Added in transpose optimizeronnxruntime::ApiGraph::ReshapeInitializerFailed to find initializer to reshape with name Cannot reshape initializer to have different number of elementsAn entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.opset_import_iter != domain_to_version_map.end()since_version.has_value()Transpose Optimizer is adding an unexpected node: domain == kOnnxDomainTranspose optimizer is expected to add only onnx domain ops. Domain: domain not found in opset imports.onnxruntime::GetSinceVersionForNewOpFusedConvResizeQLinearGlobalAveragePoolQLinearAveragePoolExisting destination type is not compatible with source type.const_transpose_optimizeronnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Optimization after layout transformation failed: channels_lastD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered Could not find OrtValue with name 'startConstantFoldingDequantizeLinearendCould not find a CPU kernel and hence can't constant fold . Can't constant fold fetches.size() == node->OutputDefs().size()D:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImpl node 'ShapeUnsupported output type of Unsupported data type: Constant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializershapeinput_node.InputDefs().size() == 2 && scale_and_index->second < 2mul_inputs.size() == 2output_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetInputNodeMergesdiv_inputs.size() == 2D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccMulonnxruntime::`anonymous-namespace'::GetScaleFromNode_FusedMatMulAndScaleFused MatMul and Scaleonnxruntime::MatMulScaleFusion::ApplyImplMatMulonnxruntime::`anonymous-namespace'::GetOutputNodeMergesalphaFusedMatMulonnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()Slice parameter is not expected. Input index:Start MatchGemmSubgraphSlice ends is less than INT_MAXExpected value:D:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsinput_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersGemm does not have 3 inputsGemm bias is not constantInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathConcatFaild to match gemm pathonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueconcat first input value is not -1Faild to match concat node for Gather pathsGemm bias shape not expectedCheckSliceParameters return falseStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializergather input 1 value is not expectedPass MatchGemmSubgraphaxisgather axis value not expectedaxesunsqueeze_after_gather axes value not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedPass ValidateGemmInitializerunidir mask is not constantGemm weight is not constant initializerGemm weight shape is not expectedGemm bias is not constant initializerGemm bias shape is not expectedSubFaild to match path 1 for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1CheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgePass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Faild to match path 4 for unidirectional maskDiv and Shape does not have edgeFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1Failed to find path for maskOutput edge count not expected for mask nodesFailed to find Softmax nodeOutput edge count not expected for Softmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphSoftmaxFailed to find mask pathwhere const not matched.Start MatchInputMaskSubgraphDistilBertEqualmask_mul const input not matchedPass MatchInputMaskSubgraphmask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphgather indices not matched.Pass MatchInputMaskSubgraphDistilBertFailed to find reshape shape path 1Failed to find reshape shape path 2Failed to find shape pathequal const not matched.present_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpermpast_k_transpose perm attribute not matchedFailed to find path for present_v and past_vFailed to match v_concatFailed to find path for past_kFailed to find path for present_konnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphpast_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1Pass CheckNodesInPathVreshape initializer value is not expectedhidden_size != num_heads * head_sizev_reshape initializer value is not expectedFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3onnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKq_transpose perm attribute not matchedPass CheckNodesInPathQq_reshape const not matchedqk_div const not matched.Start CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart FuseGptAttentionMask_Int32Cast mask from int64 to int32k_reshape const not matchedPass CheckNodesInPathKk_transpose has not perm attributek_transpose perm attribute not matchedMatchUnidirMaskSubgraph returns NULLFailed to find path for qCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseSplitFaild to find path v to Splitonnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulCheckNodesInPathK returns falseMatchPastSubgraph returns falseFailed to find path for kk and v are not from same Split nodeUsing transpose optimized patternopt_k_transpose perm attribute not matchedq and v are not from same Split nodeCheckNodesInPathQ returns falseqkv_biasMask shape is unknown or not 2D, or data type unknownFused an attention node for GPT.qkv_weightsnum_headsunidirectionalFused Attention subgraphs AttentionTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplLayerNormalizationshape of layer norm bias tensor not expectedMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplonnxruntime::ConvertMaskToInt32D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccFailed to convert mask to int32onnxruntime::FuseSubGraphQKFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.k root is not layer normk_matmul and k_add shape not matchedq root should be layer normalizationq_matmul and q_add shape not matchedFailed in match input mask subgraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphFused an attention node.onnxruntime::FuseSubGraphQKDistilBertepsilonconv_W_tensor_protoconv_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyBatchNormalizationConvBnFusion_W_ConvBnFusion_BN_B_onnxruntime::QDQS8ToU8Transformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quant1DQAveragePooldropDQArgMaxMaxPooldropSplitQDQ*DQQDQSelectorActionTransformerSigmoid2DQLeakyReluGlobalAveragePool8Alonnxruntime::NodesToOptimize::GetNodeD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)TanhHardSigmoidoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)betaactivationFailed to get Clip min/max constants.onnxruntime::`anonymous-namespace'::actions::FuseConvActivation::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccExpected activation node.activation != nullptrConvActivationFusionConvActConvAddReluconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"onnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesactivation_paramsExpected Conv then Add.UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination_D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of minFuseReluClip__min_zero_constantonnxruntime::GeluApproximation::ApplyImplGeluBiasGeluTotal Gelu Approximation (FastGelu) node count: FastGeluGelu approximationD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccBlBlBlD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::ApplyImplonnxruntime::ReshapeFusion::Fuse_SubgraphCannot replace concat node with initializer:endsstartsTotal fused reshape node count: Fused reshape node: allowzeroFused Gemm with Sumnew_gemm_output_defs.size() == 1D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.cconnxruntime::GemmSumFusion::Applynew_gemm_input_defs.size() == 3transBtransASumgraph.RemoveNode(sum_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(gemm_node.Index())!sum_input_movedsum_output_edge.src_arg_index == 0gemm_input_edge.src_arg_index < 2_sum_transformedonnxruntime::GemmSumFusion::SatisfyConditionother_sum_input != nullptr and Invalid node index onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputnode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndnode != nullptrsrc_node || dst_nodeAt least one graph node must be specified in the propagation edge.QuantizeLinear_pre_qInserting Q/DQ pair between node ("", index: at NodeArg "".D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cconnxruntime::`anonymous-namespace'::InsertQDQPairsrc_node != nullptrFailed to set op schema for added DQ node._dqFailed to set op schema for added Q node._qInserted by QDQPropagationTransformeronnxruntime::QDQPropagationTransformer::ApplyImplonnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::GetNextPropagationEdgedst_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgeonnxruntime::CheckInputInput shape is unknown or not 2D, or data type unknown_CastCast Input from int64 to int32_Int32Output edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Output edge count not expected for nodes in path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.Input data type is not int32 or int64D:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccOutput edge count not expected for nodes in path1.RangeConstantOfShapeNonZerotwo paths share the same shapeThe parent of two shape nodes are expected to be input_ids.Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Optional position subgraph nodes number of outputs unexpected.The parent of shape nodes are expected to be input_ids.Failed to match Shape node. Second input of Gather should be a constant with value 1. The third input of Range should be a constant with value 1.The first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherInput is expected to have dim value in all dimensions.onnxruntime::FuseSubGraphWord embedding shape not expected.EmbedLayerNormalizationfused EmbedLayerNorm subgraphs mask_indexposition_embeddingsFailed to match position subgraph.Segment id is not valid. Input id is not valid. Position embedding shape is not expected.Failed to get position embedding weights.Failed to match position embedding subgraph.Position embedding data type shall be float or float16.Position embedding shape not matched.Failed to get initializer tensor.onnxruntime::EmbedLayerNormFusion::ApplyImplonnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). Gamma should be of shape (hidden_size). Input_ids and segment id should have the same shape. DynamicQuantizeMatMulDynamicQuantizeLinearMatMulIntegerToFloatD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of with a fixed dimension size ' The model has input 'FreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::ApplyImplonnxruntime::MatMulAddFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add gemmSoftplusSoftsignScaledTanhParametricSoftplusThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccwith activation EluSelufused Gemm FusedGemmfused activation_Unexpected data type for QuantizeLinear input y_zero_point of onnxruntime::GetQConstantLowerUpperD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ValueMovesConvAddActConvAddActivationFusionD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.cconnxruntime::TryConvertDynamicQuantizeLSTM cannot locate recurrence tensor of const int8 type, int8 overflow might impact precision !Unable transforming DynamicQuantizeLSTM operator,weight_zp_s8_2_u8QGemmQAttentionQLinearMatMulMatMulInteger unable to locate recurrence tensor or its zero point value,DynamicQuantizeLSTMonnxruntime::Avx2WeightS8ToU8Transformer::ApplyImpl_transformedFused Gemm with Transposeonnxruntime::GetTransposePermstranspose_node.InputDefs().size() == 1transBatchBtransBatchAMatMul_With_Transposefused MatMul and Transpose !node_consumers.empty()D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cccast != nullptronnxruntime::UpdateConsumerCountCreated a new Cast node to interchange Cast and Transpose nodesonnxruntime::ReorderCastAndTransposeonnxruntime::MatmulTransposeFusion::ApplyImplCreated a new Transpose node to interchange Cast and Transpose nodesdeque too longConvMulFusion_W_mul_B_tensor_protoConvMulFusion_Mul_B_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplSqrtReduceMeanfused LayerNorm subgraphs Powonnxruntime::SimplifiedLayerNormFusion::ApplyImplstash_typeSimplifiedLayerNormalizationBlBl Blonnxruntime::SkipLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccSkipLayerNormalizationBlBlBlNOTSETauto_padSAME_LOWERSAME_UPPERgroupVALIDoutput_channels <= nchwc_output_channelsBuffer overflowReorderInputreorderstridespadskernel_shapedilationschannelsReorderOutputnearestmodecoordinate_transformation_modelinearalign_cornersasymmetricD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformerImpl::TransformConvreshape_nchwcspatial_bn_nchwcnearest_modehalf_pixelUpsamplefloorGlobalMaxPoolscalesonnxruntime::NchwcTransformer::ApplyImplaxis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxis is not in valid range [-axis D:\a\_work\1\s\onnxruntime\core/providers/common.h into softmax(input + bias)BiasSoftmaxD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.ccis_inner_broadcastonnxruntime::BiasSoftmaxFusion::ApplyImpllelonnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cc[ of node representative.output_index != kInvalidOutputIndex] because it's the graph's output.Not eliminating output onnxruntime::`anonymous-namespace'::CleanUpNodeSequenceD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cc" and Cleaning up back-to-back nodes: onnxruntime::QDQFinalCleanupTransformer::ApplyImpl" with name "Notonnxruntime::TransposeOptimizer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.ccTranspose optimizer failed: D:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplonnxruntime::BiasDropoutFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBitmaskBiasDropoutfused Add-Dropout-(Add) for BitmaskDropoutBiasDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::MatMulIntegerToFloatFusion::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodefused Gelu subgraphs ErfD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::GeluFusion::ApplyImplfused Add and GeluD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGeluFusion::ApplyImplfast_gelu_outputD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImplGPT2Gelufused GPT2Gelu subgraphs BlBlBlBlonnxruntime::ConvAddFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_RoundIsInfIsNaNShrinkAbsCeilFloorNegstorage_orderExpLogReciprocalkeepdimsnoop_with_empty_axesAcoshAtanhMaxMinAsinAcosAtanAsinhCosTanSinhCoshSignCeluHardSwishSinPadReduceSumReduceLogSumReduceLogSumExpGreaterOrEqualLessOrEqualMeanCastLikePReluBitShiftGreaterLessAndOrXorModcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearConcatcom.microsoft.QLinearAddcom.microsoft.QLinearMulHardmaxLogSoftmaxcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoidReduceL1ReduceL2ArgMinTileReduceMaxReduceMinReduceProdReduceSumSquareLRNGridSampleDepthToSpaceSpaceToDepthcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.Unsupported ONNX opsetD:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccmodel_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()Expecting the same data typeInitializer must have a datatypeutils::HasDataType(tensor_proto)onnxruntime::Initializer::Initializercalled_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceaxis >= 0onnxruntime::Initializer::scale_by_axisInvalid other(scalers) sizescalers.size() == 1 || scalers.size() == num_blocksonnxruntime::Initializer::subonnxruntime::Initializer::mulonnxruntime::Initializer::divAxis must be non-negativedata_type() == other.data_type()onnxruntime::Initializer::addExpecting the same sizesize() == other.size()onnxruntime::Tensor::MutableDataAsSpanRandomNormalRandomUniformLikeRandomNormalLikeMultinomialRandomUniformUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccmaxlllllallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::Infoonnxruntime::TryCreateKernelTried to allocate without valid type information, ort_value index=onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointq_or_dq_input_defs.size() >= 2D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.ccinserted_in_name_to_entryExisting registration with name D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.cconnxruntime::SelectorActionRegistry::RegisterSelectorAndActionNodes to optimize are not valid, skipping action.Missing action onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizationsToo many produced nodes in the runtime optimization record.Applying runtime optimization action for transformer ).onnxruntime::SetOpSinceVersionForProducedNodesNew node op () does not match produced node op in runtime optimization record (onnxruntime::MatchAndProcessMatched Not enough produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsopsetinit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14agenerated at runtimeinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8QLinearonnxruntime::MergeIntoTarget::RunFailed to remove node.Failed to set node op schema.onnxruntime::CreateReplacementNodeD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::ReplaceWithNew::RunForSaveonnxruntime::ReplaceWithNew::RunIndex out of rangeD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cconnxruntime::`anonymous-namespace'::MoveInputOutputImplonnxruntime::NodesToOptimizeIndicesBuilder::Buildtarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::MoveInputOutputnode_idx <= NodesToOptimizeIndices::kEmptyNodeIndexNode index value is too large to save to ORT format model: A target node must be set.onnxruntime::GetNodesToOptimizeIndices::::operator ()NnapiExecutionProviderRknpuExecutionProviderDnnlExecutionProviderOpenVINOExecutionProviderTvmExecutionProviderXnnpackExecutionProviderCoreMLExecutionProviderll(llelllllll0lelxelllllkonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterOnnxOperatorKernelsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.v >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseperm: [ does not align with rank of input data: Attribute perm of Transpose has an invalid value. Value is repeated.onnxruntime::DoTransposeImplTranspose not implemented for empty tensors.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cc(local_source >= source) && (local_source < source + num_blocks * blocksize)naxes > 0onnxruntime::IncrementIndexAndComputeOffsetSetupMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::Transpose::ComputeMismatched data types between input and output Tensors. input_tensor_ptr != nullptr(local_source >= source) && (local_source < source + num_blocks)Transpose of element size not supported in this build. Size=onnxruntime::DoTransposeEltWisenum_axes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.scores must be a 3D tensor.boxes and scores should have same num_batches.scores_tensorboxes must be a 3D tensor.onnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccNonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_center_point_boxonnxruntime::NonMaxSuppression::Computeoutput != nullptriou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccGather Tind type not supported in this build.Tindonnxruntime::Gather::Computeonnxruntime::GatherBase::GatherBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hMissing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK() must be within the inclusive range [indices element out of data bounds, idx=info.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseMissing/Invalid 'axes' attribute valueD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.haxes_tensor != nullptrAn axes tensor must be a scalar or a 1-D tensor.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAxes input is nullX != nullptronnxruntime::UnsqueezeBase::PrepareComputeonnxruntime::Unsqueeze::Compute'axes' has a duplicate axisnullptr != p.output_tensoraxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axisUinverseonesideddft_length must be a scalar value.STFTDFTT2T1Unsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::STFT::Computeonnxruntime::DFT::ComputeUnsupported input data type of Unsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.dft_length must be greater than zero.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.cconnxruntime::discrete_fourier_transformwindow_size < signal_sizeEnsure that the dft size is smaller than the signal.frame_length == window_lengthIf both frame_length and window are set, then the size of the window must be equal to the frame_length.onnxruntime::short_time_fourier_transformsignal_components == 1 || signal_components == 2signal shape must end in 1 (real) or 2 (real, imaginary).D:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.honnxruntime::signal::get_scalar_value_from_tensortensor->Shape().Size() == 1ratio input should have a single value.onnxruntime::bit_reverseUnsupported bit size.@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?HammingWindowHannWindowperiodicoutput_datatypeMelWeightMatrixT3BlackmanWindowonnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cconnxruntime::CreateMelWeightMatrix::operator ()lowest_index >= 0 && lowest_index < num_spectrogram_binslower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOaxis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by Broadcast Output range [) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:onnxruntime::InputBroadcaster::AdvanceByoffset % span_size_ == 0InputBroadcaster can only start at span boundary!onnxruntime::Broadcaster::Broadcasterlargest <= 1Can broadcast 0 by 0 or 1. is invalid.onnxruntime::BroadcastIterator::AppendD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Initonnxruntime::TensorAllocator::TensorAllocatorstart_offset % span_size == 0 && real_end % span_size == 0data_1.Shape() == shapeonnxruntime::Mean_6::Computeonnxruntime::Max_6::Computedata_n.Shape() == shapeAll inputs must have the same shapeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Min_6::ComputeinputCount >= 1Must have 1 or more inputsUnsupported X type: onnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()onnxruntime::mod_internal::CallModImpl::operator ()fmod attribute must be true for floating point typesonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1fmodonnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1Tensor with shape information must be 1 dimensional.onnxruntime::ExpandBroadcastLooper!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computehelper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.Unsupported Y type: onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::Compute::::operator ()cur1 == end1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computeinput_count >= 1onnxruntime::BroadcastLooperonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.RIGHTLEFTonnxruntime::BitShift::BitShiftdirectiononnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()Attempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hbodyinfo.GetAttr("body", &proto).IsOK()onnxruntime::Scan<9>::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directions dimensions or more but input had shape of Expected Invalid scan input: has length of ' dimension but input 'Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputs outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariablesscan_input_axes but expected Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsSetupSubgraphExecutionInfo should only be called once for each subgraph.info_ == nullptronnxruntime::Scan<9>::SetupSubgraphExecutionInfoCreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_onnxruntime::Scan<9>::ComputeSubgraph SessionState was not found for 'body' attribute.session_stateonnxruntime::ScanImpl::InitializeOutput OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputVScan'is defined.No attribute with name:'Attribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::ElementWiseRangedTransform::CreateD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.ccunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelreductionScatterND, indices shape: input shape: input tensor and indices tensor must has rank larger than 0. last dimension of indices must not be larger than rank of input tensor, data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::PrepareForComputeinvalid indice found, indice = onnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()ScatterScatterElementsCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.data type is different from updates typeIndices and updates must have the same rank vs Indices vs updates dimensions differs at position=. Input rank=Indices must have the same rank as Input. Indices rank= is greater than input dim= at pos=Indices dim=Indices type is not supported.onnxruntime::Scatter,class std::allocator > > >::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccnoneOnly 4-D tensor is supported does not match input batch size Grid batch size grid_dims[0] == Nbilinearzerospadding_modebicubiconnxruntime::GridSample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cc, expect 2Last dimension of grid: grid_dims[3] == 2" not supported, expect bilinear, nearest or bicubicmode "mode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"onnxruntime::GridSample::GridSampleD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.hborderreflection" not supported, expect zeros, border or reflectionpadding_mode "padding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"output_widthoutput_heightSampling ratio should be >=0, but it was sampling_ratiospatial_scalesampling_ratio_ >= 0and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.The existing summation for max mode and sampling ratios besides 1 is incorrect avgInvalid mode of value specified. It should be either avg or maxD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBaseNull input X ptrRoiAlignNull batch_indices_ptrNull rois_ptrNumber of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1First dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly Expected: Got:onnxruntime::Loop::InitInconsistent shape in loop output for output. onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfo'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_Loop subgraph input 0 has unknown shape: 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::Initializelast_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsonnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsBI inputs. Found:Loopstatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Info outputs so the subgraph requires but has num_subgraph_outputs - 1 == num_outputs'Loop' node has onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::Execute::::operator ()Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptronnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.Failed to create output tensor for onnxruntime::IfImpl::InitializeFailed to create output tensor for If output Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedonnxruntime::IfImpl::Execute outputs.If'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)then_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::IdentityOp<1>::ComputeUnable to get an allocatorD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computetypeattr->has_tp()Optional op must have a TypeProto in the 'type' attribute if the attribute is presentD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::OptionalOptionalOOptionalGetElementOptionalHasElementD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.cconnxruntime::PropagateInputOrtValueToFirstOutputonnxruntime::Optional::ComputeOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))onnxruntime::OptionalGetElement::ComputeTrying to use OptionalGetElement on an optional type OrtValue which contains no databad allocationInvalid input B: Invalid input mean: NumDimensions() != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: onnxruntime::BatchNorm::Compute dimension != Invalid input scale: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != !is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid number of outputs for BN trainingtraining_modeis_spatial_onnxruntime::BatchNorm::BatchNormmomentumTraining mode only supports spatial BNonnxruntime::BatchNorm::BatchNormreversebidirectionalforwardD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccRNNonnxruntime::RNN::ComputeAffineactivations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()layout_ == 0allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.cliplayoutD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()info.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNNactivationsinfo.GetAttrs("activations", activations_).IsOK()activation_alphaactivation_betaonnxruntime::rnn::detail::MakeDirectionD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.h'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'info.GetAttr("direction", &direction).IsOK()onnxruntime::LSTMBase::LSTMBaseclip_ > 0.finput_forgetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activation_func_names.size() == static_cast(num_directions_) * 3LSTMsigmoidtanhLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::PrePackD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeGRU operator does not support double yetInvalid data type for GRU operator of activation_func_names.size() == static_cast(num_directions_) * 2GRUonnxruntime::DeepCpuGruOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cclinear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()onnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::ComputeImpllda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmC + (M * ldc - (ldc - N)) <= C_endcur + size <= endA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endonnxruntime::rnn::detail::SafeRawPointeronnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperA dimension cannot be less than -1, got unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0i < input_shape.NumDimensions(), requested shape:gsl::narrow_cast(input_shape.Size()) == sizeshapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeA shape tensor must be a vector tensor.onnxruntime::Reshape_1::Reshape_1D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.upperinfo.GetAttr("upper", &temp).IsOK()Triluk should be a 1-D or 0-D tensor.onnxruntime::Trilu::TriluD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccInput tensor should have a rank of at least 2IsScalarOr1ElementVector(k)onnxruntime::Trilu::Computedims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsonnxruntime::SliceIteratorBase::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()Axis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`CumSumCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum::Computeexclusivestarts.size()=dims.size()=extents.size()=dims.size() == extents_.size()dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initsteps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Hardmax::ComputeHardmax inputs N, D and N * D must be < . N=, D=D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.ccinfo.GetAttr("mode", &mode).IsOK()cubicextrapolation_valueinfo.GetAttrs("scales", scales_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.honnxruntime::UpsampleBase::UpsampleBaseceilround_prefer_ceilonnxruntime::UpsampleBase::StringToCoordinateTransformationModecoordinate_transform_mode:[Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModenearest_mode:[] is not supported!scale > 0Scale value should be greater than 0.onnxruntime::UpsampleBase::ScalesValidationscale >= 1scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || (scales.size() == 4 && scales[0] == 1 && scales[3] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the Upsample operatorResize operatorround_prefer_floor`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attribute or exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to exclude_outsidecubic_coeff_aonnxruntime::UpsampleBase::StringToUpsampleModemode attribute is . It can only be (default) or ] is not supportted!tf_crop_and_resizetf_half_pixel_for_nnpytorch_half_pixelscales_size > 0scales size should be greater than 0.scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the onnxruntime::UpsampleBase::ParseScalesDatalast_loop_size > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptylast_loop_red_size > 0An axes tensor must be a vector tensor.onnxruntime::ValidateCommonFastReduceonnxruntime::ValidateNoTransposeReducecount == 1onnxruntime::ValidateKeepDimsCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:axes_tensor->Shape().NumDimensions() == 1Only works on matrices with two dimensions.onnxruntime::ValidateMustBeOverloadedmust be overloaded.projected_index.size() > 0fast_shape[0] == output.Shape().Size()Output size mismatch.onnxruntime::ValidateFastReduceKRfast_shape.size() == 2fast_shape.size() == 3Only works on matrices with three dimensions.fast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceRKReduction on all axes, output size should be 1.onnxruntime::ValidateFastReduceRKRfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKselect_last_indexD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseInvalid 'pads' attribute valueD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.honnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueedgereflectconstantCannot use 'edge' mode to pad dimension with a value of 0. Input shape:D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::PadValueFromFloatUnexpected mode of Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]onnxruntime::Pad::Computepads_tensor.IsDataType()Pads tensor should be an INT64 tensorValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1data_rank * 2 == pads.size()'pads' has wrong number of valuesonnxruntime::PadImpldata_rank > 0Input tensor has no dimensionsonnxruntime::PadInputWithDimValueOfZeroonnxruntime::GatherND::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor data type not supportedGatherNDindicesGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrbatch_dimsindices tensor must has rank larger than 0invalid index found, index = X input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeNull input ptrDepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpaceSpaceToDepth requires input width to be a multiple of block_sizeDepthToSpace requires input depth to be a multiple of (block_size * blok_size)DCRonnxruntime::SpaceDepthBase::SpaceDepthBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceDepth ops require a 4-D input. Provided rank: SpaceToDepth requires input height to be a multiple of block_sizeblocksizeAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()input count mismatchonnxruntime::SpaceToDepth::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccUnsupported input type in SpaceToDepth op: onnxruntime::DepthToSpace::ComputeUnsupported input type in DepthToSpace op: GatherElements op: Data type of input 'data' should match the data type of the outputGatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: onnxruntime::GatherElements::GatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElements op: Unsupported tensor type, size:onnxruntime::core_implD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.ccGatherElements op: Out of range value in index tensoronnxruntime::GetIndexInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.ccTile doesn't have an implementation yet for the type: 'repeat' input tensor must have the same length as the 'input' tensorTile doesn't support string type yet!input_tensor.IsDataType()onnxruntime::Tile::Compute. shape= must be 1 instead of Dimension of input input_shape[i] == 1D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeonnxruntime::Squeeze::Computeonnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0splitSplit operator does not support yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::Compute Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Cannot split using values in 'split' attribute. Axis= Input shape= NumOutputs=Input cannot be split evenly on selected axis. Input shape= Axis=An split tensor must be a vector tensor.onnxruntime::Split::ComputeImplsplit_tensor->Shape().NumDimensions() == 1Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.h!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attributeonnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()'axes' has duplicates'axes' has an axis outside of the tensor dimension count'step' value cannot be 0onnxruntime::SliceBase::Computeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeStarts and steps shape mismatchStarts and axes shape mismatchCannot slice scalarsData type for starts and ends inputs' is not supported in this build. Got onnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayStarts and ends shape mismatchEnds must be a 1-D arrayoutput == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator () got: Cannot concatenate scalarsinput_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: onnxruntime::ConcatBase::PrepareForComputeinput != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccNon concat axis dimensions must match: Axis has mismatched dimensions of onnxruntime::ConcatBase::ComputeImplData type mismatchonnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributenew_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.hdst.DataType() == src.DataType()src and dst types must matchD:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopyonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopydst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()last >= firstcounter.current_offset == lastonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefault0.0f <= ratio_value && ratio_value < 1.0fseedMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hacross_channelsonnxruntime::MeanVarianceNormalization_1::MeanVarianceNormalization_1MeanVarianceNormalization CPU EP only supports NHW and NCHW reduction for axes attribute.onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("across_channels", &across_channels_).IsOK()info.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4info.GetAttr("size", &size).IsOK()sizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()biassize_ % 2 == 1size_ > 0alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()Flattengsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisonnxruntime::Flatten::FlattenD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.ccscale must be 1D tensor with size zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size onnxruntime::DequantizeLinear::Computezero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })DequantizeLinear with type int32 should have no zero point or all zero points should be 0Sizeright.NumDimensions() == 2 N:onnxruntime::GemmHelper::GemmHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hleft.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0 K: GEMM: Dimension mismatch, W: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transB", &temp).IsOK()info.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBasec_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasnum_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falseleft operand cannot broadcast on dim right operand cannot broadcast on dim Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchnum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMulComputeHelper::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computeonnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.hmin_ <= max_onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl<__int64>::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.stod argument out of rangeinvalid stod argumentstoull argument out of rangeinvalid stoull argumentstoll argument out of rangeinvalid stoll argumentonnxruntime::`anonymous-namespace'::Cast::CastAttribute to is not set.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorsnprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().onnxruntime::`anonymous-namespace'::CastToStringsnprintf_result > 0snprintf() failed with return value: %.8gINF-INFNaNinvalid expand shapeEinsum op: There must be atleast one inputEinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsuminfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeequation->Einsum op: An implementation for the input type is not supported yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cconnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringonnxruntime::PoolAttributes::InferOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()onnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()ponnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.GlobalLpPoolpads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]Pad should be smaller than kernel.kernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()LpPoolkernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.cc`InlinedVector::at(size_type)` failed bounds checkUnsupported pooling size : onnxruntime::Pool::Computeonnxruntime::MaxPoolV8::ComputeImplInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar.OneHotonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp::ComputeDepth is negative.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::TopkOpset9ConstructorCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommononnxruntime::TopkOpset11ConstructorCommonlargestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()kop_kernel_info.GetAttr("k", &k_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()TopKvalue of k must not be negative] should not be greater than specified axis dim value [k argument [input count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1output count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!num_inputs >= 1onnxruntime::SequenceConstruct::ComputeViolation of the requirment that all input tensors must have the same data type.SplitToSequenceSplitToSequence operator does not support ) != split_dim_size (split_size_sum (Invalid data type for split tensor SequenceInsertonnxruntime::CreateCopyAndAppendCpuTensor), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceInsert::ComputeSequenceEraseonnxruntime::SequenceErase::ComputeSequenceConstructSequenceAtonnxruntime::GetSeqIdxD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cc) specified for sequence of size (Invalid sequence index (SequenceEmptyUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSSequenceLengthonnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputonnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Split should be > 0split_scalar > 0ConcatFromSequenceGot nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSETonnxruntime::ConvAttributes::ConvAttributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape.dim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeoutput_shapegroup count is <= 0 num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hInvalid input shape: output_padding X: X num_dims does not match W num_dims. group: ConvTransposeonnxruntime::ConvTranspose::DoConvTransposeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccNot enough elements in strides. Expected: Output channels M is not divisible by group.Not enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeNot enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported. C: kernel channels: M: Input channels C is not equal to kernel channels * group.output and sum shape must matchD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computepads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0strides_.size() == kernel_shape_.size()info.GetAttrs("kernel_shape", kernel_shape_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolonnxruntime::MaxUnpool::ComputeMaxUnpoolIndex 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.cc inferred output shape:Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape:Detonnxruntime::Det::ComputeMatrix dimensions are not equal. Square matrix is expectedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccCompressx_ptr != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear::Computebatch_axisonnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.h. Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=onnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of . Value must be in range [0,Invalid sequence length: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativeFailed to obtain detect_positiveonnxruntime::IsInf::IsInfFailed to obtain detect_negativedetect_positiveT4onnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccprepacked_buffers[0].get() == nullptronnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::ComputeOffsetQLinearConv : zero point of per-channel filter must be sameW_zero_point_data[i] == W_zero_point_valueIsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeOffsetIsValidQuantParam(W_scale, M)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidonnxruntime::QLinearConv::ComputeOutputScaleQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_Zero_Point)D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeConvIntegerMust be a scalar or 1D tensor or size 1.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 ()MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)MatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)onnxruntime::MatMulInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccQLinearMatmul : 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 : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)onnxruntime::QLinearMatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)*!sw.empty()Duplicate stopwords not allowedstopwordsEmpty stopwords not allowedwstr != wconv_errorSingle dimension value must be greater than 0p.secondStopword contains invalid utf8 charsattribute case_change_action is not setLOWERonnxruntime::StringNormalizer::StringNormalizercase_change_actionattribute case_change_action has invalid valuelocaleUPPERNONEFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleConversion Error:Please, install necessary language-pack-XX and configure localesis_case_sensitiveattribute is_case_sensitive is not setD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USStringNormalizerConversion Errorbad conversionInput dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 charsUpsample: input shape needs to be at least a single dimension.onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeResize: input/output value is nullptrInput dim is zero but required output dim is non-zero. output_dims[i] == 0Upsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input/output value is nullptrResize: input/output value's dimension mismatchDimension: Cannot scale 0 by any factor to generate a non-zero value. Output dim value: Input dim value: onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.sizes == nullptrInvalid roi input index.roi_input_idx_ > 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()Either scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeUpsample: unexpected modeonnxruntime::Upsample::BaseCompute: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Rank of input and output tensor should be same.output_dims.size() == dims.size()scales[3] == 1.0f: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.onnxruntime::Upsample::BaseComputeonnxruntime::UpsampleNearestimpl_->min_gram_length_ > 0Required min_gram_length must be positive: min_gram_length is requiredmin_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_gram_lengthTFD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizerimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFmode is requiredTfIdfVectorizerstatus.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not providedpool_int64s!pool_strings.empty()Number of items must compose whole end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams but ngram_indexes size: must be of equal sizeweightsstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })pool_strings must not be empty if specifiedpool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: Negative ngram_indexes values are not allowedstatus.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredngram_indexesimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: max_skip_count is requiredmax_skip_count <= status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredngram_countsInput shape must have either [C] or [B,C] dimensions with B > 0.(items % ngram_size == 0) id: onnxruntime::ngram_details::PopulateGramsp.first->second->id_ == 0Duplicate ngram detected, size: lambdD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()EyeLike : Input tensor dimension is not 2EyeLikeonnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims_size() == 1Must have a single dimensionD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.ht_proto_p->dims()[0] == 1Must have a single dimension of 1onnxruntime::`anonymous-namespace'::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.Unsupported output datatype with size: D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: Tensor proto with external data for value attribute is not supported.ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoutils::HasDataType(t_proto)Unsupported value attribute datatype: !utils::HasExternalData(t_proto)sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was p_mlvalueonnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was onnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Init but has batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::ComputeScan<8> spec does not support transpose of output. This should never be called.onnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesD:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hroi_batch_id < batch_sizeroi_batch_id >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::ComputeR->Shape()[1] == 5MaxRoiPoolspatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()onnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shapepooled_width_ > 0pooled_height_ > 0pooled_shape.size() == 2D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hLpNormalizationop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.h vs. Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: InstanceNormalizationonnxruntime::InstanceNorm::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::InstanceNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()sample_sizeinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomUniform::RandomUniformlowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttrs("shape", shape).IsOK()onnxruntime::RandomNormalLike::RandomNormalLikehighinfo.GetAttr("high", &high_).IsOK()info.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hscalemeannum_classes is < 1num_samples is < 1Invalid data type of Output type not supported in this build: Could not infer data type from input tensor with data type onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorbatch_size is < 1onnxruntime::RandomNormalLike::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterQuantizationKernelsLOGISTICSOFTMAXTreeEnsembleRegressorSOFTMAX_ZEROSUMonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccInput shape needs to be at least a single dimension.nodes_nodeidsnodes_modesnodes_missing_value_tracks_truenodes_hitratespost_transformnodes_valuesnodes_truenodeidsnodes_treeidstarget_weightstarget_treeidstarget_nodeidstarget_idsD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_hitrates_as_tensornodes_values_as_tensortarget_weights_as_tensor-base_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor (truenode).Unable to find node One falsenode is pointing either to itself, either to another tree. (falsenode).base_values.empty() is already there. in tree Node base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()onnxruntime::ml::detail::TreeEnsembleCommon::computen_targets_or_classes > 0aggregate_functionUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initnodes_featureidsnodes_falsenodeidsn_targetsbase_valuesonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == (size_t)n_targets_or_classes_D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScorespredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction but input tensor has One path in the graph requests feature onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg (weights).TreeEnsemble only works on 1D, 2D tensors.onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg features.TreeEnsembleClassifierX dims is empty.onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclass_weights_as_tensorclasslabels_int64sonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initclass_idsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeclasslabels_stringsclass_weightsclass_treeidsclass_nodeidsonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictionvalues_int64skeys_int64skeys_strings_Unuseddefault_floatvalues_floatsInput of tensor(int64) must have output of tensor(string)Input of tensor(string) must have output of tensor(int64)values_stringskeys_floatsdefault_int64info.GetAttr("default_string", &default_string_).IsOK()LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()default_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hclasses_stringsinfo.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::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,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2 attribtues in LabelEncoder (name: num_keys == num_valuesThe and the number of values is ) must have the same length. However, the number of key is Input features_per_batch[] != number of classlabels[D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpZipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countZipMapclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.LINEARPOLYD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonSVMRegressorkernel_typeinfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramsD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::ComputeUnexpected mode:num_features == feature_count_one_classcoefficientsonnxruntime::ml::SVMRegressor::SVMRegressor!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()support_vectorsn_supportsinfo.GetAttrs("rho", rho_).IsOK()rhoD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.honnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0Unsupported data type of classlabels_intsproba_.size() == probb_.size()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()prob_bprob_aD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifiervectors_per_classSVMClassifieronnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of ScalerEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOpScale size: () != (offset!scale_.empty()Empty scale in attributesUnknown Category and zeros = 0.OneHotEncodercats_int64sOne and only one of the 'cats_*' attributes must be definedcats_stringsonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()num_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccNormalizerinfo.GetAttr("norm", &norm).IsOK()normD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizeronnxruntime::ml::MakeNormalizeInvalid normalize value of MAXD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=info.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorinterceptstargetsLinearRegressoronnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:multi_classonnxruntime::ml::LinearClassifier::LinearClassifierLinearClassifiershape_size == out.length()onnxruntime::ml::CastInputToFloatInvalid typeimputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpimputed_value_int64sreplaced_value_floatImputerimputed_value_floatsEmpty input dimensions.Empty value of imputed values.Invalid input type:onnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccNumber of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()FeatureVectorizer) does not match number of inputdimensions values (onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()inputdimensionsDictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpstring_vocabularyint64_vocabularyInput of int64 must have output of string CategoryMapperInput of string must have output of int64info.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_strings", string_categories).IsOK()%finvalid stof argumentstof argument out of range0.fUnexpected CAST_TO value of Expected std::map or std::mapInvalid input type of value: map_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEinfo.GetAttr("map_form", &attr).IsOK()max_mapD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMaponnxruntime::ml::MakePackcast_to Expected DENSE or SPARSEInvalid PACK_MAP value of DENSESPARSEInvalid CAST_TO value of onnxruntime::ml::MakeCastTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64TO_FLOATTO_STRINGonnxruntime::ml::CastMap::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0BinarizerInput data with index: is NaNthresholdArrayFeatureExtractor) >=Invalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.Invalid Y argument: index is out of range: Y[] (D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cconnxruntime::LayerNormImpl::LayerNormImplop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::`anonymous-namespace'::ComputeImplSize of X.shape()[axis:] == . Size of scale and bias (if provided) must match this. Got scale size of and bias size of onnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesNumber of entries in '' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::Infonum_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first output is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextiteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but did not.validInvalid values in ''. 0 == forward. 1 == reverse.Unsupported typeInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid activation function of alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fweights.quant_para_A + (M * K) <= A_endD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplussoftsigneluhardsigmoidscaledtanhthresholdedreluleakyrelureluaffineInput initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {Input B must have shape {Input R must have shape {Input W must have shape {}. Actual:Input X must have 3 dimensions only. Actual:onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInput 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 < Left shape override: Left shape: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Right shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionAnother operand has a dim value of . The shape is: is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibledim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the inputoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Einsum op: The candidate output does not match the actual output's shapeIncompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: Data types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Inverseonnxruntime::contrib::SkipLayerNorm::SkipLayerNormD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorminput is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchScaleonnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelInput is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerBias size () does not match the number of channels (onnxruntime::contrib::ImageScaler::ImageScalerinfo.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSlice) needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] (Input's width (Crop) + scale_[1] (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::ComputeAttribute border needs to be specified with four border elements, got ) + bottomBorder ( input dimensions insteadInput's height () needs to be greater than or equal to the topBorder (onnxruntime::contrib::BifurcationDetector::Computepred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)max_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_dataBifurcationDetectoronnxruntime::contrib::BifurcationDetector::BifurcationDetectorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0min_ngram_size_ > 0max_ngram_sizengram_sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0info.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computetoken_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()TidNGramRepeatBlockscores_dims.size() == 2scores_dims[0] == batch_sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::ComputeCDistonnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hmetricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistsqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:Null crop_size_ptrNumber of dimensions for crop size should be exactly 1CropAndResize specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResizeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeTransposeMatMulinput_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::ComputeCurrently support only COO and CSR(x64) formatsCurrently supporting only 2-D matricesSparseToDenseMatMulCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cconnxruntime::contrib::SparseToDenseMatMul::ComputeOuter size must be M + 1Expecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesExpecting COO 2-D indices shape is out of bounds of lhs_right: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()COO m index: is out of bounds of out_left: COO k index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()char_embedding_sizeconv_window_sizeembedding_sizeConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: WordConvEmbeddingD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::Compute!tokenexp.empty()Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is settokenexp!char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezation!separators.empty()separators must not be emptyInput string contains invalid utf8 charsCan not digest tokenexp: Can not digest separators: tensor(string) expected as inputMatch contains invalid utf8 chars: Input string contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedonnxruntime::contrib::Tokenizer::Tokenizerattribute mark is not setmarkTokenizermincharnumattribute pad_value is not setpad_valueD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccseparatorsmincharnum_ > 0attribute mincharnum must have a positive valueattribute mincharnum is not setonnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3AttnLSTMAttention mechanism memory sequence lengths must have shape { found!], while Attention mechanism memory sequence lengths value must in (0, onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: Attention mechanism memory shape error! Expected: {}. Got: Attention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {, am_attn_size}, Got:Attention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplparameters_.model_type == 0 || parameters_.model_type == 1onnxruntime::contrib::transformers::GreedySearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.ccencoder should be a scalar. Got shape of Node input is requiredGreedySearchonnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoNot ImplementedSubgraph SessionState was not found for 'decoder' attribute.decoder_session_stateinfo.GetAttr("encoder", &proto).IsOK()decoderinfo.GetAttr("decoder", &proto).IsOK()gpt_subgraph_ == nullptronnxruntime::contrib::transformers::GreedySearch::Computedecoder_feeds_fetches_manager_D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.hmax_lengthonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsmin_lengthonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::InitializeInput 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'input_ids' is expected to have 2 dimensions, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is ].Axis must be within range [An axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::Computeaxis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfot5_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyBeamSearchparameters_.model_type == IBeamSearchParameters::kModelTypeGpt || parameters_.model_type == IBeamSearchParameters::kModelTypeT5onnxruntime::contrib::transformers::BeamSearch::Initencoder_session_stateencoder_feeds_fetches_manager_Encoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::ComputeSubgraph SessionState was not found for 'encoder' attribute.num_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.onnxruntime::contrib::transformers::BeamSearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.hparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchT5::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Executeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenqkv_hidden_sizesD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hInput 'input' is expected to have 3 dimensions, got Attention cannot have past sequence and extra add qkqkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads.hidden_size should be divisible 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::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::Attention::ComputeInputs '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 Input '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)D:\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 given4D mask in attention cpu kernel is not supportedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttentionSampleOpQGemm : 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.hQGemm : 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()))TCTBTYTYZQGemm : 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)Word embedding scale 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 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::QEmbedLayerNorm::ComputeGamma zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationBeta zero point must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 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 1onnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::NhwcMaxPool::Computeinput_shape.Size() > 0 || N == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccMatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::MatMulIntegerToFloat::ComputeInput } for per-tensor/layer quantization or shape { must have shape {} for per-channel quantization. Actual:, 4*onnxruntime::contrib::DynamicQuantizeLSTM::PrePackD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.ccWeight point must be constantRecurrentWeight zero point must be zeroDynamicQuantizeLSTM : W_scaleR_scaleW_zero_pointR_zero_pointonnxruntime::contrib::QAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1.QLinearAddQLinearMulD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\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 1IsScalarOr1ElementVector(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 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13QLinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxopset must be existed in attributes of QlinearSoftmaxinput_shape of QLinearSoftmax must be existedx_shape != nullptr && x_shape->dim_size() > 0onnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.ccQLinearLeakyReluQLinearSigmoidtensor_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\quantization\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearAveragePool::ComputeImplInput x_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinear Pooling unsupported pooling size!IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if givenWrong 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\quantization\qlinear_concat.ccEach input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (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 >= 3 && input_count_x3 % 3 == 0onnxruntime::contrib::QLinearConcat::ComputeIsScalarOr1ElementVector(tensor_y_zero_point)IsScalarOr1ElementVector(tensor_x_zero_point)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.cconnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.cconnxruntime::contrib::MatMulInteger16::ComputeA != nullptr && B != nullptrMatMulInteger16(channels % 4) == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cconnxruntime::contrib::ReorderInput::ComputeX_rank == 4Unsupported mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported transformation mode '' for NCHWc Upsample(X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::contrib::NchwcPoolBase::NchwcPoolUnsupported convolution size.(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)X_shape.NumDimensions() == 4onnxruntime::contrib::NchwcConv::Computechannels_ <= X_shape[1]onnxruntime::contrib::ReorderOutput::Computeonnxruntime::contrib::NchwcUpsample::ComputeX_shape.size() == 4channels_ > 0invalid channel countonnxruntime::contrib::ReorderOutput::ReorderOutputinfo.GetAttr("channels", &channels_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::contrib::ReorderInput::ReorderInputinfo.GetAttr("channels_last", &channels_last_).IsOK()info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1scales_.size() == 4onnxruntime::contrib::NchwcUpsample::NchwcUpsampleinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBasepool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcConv::NchwcConv' is specified but is empty.(for attribute ''.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cconnxruntime::ml::GetNumberOfElementsAttrsOrDefaultn_dims == 1Attribute '' must be a vector.Attribute:'n_elements > 0' has one dimension but is empty.proto.data_type() == proto_typeUnexpected type (onnxruntime::ml::GetVectorAttrsOrDefaultInput 1 dimension 0 should have same length as the last dimension of input 0Input 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got [seqno= row[.. rows[not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemoryprefix.length() == prefix_lengthonnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::ProcessD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.ccsubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph output present_key_self_0 is expected to have 4 dimension, got subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizeonnxruntime::contrib::transformers::Subgraph::SetupD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.ccsubgraph past state is expected to have 5 dimension, got onnxruntime::contrib::transformers::Subgraph::GetParameterssubgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headsmodel_typeeos_token_idpad_token_iddecoder_start_token_idno_repeat_ngram_sizecontext != nullptronnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.ccrepetition_penalty > 0.0finput_ids shall have 2 dimensions. Got dims.size() == 2) shall be greater than input sequence length (max_length (max_length > sequence_length) shall be no more than max_length <= kMaxSequenceLengthrepetition_penalty shall be greater than 0, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.cc) shall be be no more than num_beams (num_return_sequences (num_beams >= num_return_sequencesearly_stoppingonnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got num_return_sequences >= 1Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2onnxruntime::contrib::transformers::GptSubgraph::Validatesubgraph input 0 shall be named as input_ids, got: input_idssubgraph input 1 shall be named as position_ids, got: position_idssubgraph input 2 shall be named as attention_mask, got: attention_masksession_state_ != nullptrSetup must be called before CreateInitialFeedsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.cconnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).input_ids_shape.NumDimensions() == 2subgraph output 0 (logits) shall be float or float16 data typesubgraph input 2 (attention_mask) shall have int32 typesubgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph input 3 shall be named as past_0, got: past_0subgraph output 0 shall be named as logits, got: logitssubgraph input 1 shall be named as present_0, got: present_0subgraph input 1 (position_ids) shall have int32 typesubgraph input 0 (input_ids) shall have int32 typeonnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsBeamSearch op: An implementation for the input type logits_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedslast_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferelement_type == DataTypeImpl::GetType()unimplemented activation: activation_params count mismatchInput 0 and 1 shall have same shapeinput_ids and position_ids shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeposition_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have size of Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZeroonnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()Batch can only be done if all beams have been generatednext_scores.size() == next_indices.size()beam_idx == num_beams_beam_hyp.Size() >= gsl::narrow_cast(num_beams_)sequences != nullptrhypothesis_buffer_offset_ <= hypothesis_buffer_length_top_k <= Size()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamHypotheses::Outputonnxruntime::contrib::transformers::BeamSearchScorer::Initializenext_beam_scores_.empty()output_sequences != nullptronnxruntime::contrib::transformers::BeamSearchScorer::Finalizeencoder_hidden_statesdecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typeonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesonnxruntime::contrib::transformers::T5DecoderSubgraph::ValidatekFirstPastInputIndex currently only supports 2 or 3number of outputs expected to be kFirstPastInputIndex + 4 * layers, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.ccdecoder subgraph input 0 shall be named as input_ids, got: number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder_attention_maskInvalid hidden_states expension: has_hidden_state_ == falseonnxruntime::contrib::transformers::T5EncoderSubgraph::Validateexpect 3 inputs, got:expect >=6 outputs, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.ccencoder_input_idsnumber of outputs expected to be 2 + 4 * layers, got:encoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0encoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeonnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsencoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsencoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0cpu_allocator shouldn't be nullptrQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QlinearBuildLookupTableD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.ccOrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled already is not supportedCudaPinnedCudaOpenVINO_GPUDMLonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccSpecified device is not supported.OrtApis::GetTensorTypeAndShapeD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccthe ort_value must contain a constructed tensor or sparse tensorArgument is not a tensorUnsupported indices_format passed`anonymous-namespace'::GetIndicesTensorNot implementedtype_proto is not of type sequence!type_proto is not of type map!strides.empty()shape.Size() must >=0D:\a\_work\1\s\onnxruntime\core\framework\tensor.ccStrided tensor is supported for training only for now.onnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Inittensor failed memory size calculationtensor size overflowp_type != nullptronnxruntime::Tensor::TensorTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::IDataTransfer::CopyTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensorD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopySparseTensorsonnxruntime::data_types_internal::MapTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.hexpected a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeelem_proto != nullptronnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::ToDataContainer(null)onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())utils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypethisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatibleuint64float16uint32int64tensor type bfloat16(unknown type)doubleint8floatbooluint16int32uint8int16onnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFuncWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())D:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredonnxruntime::data_types_internal::IsCompatibleMLDataType for: sparse tensor type is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitionFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooStringsExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooDataFormat() == SparseFormat::kCsrcMust contain Csr format. Contains: allocator_ != nullptrThis method should follow a call to constructor that supplies the allocatoronnxruntime::SparseTensor::GetCooIndexDimsvalues_count == index_sizeIndex size: must be equal to or twice the values size: Use MakeCooStringsNot expecting an allocator setonnxruntime::SparseTensor::UseCooIndicesSparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCsrIndicesThis method does not expect allocator to be setOuter index count must be rows + 1 or zero. Got: rows: Must contain BlockSparse format. Got: onnxruntime::SparseTensor::MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataUse MakeCsrStringsdense shape must 2-D. Got: format_data_.size() == 2UExpecting two indices. Got: onnxruntime::SparseTensor::AsCsrExpecting inner index size: the same as values size: Inner and Outer indices must either be both zero or non-zeroonnxruntime::SparseTensor::ValidateCsrIndicesMust contain Coo format. Got: SparseTensor Allocation failed for size: onnxruntime::SparseTensor::AllocateBufferValues size format_data_.size() == 1UExpecting to contain one index, got: onnxruntime::SparseTensor::AsCooFormat() == SparseFormat::kCooD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cconnxruntime::`anonymous-namespace'::CopyDataMust have the same size. Got src_size: dst_size: must be less than total buffer size: this tensor already has populated sparse_indicesonnxruntime::SparseTensor::GetSparseTensorFromOrtValuethe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::UseBlockSparseIndicesExpecting fully sparse tensors to have indices shape {0}Expecting fully sparse tensors to have value shape {0}Expecting index blocks: to device type: onnxruntime::SparseTensor::MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataUse MakeBlockSparseStringsExpecting to have at lest 3-D shape. Got:Expecting one index. Got: onnxruntime::SparseTensor::AsBlockSparseFormat() == SparseFormat::kBlockSparse to be equal to values blocks: Indices shape must have dim[0] == 2Expecting indices to have 2-D shape . Got: onnxruntime::SparseTensor::ValidateBlockSparseShapesDestination should be emptyThis instance should not be emptyonnxruntime::SparseTensor::CopyUnable to find a data transfer for copying from device type: Must have the same shapeSrc and Dst must be of the same typeX-device copy of strings not supportedDestination must have a CPU allocator setdimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.Invalid tensor shape slice argument.onnxruntime::TensorShape::SizeFromDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimension}{onnxruntime::TensorShape::Slicedimstart <= dimend && dimend <= values_.size()D:\a\_work\1\s\onnxruntime\core\framework\config_options.cconnxruntime::ConfigOptions::AddConfigEntryConfig with key [] already exists with value []. It will be overwrittenConfig value is longer than maximum length 1024Config key is empty or longer than maximum length 128onnxruntime::SessionOptions::AddInitializerBuffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.Received nullptr for OrtValueinit_num == values.size()Expecting same size spansAn OrtValue for this name has already been added: D:\a\_work\1\s\onnxruntime\core\framework\session_options.ccReceived nullptr for nameonnxruntime::SessionOptions::AddExternalInitializersNo attribute with name: is defined.Attribute: expected to be of type: but is of type: No attribute with this name is defined.Attribute name and type don't matchRequested attribute: is expected to have type: node_offsets_index < node_offsets_size_D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetonnxruntime::OpKernelContext::GetOrCreateOutputMLValueInvalid arg_num of . Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrCPU allocator not foundTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Execution frame was nullOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cconnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0 node_version: Version mismatch. and type (Op with name (static_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptr kernel_end_version: kernel start version: ), but the node in the model has the following type (onnxruntime::`anonymous-namespace'::MatchKernelDefTypesD:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.ccThis op has been implemented only for the following types (: Conflicting with a registered kernel with op versions.Failed to add kernel for Encountered following errors: ( kernel is not supported in Kernel not foundkernel def can't be NULL However the types are incompatible. (node_version: in the supported version rangeFound kernel for Op with name (Candidate for fallback CPU execution: onnxruntime::GetCpuPreferredNodes::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cckernel_info != nullptronnxruntime::GetCpuPreferredNodes capable of executing this node because the CPU execution path is deemed faster than overhead involved with execution on other EPs ORT optimization- Force fallback to CPU execution for node: cannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoD:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cc type: UnpackTensor: the pre-allocated size does not match the raw data size, expected ) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protodata overflownullptr == p_dataonnxruntime::utils::UnpackTensorWithExternalDataImpl) in proto, external_data.length: external data size mismatch. Computed size: TensorProto: `anonymous-namespace'::ReadExternalDataForTensorTensor does not have external data to read from.`anonymous-namespace'::GetExternalDataInfoD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccExternal data type cannot be UNDEFINED or STRING.*/_ORT_MEM_ADDR_/*onnxruntime::utils::DenseTensorToSparseTensorProto data_type: onnxruntime::utils::UnpackInitializerDataUnsupported type: Element_size of: Unsupported sparse tensor data type of Must have a valid data typeHasDataType(dense_proto)cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::SparseTensorProtoToDenseTensorProto is not supported.onnxruntime::utils::TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: External initializer: TensorProtoToTensor() tensor shape mismatch! can not be writen into Tensor type TensorProto type are out of bounds or can not be read in full. given file_length: size to read: offset: onnxruntime::utils::GetFileContentutils::HasExternalData(tensor_proto)onnxruntime::utils::GetExtDataFromTensorProtoGetFileLength for Invalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Sparse Indices raw data size does not match expected.onnxruntime::utils::CopySparseDataSparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedonnxruntime::utils::TensorProtoToMLValueonnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Unsupported attribute value type of TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated buffer, Got The preallocated buffer is too small. Requires Invalid TensorProtoDuplicate allocator for OrtMemType:onnxruntime::AllocatorManager::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cc New allocator: Existing allocator: device:Received invalid value of arena_extend_strategy onnxruntime::CreateAllocatorIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_onnxruntime::IExecutionProvider::GenerateMetaDefIdonnxruntime::IExecutionProvider::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccUnsupported element size: Support 2-D matrices onlyInput must be of CSR formatCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::DenseTensorToSparseCsronnxruntime::sparse_utils::SparseCooToDenseTensor > dense_size: Invalid index: onnxruntime::sparse_utils::DenseTensorToSparseCooOuter indices must be M + 1. Got: outer_num == (rows + 1)Input must be of COO formatExpecting indices to be equal the number of values or be twice as manyUnable to convert strings tensor to a sparse tensor that is not on CPUExpecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::SparseCsrToDenseTensoronnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsdata_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from invalid allocator.Unsupported OrtValue type.fetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsUnsupported OrtValue type to copy between device.onnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedFailed to find allocator for device allocator != nullptronnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccSNPEExecutionProviderInternalTestingExecutionProvideronnxruntime::utils::ExecuteGraphonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::ExLibLoader::LoadExternalLibA dso with name Failed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoader has already been loaded.Caught exception while loading custom ops with message: onnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: Unloading DSO D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccD:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::SetupAllocatorsonnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingAllocator already registered for . Ignoring allocator from session.disable_prepackingCould not find OrtValue with idx 'onnxruntime::VerifyEachNodeIsAssignedToAnEponnxruntime::VerifyEachNodeIsAssignedToAnEpImpl]. Number of nodes: Node placementsonnxruntime::SessionState::CreateSubgraphSessionStatesubgraphCould not find an implementation for ) node with name 'onnxruntime::SessionState::GetNodeIndexInfonode_index_info_.has_value()Main Graph instance should have populated all subgraphs when being resolved.onnxruntime::SessionState::UpdateToBeExecutedNodesexisting_entries.find(attribute_name) == existing_entries.cend()Entry exists in node SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionState Attribute:onnxruntime::SessionState::FinalizeSessionStateImpl' OpType: Index:subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulatoronnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateRerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator () Node(s) placed on [ All nodes placed on [Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf. onnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()allocator_for_caching.get() != nullptrweights_to_be_filled_in.buffers_.size() > 0 doesn't have an implementation that can cache computed pre-packed weights doesn't have an implementation that can consume provided pre-packed weightsonnxruntime::KernelUseSharedPrePackedBuffers+The kernel corresponding to the node . Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::CreateKernelsduplicated ort_value index:onnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.onnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty() for attribute Using an input in multiple nodes on different devices is not supported currently. Input: is used by node Only one node should produce an output. Existing entry for Failed to find input name in the mapping: Unable to write the provided PrePackedWeights instance into the containerUsing cached version of pre-packed weight for constant initializer: ) and node (!op_type.empty()The op type of a node cannot be empty used in the node: which is of op type: entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'p_op_kernelbad variant accessThe node is not placed on any Execution Provider. (node Failed to find kernel for in KernelRegistryManager). found duplicated provider onnxruntime::utils::GetMLDataTypenullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolveronnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferktsrFailed to verify KernelTypeStrResolver flatbuffers data.D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping feeds: D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccNo provider specified.onnxruntime::PartitionOrtFormatModelonnxruntime::GraphPartitioner::Partitiononnxruntime::PartitionOnnxFormatModelonnxruntime::InlineNodessingle_node_compute_func should have 1 element.onnxruntime::PartitionOrtFormatModelImplonnxruntime::PartitionOnnxFormatModelImplNode ' did not return correct number of compiled functionsThe Function Style fusion is deprecated., but was not selected by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP. with domain: was inserted using the NHWC format as requested by D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::TryAssignSingleNodeonnxruntime::GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.onnxruntime::KernelLookup::KernelLookup!provider_type_.empty()1 == indexed_sub_graph.nodes.size()D:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.hCreate state function failed. Return value:D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.hprovider_type must be specified.API VERSION onnxruntime::FunctionKernel::CreateAl with following configs: initial_chunk_size_bytes: Creating BFCArena for initial_growth_chunk_size_bytes: max_dead_bytes_per_chunk: arena_extend_strategy: memory limit: Could not find Region for onnxruntime::BFCArena::RegionManager::RegionForentry != regions_.end()onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionp_int < base_int + memory_size_Could not find Region for: p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.h is smaller than requested bytes of Available memory of Incorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator ()cudaMallochipMalloch < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) bins of max chunk size Creating onnxruntime::BFCArena::BFCArenaD:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc0 == memory_size % kMinAllocationSizec->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesceCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()tried to allocate 0 bytesh != kInvalidChunkHandleonnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end() size: Reserving memory in BFCArena for Total allocated bytes: Allocated memory at Extended allocation by onnxruntime::BFCArena::ExtendFailed to allocate memory for requested buffer of size bytes. BFC Arena shrunk by onnxruntime::BFCArena::Shrink The total allocated bytes is now bytes. !c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunk!chunk->in_use()onnxruntime::BFCArena::FindChunkPtrExtending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes= (requested) num_bytes: . bin_num:onnxruntime::BFCArena::AllocateRawInternal (actual) rounded_bytes:args entryDuplicate entry for kernel type str: kernel_type_strargskernel_type_str_argskernel_type_str_args entryop_kernel_type_str_args entryop_idop_kernel_type_str_argsonnxruntime::KernelTypeStrResolver::LoadFromOrtFormatonnxruntime::KernelTypeStrResolver::SaveToOrtFormat is null. onnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasonnxruntime::KernelTypeStrResolver::RegisterOpSchemaOp schema must be available.Duplicate entry for op id: onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrKernel type string already exists for formal parameter name 'onnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()Failed to find args for kernel type string '', but the existing argument with that formal parameter name has a different formal parameter type string.D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.cc'. If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.Failed to find op_id: onnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrInvalid ORT format model.khooffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexTensor shape cannot contain any negative value returned nullptrTrying to allocate memory for unused optional inputs/outputsbuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrameAllocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()Fetches vector passed to GetOutputs contains failed. Error:fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initshape && sp_tensor.DenseShape() == *shapeinvalid index onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()mlvalue.Fence() == nullptronnxruntime::AllocateSparseTensor. Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer, block in memory pattern size is: For ort_value with index: , fall back to default allocation behavior but the actually size is: ort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperonnxruntime::IExecutionFrame::GetMLValueD:\a\_work\1\s\onnxruntime\core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::IExecutionFrame::IExecutionFrameD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccnode_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()TraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFree size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::GetAllocationPlan failed: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()Expected shape from model of onnxruntime::ExecutionFrame::VerifyOutputSizes for output does not match actual shape of Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.ccCan't slice a non-tensor OrtValue. Type was onnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iterator. Dimension 0 is Invalid dim0_offset of Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsAllocated(). Shape:D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer::Createonnxruntime::utils::detail::CopyLittleEndianD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccsource and destination buffer size mismatchmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infomodel format error! Missing 'location'model format error!checksumlengthparsing failedlocationUnknown exception was caught by catch-all handler._fence_after_kernel_timethread_scheduling_statsproviderNon-zero status code returned while running node. Name:'' Status Message: _fence_beforeop_nameGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.onnxruntime::ParallelExecutor::RunNodeAsyncBegin executionParallelExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.ccException running nodes starting at '. onnxruntime::ParallelExecutor::ExecuteMultiple errors were found.D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalentry != nullptrAll implicit inputs should have OrtValue instances by now. does not.SequentialExecutor::Executeoutput_type_shapeinput_type_shapeoutput_sizeparameter_sizeactivation_sizeexec_plan_indexgraph_indexD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::Execute{"":[]}onnxruntime::ReleaseNodeMLValuesend >= starts_.back()Invalid 'end'. Value is larger than 'start'.onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndstarts_.size() == ends_.size() + 1No matching 'start' entry.starts_.empty() || start > ends_.back()Invalid 'start'. Value is smaller than previous 'end'.D:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.honnxruntime::AllocPlanPerValue::ProgramCounter::AddStartstarts_.size() == ends_.size()Previous entry was not terminated.found_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseonnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Indexonnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()Invalid program_counter entries at index !IsNonTensor(*node_output)Only tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::GeneratePlanForWeightsHelperspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GetLocationForNodeInputallocatorCan not find the execution provider onnxruntime::PlannerImpl::ComputeUseCountsp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the node onnxruntime::PlannerImpl::ComputeUseCounts::::operator ()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cc) : onnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::GenerateDeallocationPlanAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::VerifyMemoryTimeScheduleentry.program_counter.HasValidEntries()D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.cconnxruntime::PrePackedWeights::GetHashbuffers_.size() == buffer_sizes_.size()Failed to copy tensor to string tensor is not supported for copying between allocatorsonnxruntime::session_state_utils::DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires DeserializeTensorProto() takes either pre-allocated buffer or an allocator!D:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::ExtDataTensorProtoToTensorFailed memory size calculationsession.use_device_allocator_for_initializersDone saving initialized tensorsDeserialize tensor failed.Using user supplied initializer with name (Skipping entry for missing optional value at idx [Memory] SessionStateInitializer statically allocates bytes for entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (GraphSubgraph input with name is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.cconnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorUnsupported device allocator in the context of pre-packed weights caching: D:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncs not found.Can't use func with null ptrfunc info for node: already exist.!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.hcurrent <= buffer_size_[TypeInferenceError] NoOpConstantTypeProto must have shape for this to runshape != nullptronnxruntime::utils::GetShapeD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hOutput:Source and target must both be tensors , or optional typed entities , or sparse tensors. Falling back to lenient merge. target:ALLOW_RELEASED_ONNX_OPSET_ONLYby 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. 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::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:node_arg) does not exist in the graph.This is an invalid model. Graph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Subgraph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (output 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 nullgraph_proto != nullptronnxruntime::Graph::Graph' Model is invalid.Duplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: '. Please, fix your model.' 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.onnxruntime::Node::SaveToOrtFormatfbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()node_arg_name cannot be null], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:input index: onnxruntime::Node::LoadEdgesFromOrtFormatinput edges' source:Error merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccTensor element type mismatch. Input=Type mismatch. Current=onnxruntime::NodeArg::UpdateTypeAndShape . Got: Optional Type mismatch. Expected: ] op_type [Node [Serialization of fused function body is not currently supported, does not have the graph for key ) output arg () does not match expected type () of node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. Type Error: Shape of initializer , Error This is an invalid model. In Node, onnxruntime::Graph::VerifyNodeAndOpMatch) is not a registered function/op) Op (Node () does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.) in node () of operator (' of input parameter (This is an invalid model. Type Error: Type ' in node () bound to different types () of Optype (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) type inference failedSome 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.Graph attribute inferencing failed: in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInferenceInvalid node indexes specified when adding 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.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.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=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 ''. It is not used by any node and should be removed from the model.Removing initializer ''. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg ' must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != func_meta_defonnxruntime::Graph::CreateFusedSubGraphNode OpType:Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)onnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Node has no function body and cannot be inlined._token_ as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodeFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProto'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternalCannot find NodeArgs for [initializer_node_arg != nullptronnxruntime::Graph::CleanUnusedInitializersAndNodeArgsonnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!foundFailed to find existing initializer with name onnxruntime::Graph::ReplaceInitializedTensorImplTrying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()onnxruntime::Graph::InjectExternalInitializedTensorsReplaced external initializer: onnxruntime::Graph::SaveToOrtFormatFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorsparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0index < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::Graph::LoadFromOrtFormat::::operator ()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.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.callnode.TryGetFunctionProto(inlined_fp)onnxruntime::Graph::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::SetInputs) : ((""") -> (onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.absl::container_internal::raw_hash_map<>::at, but it its version is higherDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker., but it its domain is notthan the operator set version onnxruntime::GraphViewerToProtoD:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.ccMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: nodeInvalid ExecutionOrderonnxruntime::GraphViewer::GetNodesInTopologicalOrderNot supported with filtered graph.filter_info_ == nullptronnxruntime::GraphViewer::GetRootNodesIndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptronnxruntime::GraphViewer::GraphViewerD:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Input not found:nodearg is till opset D:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain onnxruntime::model_load_utils::ValidateOpsetForDomainModelProto does not have a graph.D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelMissing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unsupported model IR version: , max supported IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet are '0' and '1'. The environment variable contained the value: onnxruntime::SaveModelGraph is null. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatNo graph was found in the protobuf.Failed to load model with error: onnxruntime::Model::LoadProtobuf parsing failed. is less than 0. less than 0.Protobuf serialization failed.onnxruntime::Model::SaveNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatLoad model failed. File doesn't existsystem error number oOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.Output Value of alphaValue of betaConstrain input and output types to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccAttribute expected to have tensor type expected to have tensor or sparse typeNegative values are not allowed in a shape specification should specify a shapeInput's shape must be 4-D'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)) 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 () + scale[1] () + scale[0] (drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevgatesseq_lengthsGRUUnithiddenIf 1, mean and variance are computed across channels. Default is 0.Scaling valueIf 0, normalize the mean only. Default is 1.Constrain indices to integer typesConstrain input and output types to all tensor types.extra_shapeinput_as_shapeGivenTensorFillvaluesBias applied to each channel, same size as C.The scale to apply.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Threshold valuedataAttribute expected to have tensor or sparse tensor typekeyq_weightkv_weightkey_padding_maskvalue_cachekey_cachestatic_kvuse_pasthas_layer_statewindowglobal_weightmaskGglobal_biasglobalLongformerAttentionConstrain to integer typesqueryweightMpastextra_addpresentD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccConstrain mask index to integer typesOne sided attention windows length W, or half of total window lengthError parsing node: = Constant()Error unexpected extra input in node:key and value cache shall be 4 dimensionsWhether every token can only attend to previous tokens. Default value is 0.Number of attention headsHidden layer sizes of Q, K, V paths in Attentioninv_std_varConstrain mean and inv_std_var to float tensors.The NGram size.scoresscores_outConstrain scores input and output types to float tensors.The maximum NGram size for suffix matching.The minimum NGram size for suffix matching.src_tokensX_bias = Add (X, bias)one 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) X_bias = Identity (X)Constrain input and output types to float or half tensors.skipposition_embeddingsegment_embeddingembedding_sumConstrain input and output float tensors types.Constrain input and output integer tensors typesabhas_key_padding_masknew_value_cachenew_key_cacheConstrain key_padding_mask to bool tensors.Constrain input and output types to float and float16 tensors.DecoderAttentionThe epsilon value to use to avoid division by zero.segment_idsword_embeddingcur_tokensprev_suffix_match_idxpred_tokenstokenssuffix_match_idxConstrain to integer types.GemmFastGeluW̼+̼+C is null expected to have tensor or sparse tensor type: Incompatible dimensions expected to have rank but has rank expected to have rank >Dimension mismatch in unification between Constrain input and output types to all numeric tensors.TorchEmbeddingBoolean. 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.apply softmax to elements for dimensions axis or highertrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1(Optional) Seed to the random generator, if not specified we will auto generate one.A != mat_h:Input rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w:padding_idxscale_grad_by_freq'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Input tensors of wrong rank (0).Pads has incorrect number of valuesInputs 0 shall be 2 dimensionsIncompatible dimensions for matrix multiplicationFailed to parse num_beams or it is not positive integer scalarFailed to parse max_length or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccprefix_vocab_masksequencessequences_scoresConstrain mask to integer typesmodel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartConstrain 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 tensorsno repeat ngrams sizeThe id of the token that indicates decoding starts.model type: 0 for GPT-2; 1 for encoder decoder like T5early stop or notDecoder subgraph to execute in a loop.The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.repetition_penaltyvocab_maskConstrain the output to a boolean tensor.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.Three interpolation modes: bilinear (default), nearest and bicubic.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.GridConstrain input types to all tensor types.Constrain output types to float tensors.The id of the padding tokenThe id of the end-of-sequence tokenresidualratioConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.Constrain input 'training_mode' types to boolean tensors.isnan_onlyisinf_onlyIsAllFiniteBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.If true, check only for NaN.If true, check only for Inf, -Inf.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationsparse_tensor(float)sparse_tensor(int64)sparse_tensor(double)sparse_tensor(uint64)sparse_tensor(int32)sparse_tensor(uint32)The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.inputs are expected to have tensor type and output type should not be null.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredConstrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Scalar multiplier for the product of the input tensors.Constrain input and output types to float/int tensors.Whether B should be transposedWhether A should be transposedScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Input axis is invalid: activation_gammaInput/Output is a string tensornormalizedsignal_ndimIrfftRfftComplexMulConjComplexMulConstrain input and output types to float tensorsPadsZSecond input does not have rank 2First input does not have rank 2Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.roisbatch_indicescrop_sizeConstrain types to int tensors.Constrain types to float tensors.EpsilonRank = Size (XShape)XShape = Shape (X)idxInput 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".Constrains input to only numeric types.rois input tensor has wrong dimensionfirst input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionSequenceConstrain to tensor(float).Constrain to tensor(int32).'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayxConstrain 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.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.last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.Constrain input and output types to any tensor type.Constrain indice type to int32 or int64This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Constrain mean and inv_std_var to be float tensors.Constrain output Y, scale and bias type to float tensors.EfficientNMS_TRTConstrain output Y and scale type to float tensors.boxesanchorsnum_detectionsdetection_boxesdetection_scoresAttribute 'pooled_size' must be >= 1.pooled_sizeInput 0 shall be 3 dimensionstype used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).InvStdDevType of Mean and InvStdDev tensors.Constrain input X type to float tensors.Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)Scale2D = Flatten (Scale)NormalizedV = Cast (Normalized)B2D = Flatten (B)Scaled = Mul (NormalizedV, Scale2D)Biased = Identity (Scaled)Biased = Add (Scaled, B2D)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)max_output_boxesAttribute 'max_output_boxes' must be >= 1.Axis1D = Constant()Zero1D = Constant()NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)Mean2D = ReduceMean (XU)XU = Cast (X2D)MeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)target_device(Optional) Target device like CPU, DSP, etc.notes(Optional) Some notes for the modelinputsoutputsConstrain input and output types to uint8, uint16, float tensors.Constrain output 'mask' types to bit-packed uint32 tensor.Constrain 'training_mode' to boolean tensor.c2p_attentionp2c_attentiondisentangled_attentionspanMaximum relative distance, k.factorScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.DLCpayload of the SNPE DLC file.snpe_version(Optional) SNPE version used to convert the model.feature_map_0feature_map_1feature_map_2feature_map_3Image size.patchesPooled size.image_sizeDisentangledAttention_TRTPyramidROIAlign_TRTc2c_attentionBackground class ID.detection_classesEncoding type for the boxes or anchors inputs.background_classBox IOU threshold value.box_codingMax detections to output.iou_thresholdplugin_versionVersion number of the TRT plugin.score_activationActivation function to apply to the scores input.score_thresholdScore threshold value.MultilevelCropAndResize_TRT̼+ expected to have: or UNDEFINED. Got: Input data type does not match the expected data typeScale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is not specified should be of integer type and specify a type.Value of attribute Dimension= does not specify a valid type.Can't merge shape info. Both source and target dimension have values but they differ. Source=Constrain the input to float and bfloat.Constrain shape and strides to uint64.Constrain y to uint8.QuantizeBFPThe datatype to dequantize to.Shape of quantized tensor must be 1D.Constrain y to float and bfloat16.Constrain the input to uint8.DequantizeBFPx_scalex_zero_pointConstrain 'y', 'x_scale' to float tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.The type of BFP - must match with the BFPType enumNumbers within a bounding box will span across these dimensions.Any dimension not in this list is the same for all numbers within a bounding box.As an example, consider a 2D tensor with shape [d0, d1] and block_dims equal to [1].Within a bounding box, all elements will be within the same row but will be from different columnns.The default is the last dimension.bfp_typeblock_dimsC_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.y_scaley_zero_pointConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.cc Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} inputs are expected to have tensor type.{additionalDocumentation}{name}A_scaleA_zero_pointB_scaleB_zero_pointC_scaleConstrain input and output types to 8 bit tensors.Y_zero_point]. Its actual value is: 'axis' must be in [ , apply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.Constrain input and output types to singed/unsigned int8 tensors.data_scaledata_zero_pointreduced_zero_pointreduced_scaleQLinearReduceMeanCoefficient of leakage.X_scaleX_zero_pointY_scaleb_zero_pointConstrain input B data type to 8-bit integer tensor.Constrain input A, b_scale and output Y data type as float tensor.a_scalea_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A data type to 8-bit integer tensor.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointaxis must be in [-rank, rank-1]. input rank was Constrain input type to 8-bit integer tensor.reducedA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input 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).ReduceSumIntegerKeep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.MulIntegerb_scaleConstrain input A and its zero point types to 8 bit tensors.Constrain scale types to float tensors.Constrain input C to 32 bit integer tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Constrain output zero point types to 8 bit tensors.input_scaleweight_scaleAll inputs to Concat must have same rankaxis must be in [-rank, rank)Which axis to concat onT8TVSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Constrain scale types to any float tensor type.initial_cPY_hConstrain seq_lens to integer tensor.Y_cRequired attribute axis is missingConstrain weights types to 8 bit tensors.Number of neurons in the hidden layerSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Couple the input and forget gates if 1.Rsequence_lensinitial_hcublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.The output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)cublasLt order of output matrixDequantizeWithOrderorder_AcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_BcublasLt order of matrix Border_YcublasLt order of matrix Y and optional matrix Cscale_Amask_index_outConstrain input and output types to float32 tensors.order_inputcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_outputcublasLt order of output matrix.Fscale_inputConstrain to float typesQQuantizeWithOrderConstrain Scale to float32 typesgamma_scalebeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointbeta_zero_pointlayernorm_outinput_zero_pointConstrain input and output types to int8 tensors.weight_zero_pointword_embedding_quantgamma_quantposition_embedding_quantbeta_quantword_embedding_scaleposition_embedding_scalesegment_embedding_scalecublasLt order of matrix Y, must be same as order_X if specified together. Optional.Constrain scales to float32QOrderedGeluorder_global_weightscale_weightscale_biasscale_qkv_gemmscale_values_gemmQOrderedAttentionConstrain scales to float32 tensors.cublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.QOrderedLayerNormalizationcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.order_Xscale_XConstrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization tensor must be int8 tensors.quantization scale must be float tensors.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.scale_V_gemmscale_K_gemmQ_weightV_weightK_weightscale_Q_weightscale_V_weightscale_K_weightK_biasQ_biasscale_QKT_gemmV_biasscale_QKT_softmaxscale_Bscale_YConstrain bias and scales to float32scale_CQOrderedMatMulorder_weightcublasLt order of weight matrixcublasLt order of global biasscale_Q_gemmscale_global_weightscale_global_gemmscale_outputQOrderedLongformerAttentionBe compatible with float version.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.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.NhwcConvThe size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Whether to use ceil or floor (default) to compute the output shape.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Works on NHWC layout or not? Default not.input and zero_point pair is expected to have be same type.wweight and zero_point pair is expected to have same type.w_zero_pointw_scaleD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.for node: itr != node_args.end()Attempting to get index by a name which does not exist:D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::GetIndexFromNameonnxruntime::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 cannot be safely updated to in one of the subgraphs. ExplicitInputs: ImplicitInputs:Invalid input index for node . Index:Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputonnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Failed since multiple edges matched:onnxruntime::graph_utils::FindPathonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputInitializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputonnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)index >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.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::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::utils::SetNodeAttributeutils::HasName(attribute)D:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccAttributeProto must have a name.D:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::SaveRuntimeOptimizationRecordToOrtFormatruntime optimization record produced op idonnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatAttempting to load runtime optimization records for a previously loaded optimizer: ho!delimiter.empty()delimiter must not be emptyD:\a\_work\1\s\onnxruntime\core/common/string_utils.honnxruntime::utils::SplitStringinvalid string_view positiononnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringInvalid OpIdentifier string: Failed to parse since_version from D:\a\_work\1\s\onnxruntime\core/graph/op_identifier.honnxruntime::fbs::utils::LoadOpIdentifierOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.ccTENSORUNDEFINEDSTRINGGRAPHFLOATINTSTRINGSTENSORSFLOATSINTSSPARSE_TENSORSGRAPHSSPARSE_TENSORNull tensor in tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing string data for initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null string attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveInitializerOrtFormatSaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.9p,9p49p9p9p$9pH9pP9p89p@9ph9pp9pX9ponnxruntime::function_utils::CreateSchemaTAggregatedTypesinput_arg->Type() != nullptrNo opset registered for domain it != opset_imports.end() in function opset imports.inTinonnxruntime::function_utils::IOTypeConstraintHelper::::operator ()domain_version != -1 is not found in model local functions, domain: outToutD:\a\_work\1\s\onnxruntime\core\graph\function_utils.ccall_tensor_types_with_bfloatNumber of actual parameters cannot exceed number of formal parametersThe given function name: onnx_func_protoactuals.size() <= formals.size()onnxruntime::function_utils::Inliner::bindonnxruntime::FunctionImpl::FunctionImplD:\a\_work\1\s\onnxruntime\core\graph\function.cc is not found or is not constant initializer.initializer != nullptrword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsbeta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionssegment_embedding should have 2 dimensions, 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.Inputs 4 shall be 5 dimensionsInvalid bias shapeqkv_hidden_sizes should have 3 elementsConstrain input and output types.Can not get shape initializer data!Unsupported type:deltalimitUnsupported non-raw-data data type!tensor rank too smallinvalid scales dimensioninvalid scales valueD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccAWConstrain seq_lens to integral tensors.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.MWQWmemory_seq_lenscustom join thread function not setReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1onnxruntime::concurrency::CreateThreadPoolHelperD:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::math::NextPositionD:\a\_work\1\s\onnxruntime\core\util\math_cpu.ccdims[d_i] < d_max??rB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KAQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;bad dimensions?@@@), BIsSigned() is not supported on this deviceQuant GEMM format: AIsSigned(ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!    onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]NO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFSUCCESSFAILNO_SUCHFILEGENERAL ERRORcode != static_cast(common::OK)MODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAIL%hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %wsExceptionReturnHrLogHrFailFast[%hs] kernelbase.dllRaiseFailFastException Msg:[%ws] CallContext:[%hs] [%hs(%hs)] genericsystemDeleteFile() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: , allocation_granularity = , mapped_offset = mapped offset must be a multiple of the allocation granularity\/ "LoadLibrary failed with error FreeLibrary failed with error in library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathUNC\"" when trying to load "Received negative size from stat callfile_path == nullptronnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferopen file GetFileSizeEx Invalid fd was supplied: fail: unexpected endonnxruntime::`anonymous-namespace'::WindowsEnv::MapFileIntoMemoryopen file mapping , errcode = offset < 0length > buffer.size()SetFilePointerEx ReadFile onnxruntime. Specify the number of threads explicitly so the affinity is not set., error msg: , error code: doserrno: WindowThread:_beginthreadex failed with message: kernel32.dllSetThreadDescriptiononnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation - fail, errcode = , mask: SetThreadAffinityMask failed for thread: onnxruntime::`anonymous-namespace'::WindowsThread::ThreadMainFatal error: 0 count processors from GetSystemInfo error msg: %wsNegative thread index is not allowedindex >= 0onnxruntime::`anonymous-namespace'::WindowsThread::WindowsThreadcustom_create_thread_fn returned invalid handle.unmap view of file failed. error code: onnxruntime::`anonymous-namespace'::UnmapFileD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc/\onnxruntime::LoopDirD:\a\_work\1\s\onnxruntime\core/platform/path_lib.h\*Failed to find symbol onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameunknown erroronnxruntimeOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLoggerISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultFailed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootD:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::Path::Parse/\DistributionDistributionEnqueueRunWait", "block_size": ["thread_id": "], "core": ": ": {Nested parallelism not supported!current_parallel_section.has_value()onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectionWaitRevokeUnknownEvent"core": "num_run": }, "sub_threads": {", "thread_pool_name": "{"main_thread": {enabled_onnxruntime::concurrency::ThreadPoolProfiler::StopD:\a\_work\1\s\onnxruntime\core\common\threadpool.cc}}onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartpoints_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::ResetLogStart must pair with LogEnd!points_.empty()unnamed_thread_poolProfiler not started yetn <= num_threads_ + 1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection!ps_n >= 0onnxruntime::concurrency::ThreadPool::ParallelForMore work items than threadsonnxruntime::ToUTF8StringD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideStringlength overflow"tid" :"dur" :"ts" :"ph" : "X",[ ",{"cat" : ""pid" :} }, ] "name" :""args" : {" : " : "onnxruntime::profiling::Profiler::StartD:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptrMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfilingonnxruntime::profiling::Profiler::InitializeNodeKernelApiSessionXbp@bpHbpPbpunexpected failureillegal input path:api-ms-win-eventing-provider-l1-1-0.dlladvapi32.dllEventSetInformation] , : [ is not a valid day is not a valid dateampm is not a valid yearGetSystemTimePreciseAsFileTimekernel32.dlltensor_typemap_typesequence_type is missing type info.onnxruntime::fbs::utils::SaveValueInfoOrtFormatdim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatWe do not support type [] for nowonnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Null entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatonnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.ccType: is not supported currentlyNull type info for . Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.opset import domain is null. Invalid ORT format model.Model must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadValueInfoOrtFormatopset id is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatmcpdpcp, has unsupported type: , max=Operator ') has input size not in range [min= has inconsistent type typestr: ' has been deprecated since version has unsupported type !(it.GetName().empty()): failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == iONNX Schema ' is expected to have field 'strings'' is expected to have field 'ints'' is expected to have field 'graphs'' is expected to have field 'tensors'' is expected to have field 'g'' is expected to have field 'sparse_tensor'' is expected to have field 'floats'' is expected to have field 'type_proto'Duplicate type constraint nameExtra unparsed input unexpected.Error parsing function body: has unknown expected type' is expected to have field 'type_protos'Required attribute '' is missing.) than declared () in op definition. is marked single but has an empty string in the graph) has more inputs () has output size not in allowed input sizes.has output size not in allowed output sizes.Unrecognized attribute: for operator ' is expected to have field 't'Mismatched attribute type in ') has more outputs ()'s input ' appeared multiple times.)'s output optionalInvalid tensor data type opaquemapmap(optional(Unsuported type proto value case.sparse_tensor(complex64DataTypeUtils::FromDataTypeString - Received invalid data type string complex128Invalid data type tensor(Input type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setValue(s) to change to.Label encoder has only one output.Label encoder has only one input.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.A list of floats.A list of ints.Only one of values_*'s can be set in label encoder.The input type is a tensor of any shape.An integer vocabulary array.
One and only one of the vocabularies must be defined.map(string, double)map(string, float)A string vocabulary array.
One and only one of the vocabularies must be defined.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.A value that needs replacing.Value(s) to change toThe size of each input in the input listThe input type must be a tensor of a numeric type.The output is a tensor of strings or integers. Its shape will be the same as the input shape.The input must be a tensor of strings or integers, either [N,C] or [C].The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceIf the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(int64, double)map(string, int64)An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.The output is a 1-D tensor of string, float, or integer.map(int64, float)Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.map(int64, string)The input must be an integer map to either string or float.The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsClass labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.The number of support vectors.Support vector coefficients.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.First set of probability coefficients.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The input must be a tensor of a numeric type, either [C] or [N,C].Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.Weights of the intercepts, if used.Weights of the model(s).Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'One of 'MAX,' 'L1,' 'L2'The total number of regression targets, 1 if not defined.Input's shape should be 1D or 2DA string.A list of strings. One and only one of 'value_*'s should be set.A float.An integer.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]A collection of weights of the model(s).The output will be a tensor of strings or integers.seq(map(int64, float))seq(map(string, float))The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The output will be a sequence of string or integer maps to float.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Child node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)The weight for each targetThe index of the target that each weight is forDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Child node if expression is falseThe node id of each weightThe id of the tree that each node is in.Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.Thresholds to do the splitting on for each node.Feature id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.The id of the tree that this node is in.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The index of the class list that each weight is for.node id that this weight is for.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Popularity of each node, used for performance and may be omitted.Child node if expression is false.Child node if expression is true.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.Constrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentWrong op_type name for running propagation: D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.cc broadcasting: () vs (Invalid rank for K input must be a one-dimensional tensor of size 1.Invalid value for attribute axisKValuesAxis has less than the requested k elements.K input must be of type int64.X_ReduceMax = ReduceMax (input)sumConstrain input and output types to numeric tensors. X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) slopeConstrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.List of tensors for { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } data_0Value of alpha.Value of beta.Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). 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. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.Constrain output Y data type as 32-bit integer tensor.Constrain input and output types to floating-point tensors.const_zeroaxis tensor can be int32 or int64 onlyinput and zero_point pair is expected to have same type.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input and output types to all tensors.Whether to return the elements in sorted order.Constrain index tensor to int64IndicesWhether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).InputsType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.Rank of input does not match the equation indices.Einsum expression string.output = NegativeLogLikelihoodLoss (X_Log, labels)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)Shape3Dlog_prob = Identity (X_Log) X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) targetInput and target dimension value mismatch.Target rank must be 1 less than the input rank.Weight rank must be 1.NegativeLogLikelihoodLossConstrain target to integer typesEllipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.lossConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)loss_NCdd = Neg (input_gather_element_transform)const_one_floatsqueeze_mask = Squeeze (mask, axes)const_zero_casted = Cast (const_zero_float)const_zero_floatinput_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) weight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)const_one_casted = Cast (const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)loss_Ndd = Squeeze (loss_N1dd, axes)loss = Squeeze (loss_N1dd, axes)loss = ReduceSum (loss_Ndd)loss = ReduceMean (loss_Ndd)expanded_target = Unsqueeze (target, axes)const_one input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) ignore_indexconst_ignore_index loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) input_gather_element = GatherElements (input, transform_targets) const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) loss_unweighted = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)loss_Ndd = Mul (loss_unweighted, weight_gather)loss = Mul (loss_unweighted, weight_gather)If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!window input must have rank = 1.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.Constrain to any numerical types.Constrain to float tensorsframe_length input must be scalar.frame_lengthConstrain signal and output to float tensors.signalframe_stepThe data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. num_mel_bins { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } dft_length input must be scalar.num_mel_bins input must be scalar.upper_edge_hertzConstrain to integer tensors.sample_ratelower_edge_hertzThe data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. size input must be greater than 0.If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. dft_lengthsize input must be a scalar.Constrain scalar length types to int64_t.Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Blackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } HannA {name} window with length: size. The output has the shape: [size].Constrain output types to numeric tensors.Constrain the input size to int64_t.SoftmaxCrossEntropyLosslog_probis_onesided and inverse attributes cannot be enabled at the same timelabelsWhether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index.axis attribute value is invalid for a tensor of rank If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.dft_length input must be a scalar.Input Sequence and Tensor are expected to have the same elem type. Sequence=input_sequenceConstrain input types to any tensor type. Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.Constrain 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.positionConstrain to any tensor type.Constrain output types to any tensor type.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.cc(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.Element type of inputs are expected to be the same.output_sequenceSequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.out_sequenceConstrain to any tensor or sequence type.Constrain input types to any sequence type.The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function._seqemptyadditional_inputsSequenceMapInput 0 expected but not providedExpected 1 or more outputs. expected but not providedExpected a sequence type for input 0Graph attribute inferencing returned type information for outputs. Expected Expected 1 or more inputs.Invalid ``body`` argument. Expected a graphSequenceMap__in_cond_seqlen_itercountSequenceMap_loop_body_cond_out_cond_innew_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Constrain output types to all tensor types.Constrain split size to integral tensor.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1]. expected to have type infoGraph attribute inferencer for "body" not available expected to be a sequence typeInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. concat_resultConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInvalid value of attribute 'axis'. Rank= sum of split values=Input 'split' can not be empty. Value=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size='output_shape' must have same number of elements as the shape of input tensor X.'output_shape' must be rank 1 tensor.Attribute strides has incorrect size.Attribute pads has incorrect size.Attribute kernel_shape must be specified.Attribute kernel_shape has incorrect size.RoIs tensor must have 2 dimensionsInput tensor must have at least 2 dimensionsp value of the Lp norm used to pool over the input data.Second input tensor has wrong dimensionAttribute pads has incorrect sizeConstrain input and output types to float and 8 bit tensors.Attribute strides has incorrect sizeAttribute dilations has incorrect sizeAttribute kernel_shape must be specifiedAttribute kernel_shape has incorrect sizeDilation 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.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.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. running_meaninput_meaninput_varConstrain scale and bias types to float tensors.running_varConstrain mean and variance types to float tensors.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe pads attribute cannot be used simultaneously with auto_pad attributeIf set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.This number of op outputs should be 3 when Training_mode = True, but it is not. 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. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).This number of op outputs should be 1 when Training_mode = False, but it is not.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input 'w'.number of groups input channels and output channels are divided into. default is 1.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.Constrain filter type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain input x and its zero point data type to 8-bit integer tensor.Attribute pooled_shape must be specifiedAttribute pooled_shape has incorrect lengthMultiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).FloatEpsilonNormalizedT = Cast (Normalized)Epsilon = Cast (FloatEpsilon)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. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.Constrain input types and output Y type to float tensors.The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Scaled = Mul (NormalizedT, Scale2D)Type of Mean and InvStdDev. This also specifies stage one's computation precision.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.list of 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.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.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.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"List of stop words. If not set, no word would be removed from X.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseThe weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Input shape must have either [C] or [1,C] dimensions where C > 0Scaling parameter.The number of channels to sum overThe exponent.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). 1-D tensor of floatsInput is ether string UTF-8 or int32/int64Constrain input and output types to float tensors.Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The axis on which to apply normalization, -1 mean last axis.) for attribute 'axis'Invalid value(The lambd value for the Shrink formulation. Default is 0.5.Constrain input to only numeric types.The bias value added to output. Default is 0. { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to boolean tensors.Constrain input types to all numeric tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input and output types to integer tensors.Constrain input to boolean tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrain output to boolean tensor.cond was If node has Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Only boolAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types axis value Number of scan input axes specified () is not equal to number of scan inputs (Scan 'body' subgraph outputs should all be tensors but output was not but subgraphs produce then_branch and else_branch produce different number of outputs. Number of scan output axes specified () is not equal to number of scan outputs (Scan input was not a tensor.optional(tensor(int32))optional(tensor(int16))optional(tensor(float16))optional(tensor(int64))optional(tensor(uint32))optional(tensor(uint16))optional(tensor(int8))optional(tensor(uint64))optional(tensor(complex128))optional(tensor(complex64))optional(tensor(bfloat16))optional(seq(tensor(bfloat16)))optional(tensor(double))optional(tensor(float))optional(tensor(bool))optional(tensor(string))optional(seq(tensor(int16)))optional(seq(tensor(int8)))optional(seq(tensor(int64)))optional(seq(tensor(int32)))optional(seq(tensor(uint16)))optional(seq(tensor(uint8)))optional(seq(tensor(uint64)))optional(seq(tensor(uint32)))optional(seq(tensor(complex64)))optional(seq(tensor(bool)))optional(tensor(uint8))optional(seq(tensor(complex128)))optional(seq(tensor(float)))optional(seq(tensor(float16)))optional(seq(tensor(string)))optional(seq(tensor(double)))An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).All Tensor typestensor of int64, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of bool, which should be a scalar.v_initialv_final_and_scan_outputsAn attribute specifying the number of scan_inputs M. The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.initial_state_and_scan_inputsfinal_state_and_scan_outputsaxis must be in [-rank, rank-1]. expected to have tensor or sparse tensor typeInput: indices must be in [-rank, rank-1]. and Output does not match type of output: type: Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableMismatch between number of splits () and outputs (Slice op must have either three, four or five inputs.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInput axes has incorrect lengthIncorrect or missing input value for starts and endsWhich 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 Slice(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.Input tensor can be of arbitrary type.(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.reshapedAll inputs to Concat must have same rank. Input has rank Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Constrain output to int64 tensor.Constrain output to int64 tensor, which should be a scalar though.output = Cast (input)target_typeConstrain input types. Casting from complex is not supported.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output types. Casting to complex is not supported.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Invalid dimension value: (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.Dimension could not be inferred: incompatible shapesInvalid position of 0.Target 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 TensorProtoDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input must be 1D tensor of type int64Blocks of [blocksize, blocksize] are moved.Constrain repeat's type to int64 tensors.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.repeatsList 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).squeezedexpandedInput tensor must be 4-dimensionalBlocksize must be positivevalues in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatesList 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).Input and output types can be of any tensor type.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.updatesWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]data tensor must have rank >= 1stepsInput axes has invalid dataInput steps has incorrect lengthInput rank for starts and ends should be the same: (transposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).}, input shape = {Invalid attribute perm {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Attribute perm for Transpose has repeated value: Constrain input and output types to all tensor types (including bfloat). Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. (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. **History** - Version 16 adds bfloat16 to the types allowed (for the second and third parameter). (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.Constrain to all tensor types.'sequence_lens' must have rank of 1'input' must have rank >= 2(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.Input 'values' must be rank 1 tensor.Input 'depth' must have exactly one element.Constrain output types to boolean tensors.Constrain input types to float tensors.(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.depthConstrain input and output types to all tensor, sequence, and optional types.Constrain roi type to float or double.gridSupport padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.conditionConstrain to boolean tensors.'axis' must be in [-rank(indices), rank(indices)-1]Indices tensor must have rank >= 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). 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).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".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 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.roisizesFour 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".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.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.constant_valueSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorinverse_indicesBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.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.ccInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.selected_indicesmax_output_boxes_per_classdefault 1; Pooled output Y's width.default 1; Pooled output Y's height.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. Optional is expected to have either an input or the type attribute set.Type of the element in the optional outputOptional is expected to have an output.Input type is null. Type information is expected for the input.Attribute 'type' should be a TypeProto and it should specify a type.OptionalHasElement is expected to have 1 output.OptionalHasElement is expected to have 1 input.Constrain output type to all optional tensor or optional sequence types.Constrain input type to all tensor and sequence types.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\optional\defs.ccInput must be an optional-type value containing an element with type information.Input type is null. Input must have Type information.Constrain output type to all tensor or sequence types.Constrain output to a boolean tensor.Constrain input type to optional tensor and optional sequence types.OptionalGetElement must have an input element.The value for the elements of the output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the sole element for the scalar, int64, output tensor.The value for the elements of the output tensor in sparse format.Attribute 'value_floats' expect a list of floats.Attribute 'value_float' expect a float.Attribute 'value_strings' expect a list of strings.Attribute 'value_string' expect a string.The values for the elements for the 1D, UTF-8 string, output tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.value_intsparse_valueOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_stringsAttribute 'value_ints' expect a list of integers.Attribute 'value_int' expect an integer.value_floatvalue_intsvalue_stringvalue_floatsAttribute expected to have a one-dim sparse tensorAttribute expected to have a one-dim tensorBernoulliThe data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.Constrain output types to all numeric tensors and bool 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 int32.Constrain output types to integral tensors.X_random = RandomUniformLike (input)output = Cast (X_greater)X_greater = Greater (X_random, input) { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Constrain input types to common numeric type tensors.The standard deviation of the normal distribution.The mean of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Output type must be int32 or int64Number of times to sample.Input tensor must have rank 2(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.Input tensor must be 2-dimensional(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Constrain input types.Constrain output types. Strings and complex are not supported.Upper boundary of the output values.Lower boundary of the output values.(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'.Constrain input types. Strings and complex are not supported.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)rank must be greater than axisWhich axis to concat on. Default value is 1.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Constrain input and output to only numeric types.paddingsThree modes: constant(default), reflect, edgeList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Which axis to split onlength of each outputoutputs...'step' cannot be 0 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).This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).length of each output. Values should be >= 0.Invalid Target shape product of 0Target shape may not have multiple -1 dimensionsInvalid position of 0D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.ccAttribute value for pads is requiredAttribute pads has incorrect lengthWhich axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Constrain input and output types to all tensor and sequence types.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Ending indices (exclusive) of corresponding axis in axes`'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.) is not equal to the existing rank value (Ranks inferred (Constrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAttribute 'scales' is required.consumed_inputsNew shapelegacy optimization attribute.One float, indicates the value to be filled, default is 0The scale along height dimension. It takes value greater than or equal to 1.height_scaleThe scale along width dimension. It takes value greater than or equal to 1.width_scaleConstrain tiles and axis's type to int64 tensors.tilesPadding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Dilation value along each spatial axis of filter.Stride along each spatial axis.X_varianceProcessed_STDThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.Stride along each axis.VarianceSTDX_squaredE_XsquaredX_RMEX_squaredD:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccExponentIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain output mask types to boolean tensors. 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. saved_var Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. saved_meanThe ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropoutConstrain mean and variance types to float tensors. It allows all float type for U.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testIf true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.varThe 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].One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size]. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) First input tensor must have rank 3 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\defs.cc Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Mismatched tensor element type for output then=Mismatched type for output Int64 tensorAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. else=An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float or int32 tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain input and output types to high-precision and 8 bit numeric tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.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).axes as an input and attribute cannot be specified at the same time.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\old.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).loss_N1ddloss_Nddinput_gather_elementloss_NCddexpanded_target'shape' input must be 1D tensorMinimum value, under which element is replaced by minMaximum value, above which element is replaced by maxNumber of top elements to retrieveDimension on which to do the sort.Invalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Value of alpha default to 0.2Value of beta default to 0.5Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.broadcastIf set, defines the broadcast dimensions. See doc for details. If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Pass 1 to enable broadcastingX_LogDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeX_LogSM_NCDX_shapeCoefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.transform_targetsconst_zero_castedconst_zero_target_typedexpanded_target_int64loss_sumweight_gather_sumweight_gatherloss_unweightedX_NDCX_LogSMweight_gather_temp_1X_NCDconst_one_castedweight_gather_tempinput_gather_element_transformsqueeze_maskConstrain input to integral tensors.Constrain input to float tensors.If set, defines the broadcast dimensions.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccEnable broadcastingD:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\old.ccOne of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The type of the output tensor is integer.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\old.ccThe sequence output for the hidden is optional if 0. Default 0.fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\old.cctarget map type missing key type.Mismatched map tensor key type:target optional type missing element type.source map type missing key type.target sequence type missing element type.source optional type missing element type.Mismatched sparse tensor element type:source sequence type missing element type.Output was expected to have tensor type. Got Input was expected to have sequence type. Got does not match existing output type of Input element type of Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got source map type missing value type.target map type missing value type.Element type of optional input expected to have map typeElement type of sequence input expected to have optional typeElement type of input expected to have sequence type expected to have tensor or sparse tensor type. Got: unknownMismatched type:Mismatched tensor element type: target= source= expected to have type but instead is nullMismatch between number of source and target dimensions. Source=Key type of map input Value type of map input Input type was nullInput was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensorKey type of map input was unknownValue type of map input was unknownElement type of optional input was unknownInput was expected to have map type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Data size mismatch. Tensor: does not match the actual size expected size load external data into raw data for tensor: Cannot parse data from external tensors. Please . Expected:ParseData type mismatch for tensor: The type of tensor: Actual: is undefined so it cannot be parsed.domain ==> Context: graph' is required but missing.elem_typenameField 'value_info' of 'utf8str_to_wstring: string is too long for converting to wstring.' is required to be non-empty.] 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 (] not in sorted order.] out of range [0, ) index value at position [) must have rank 1 or 2.) has no index values.) dimensions are not positive.) must have INT64 type.Sparse tensor values () must have a dense-rank > 0sparse_tensor_proto) must have rank 1.values of data_type 'Unrecognized data_type (tensor name: ) has Sparse tensor indices ( values, but NNZ is ) should be stored in ) is stored externally but doesn't have a location.) should be file inside the , but it doesn't exist or is not accessible.' points outside the directory, but the ') should be a relative path, but it is an absolute path: Location of external TensorProto ( tensor name: ' instead of '' should be stored in field ') should not be stored in raw_data fieldSTRING data (tensor name: TensorProto (tensor name: ) should contain one and only one value field.TensorProto ( tensor name: ) is 0-element but contains data!setting data_type field (tensor name: float_datadata_type) to UNDEFINED is not allowed): Unrecognized type value case (value_info name: key_typevalue_type) is stored externally and should not have data field.Data of TensorProto ( tensor name: double_datauint64_dataint64_dataraw_dataint32_datastring_dataConstantFill in initializer but not in graph inputSparse tensor initializers must have a non-empty nameTensor initializers must have a non-empty name initializer name is not uniqueGraph must be in single static assignment (SSA) form, however 'initBad node spec for node. Name: ' has been used as graph input names multiple times.Nodes in a graph must be topologically sorted, however input '' has been used as output names multiple times.name: ' of node: sparse initializer name is not unique across initializers and sparse_initializers is not output of any previous nodes.) has zero input and zero output., type: ) should refer to attribute in parent node.op_type) should not contain more than one value field.Attribute (name: attrtype field and data field mismatch in attribute Op registered for OpType: No Op registered for is deprecated in domain_version of No opset import for domain ' with domain_version of NodeProto (name: Warning: Checker does not support models with experimental ops: stol argument out of rangeinvalid stol argument[ParseError at position Error context: Unhandled type: %dError parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).Unexpected type.Unexpected type in sparse-tensor element type.Expecting primitive type as map key type.Identifier expected but not found.Unexpected attribute type.doc_stringmodel_versionproducer_versionproducer_nameopset_importir_versiontype_protossparse_tensorsString value expected, but not found.Unexpected literal type.Integer value expected, but not found.Value expected but not found.Expected character (line: column: metadata_propsgraphstensorsstringsfloatstype_protointData for input already exists. does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.(op_type: inputs but were provided type is missing., node name: Cannot find missing input: in initializers. Graph initializer names must appear after the actual inputs: were provided.The number of graph input cannot be smaller than the number of node inputCannot use the same name as both a subgraph initializer and subgraph input: Graph has type case mismatch. existing= inferred=NOT_SETsparse_tensor_typeopaque_typeoptional_typeShape inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Cannot infer type and shape for node name . No opset import for domain optype type case unsupported for symbolic shape inference. inferred=type case unsupported. existing=key type mismatch from MapProto. existing=Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (unk__Inferred shape and existing shape differ in dimension : () is not equal to the existing dim value (Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'Dimension value inferred (onnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TensorProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.FunctionProtoonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Optionalonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.StringStringEntryProtoonnx.ModelProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.AttributeProtoFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   text size: Invalid RE2: Error reverse compiling 'pattern too large - compile failedError compiling 'Error parsing '': pattern length program size list count bytemap range Unexpected re_anchor value: RE2: invalid startpos, endpos pair. [startpos: endpos: D:\a\_work\1\s\cmake\external\re2\re2\re2.ccUnknown encoding SearchNFA inconsistencySearchBitState inconsistencySearchOnePass inconsistencySearchDFA inconsistencyDFA out of memory: invalid perl operatorbad repetition operatorinvalid repetition sizeno argument for repetition operatortrailing \unexpected )missing )missing ]NumCapturesWalker::ShortVisit calledinvalid named capture groupinvalid UTF-8Regexp not destroyed.invalid character class rangeinvalid character classinvalid escape sequenceunexpected errorno errorUnexpected op in Regexp::Equal: Bad reference count D:\a\_work\1\s\cmake\external\re2\re2\regexp.ccWalk NULLD:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hStack not empty.xq`qHq0qqqqpq`q8q qqqqqCompiler::Copy called!D:\a\_work\1\s\cmake\external\re2\re2\compile.ccshould never happenMissing case in Compiler: No ranges in char classAnyBad hex digit RE2: unexpected op: unknown round: Concat of Bad call to ParseState::ParsePerlFlagsRepetitionWalker::ShortVisit calledD:\a\_work\1\s\cmake\external\re2\re2\parse.ccAddFoldedRange recurses too much.Unexpected opcode in short circuit: Bad args: nsubmatch=context does not contain text in AddToThreadqUnhandled in stepD:\a\_work\1\s\cmake\external\re2\re2\nfa.ccunhandled Failed to analyze start state.StateSaver failed to restore state.Unexpected special state in RunStateOnByte|NULL state in RunStateOnByteDeadState in RunStateOnByteD:\a\_work\1\s\cmake\external\re2\re2\dfa.ccunhandled opcode: RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheUnexpected opcode: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccGrowStack() failed: njob_ = job_.size() = 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: PqTqLqTqqqqqq`qq`qqqqqqqqq@qq0qq qqqqqqqqqqqqqqqqqXqqXqqpqqpqqqqqpqq`qqPqq@qq0qxq qxqqqqq[:^punct:][:punct:][:^print:][:print:][:^lower:][:lower:][:^graph:][:graph:][:^xdigit:][:xdigit:][:^word:][:word:][:^upper:][:upper:][:^space:][:space:][:^alpha:][:alpha:][:^alnum:][:alnum:]\W\w\S\s[:^digit:][:digit:][:^cntrl:][:cntrl:][:^blank:][:blank:][:^ascii:][:ascii:]\D\d09az09AZ__az ~09AZ__az 09AFafAZAZaz09 09AZaz!/:@[`{~ !~"r`Or"rqrh"r0qr"r ar6r#"rxrr"rr"rX{r"rr^r"rbr"rgr"rdr"r0r!rhbr!rؑr!r\r!rhdr!rddroGr,r !rx6r!rrP"rBrH"rHrD"rmr r<"rrr4"rH{r("rPr"rYr"r`drAr8!r4r[grR0!r@rr,!r8gr !rr!rxqr!r(Hr!rHOr rXbr!rpr!rXdr!rpwrp!r(Arh!r`r`!rhwrP!rcr @!r dr H%r6rKr@%rx(r8%rr,%r`Dr!0Br %r0yr%rar%rIr$r8cr `gr%rAr%rGr%r(gr%rar%rxar %r`2r3rh%rGrX%r xrpKr$rIr$rP2rp$rjrh$rrr`$rKr P$r`Hrrr@$rr0$rxr$rHdr$rPIr$rPrr$rDr$r`,r|pr$r [r $rOr $r`r#rqr#rbr#r|r#r\grKr#r|rc0^r"#r r74r#rrr"Pr#r(r ,$r7rY2r%$$rhcr$rhrroPLrjrg$r3r$rar#rpIr#rr#rxArH#r8Lr8#rr0#ryrmrB,#rhyr #r(^r#r(Lr#rr"r^r#rpcr#rgr|#rr@Vrut#rarh#rrx2r`#rx4rX#r]rP#rjr'r{rCPBrB'rcr'r(rr'rbr%Hr'rdr'r Br'r2r'r3rrp(rpxr*r*h(rqrP(rrrD(r8qr8(r,gr((rqr(rLr(rp4r`'rbrP'rPOr@'r Hr('rrr'r(qr'r8Ar'rh2r'rjrperEr5'r@jr'rrr'r7r'rX4r'rp*rpqr'rZrH|'rAr p'rL[r&rcr&r]r &rr(r4&rJrK&rHHr|&rhjrt&r`KrmP[rrQh&rpAr&rxr&r06r@br&rar&rAr&r0br&r(r&rIr x{r&rwrxcr &rPpr54r &r@_rp nrF&rar%rEr%rYr%r0gr%r(r%r(r`&r`rrP&rXwrD&r]r8&r`{r(&r6r &r`Ir&rErBr&rHr@Er@qr 8Erar0Er7r(ErxbrEryrErGrErpjrDr rrErdrxErpdrpErdrlEr@Oro@KrXErxrPEr]rLErdrHEr@grDevanagariDeseretCyrillicCypriotCuneiformCsCopticCommonGeorgianEthiopicElymaicElbasanEgyptian_HieroglyphsDuployanDograDives_AkuruCarianCanadian_AboriginalBuhidBugineseBrailleBrahmiBopomofoBhaiksukiCoChorasmianCherokeeChamChakmaCfCcCaucasian_AlbanianAnatolian_HieroglyphsAhomAdlamBengaliBatakBassa_VahBamumBalineseAvestanArmenianArabicMeroitic_CursiveMende_KikakuiMeetei_MayekMedefaidrinMeMcMasaram_GondiMarchenMyanmarMultaniMroMongolianModiMnMiaoMeroitic_HieroglyphsLtLoLmLlLisuLinear_BLinear_ALimbuManichaeanMandaicMalayalamMakasarMahajaniLydianLycianLuKharoshthiKayah_LiKatakanaKannadaKaithiJavaneseInscriptional_ParthianInscriptional_PahlaviLepchaLatinLaoLKhudawadiKhojkiKhmerKhitan_Small_ScriptHanGurmukhiGunjala_GondiGujaratiGreekGranthaGothicGlagoliticInheritedImperial_AramaicHiraganaHebrewHatranHanunooHanifi_RohingyaHangulSyriacSyloti_NagriSundaneseSoyomboSora_SompengSogdianSoSmTangutTamilTakriTai_VietTai_ThamTai_LeTagbanwaTagalogSamaritanRunicRejangPsalter_PahlaviPsPoPiPhoenicianSkSinhalaSignWritingSiddhamShavianSharadaScSaurashtraOsmanyaOsageOriyaOld_TurkicOld_South_ArabianOld_SogdianOld_PersianOld_PermicPhags_PaPfPePdPcPau_Cin_HauPalmyrenePahawh_HmongNlNkoNewaNew_Tai_LueNdNandinagariNabataeanNOld_North_ArabianOld_ItalicOld_HungarianOl_ChikiOghamNyiakeng_Puachue_HmongNushuNo0J JMO`jooWW  ? ? 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B:^ :^! :^A:^^AAB^:^!l  ~ 4(^i F<! 8"* $J&P$(h*,..02546.8X:J<>r@B.DF HJ.LZNYB(j42$.td4Pd[t([t[t0g'Dp`0Pt$g[t([t[t  :^:@A: t T.d3420 pd,\t(s5\tPX0(4E@p`Pt$g`\t(i\t\t  :^:@A: mbl.d3420 pd\t(s\tVX0* 1 p`0Pt$g\tr(\t]t ]Bж A:ж :P.8j* 1 p`0Pt$gL]tr(\tU]t.8j5 $h? p`0Pt$g]t(]t]t =:PjPq-jjtb$d)4'$ pt$g]t()s]t$ $p`0t$g^thr^tz*0 4G< p`Pt$gL^t(U^t^t a:^2^^:^a:^:^2^H (.E (]Xr  tCdA4@>Pd^t(5s^tJ)^, "lx, " 4 2p`P6 %4% p`Pt$g4_t (=_t`tz ^:ara a :^:ara a :^:ara a :^:araPg2 j S SBpaS:^ArA:tTA^5BtT%%^AABtT*^Baza a B^:ara a :^:ara g2 SU SB^z:tT0^ E BtTAM5^  BtTA6^ % BtT<^  BtTG^BtTAx:Y iE "F&(*v,P.(,(!68N:(8(^0d2(4(b>j@~BjFjH~JZNjP`R:VyXZV\(ZVdfTh(fVjlTn(l Vj@~BbFjH~JTNjPJR^TFXZH\j^~`bdjf~hbljn~pTtjv~xT|j~~Jj~bj~bj~Tj`:Hj~hj~TjTj~zj~Tj`:ؾHj~bj~TdH(6x(`: N( N( zN(~N!(%x)(-p1>%5H9(5n%`p%H=(AEhI(M6QU(Y6]xa(e`i:qQ}H(}jqH(qHu(yvq~H(}' ' p`0Pt$ggt"(gtgt pA:^2b`h  : 9- E p`0Pt$ght(!ht(< qFHTJ(Hu @vB(D 4v6(8d4"(- m p`0Pt$g`h.h>hꐶ1QE J p`0t$g@ot(IotVot P `4@,:&R&V p`P0t$got(otot P @ |<T 4 2 p ` 4ZP p`Pdot(ot;pt 76d\P8AP8A>` h.p h*^:\\\^!B@A:|h u u)& d@  0 4d\ p`Pt$gpt(ptpt a :^r<^a>`xg h tTY v^( " ev-  p`0Pt$gqtr( qtlqt = :P rP Т :Т ! :A : :^ : :^a:^:^:^a:^ 6m= LZ q 1VV2- x2 !dТ Xr!Т Xr 4H@ p`Pdqt(rt9rt@ h.p h. h. h. h.0 h.` h. h. h. h. h E-  p`0Pt$gdrt"(mrtrt =A:PrP^A:^2^A:^:^:^A:^Y^03aAB^~d.d:1yxD 52x*d>  4 #h-4c\ p`Pt$gDst(Mstost P :P :^2@p29 D td4Pd 4 r p% ( p`0Pt$gst2( Mtst2 $t$d$4$Pt$g tt(tttt -] 2 !d!4! p Pt$gPttj(Ytttt h. hZ:^:@:A ^A5B^r^A:@a: $:n> e5,T42&T 4 t5420 Pdtt(tt ut ^`2@2bQ R., d241* p Pt$g@utB(NtIutt x$$d4 pd|ut(OtutX2* 49. p`Pt$gutb(utut ^p2^Ar^P NR  p`0Pdvt(vtiwtb ^2px!:^ r :^a:pxa:^ r :^:px:^r :^p2px2^Aj A:^:px:^r :^a:px:^r :^:px :^r :^a:px :^A ^aBpxa:^:px:^p2px2^:px:^a:px:^:px :^a:px >h.`h.ph~PhH  t"$(& (t*,(.0t24(6z8t:<(>z\x^`(b:Z0 44* p`Pt$gwtB (wt}xt@"^Z<^64g0gtTA ^D^64g`g tTP BP : a:^j aA!: a:^r aB x"x$4&"(*d"-2!42468z6:<>x<@b<vx 4"d @< 6- K p`0Pt$gxtB(xt yt =:PrP)=bT**h%d%T%4%!pdLyt(Uythyt \@4^j @FHe!  td 9 r! 9 r!t r!4 yt! yt! rWWt Md :T r0!  r yt! r yte e \T 4 2 p`8 'h14ld p`Pt$glzt (uztzt ^B^zp : :^:a a ^:  %^B^2^Au^^2e  ]Au4AA]^ , d4 p Pt$gH{t(Q{tX{t ^i  ! d e lts!e  `{t!e  `{t!e  `{t! e lts! d e lts T<4:6 p `d{t(|t|t ^:@2AX6 p`0t$g@|t:(I|tP|t @p0 xh< p`0Pt$g|t(|t|t a:^2@`2n5.- s p`0Pt$g|t(|t2}t ^:@:A aA :^Ar^:^2^%^AB^2^( 0E ()Y4Ad42 6%x^!h_ p`0Pt$g}t(}t~t" =:3a:7A:P!rPhh^!:^:^:^!:^ %^^%^!^%^!>A (   a T"  !4055%s!055%s!d6V6Ps!TV6u6~t!u6z6~t!u6z6~t!V6u6~t!6V6Ps!d0;X;Ps!0;X;Ps0 4H@ p`Pt$g@t (Itt@ ^!:@:^:@a:1QuP A%BP ": :^!:@: aFdj^p2^!Ґ^!: aA>db^2^ʐ^:e^z.:0.2268,:8<2@86@8x.,l 4 2l"4$2. td4P:)x}%h~! p ` 0Pt$gxt(tt =::PrP!:^raa:^:^ :^:^:^P A2m=iz!E  m .Ptt.  ]| :)xd%he! p ` 0Pt$gPt2(Ytt =:A:PrPA:^Ar!!:^2^:^:^A:^ P 2=-Q!v a  e4ff.I % / !4! p ` Pt$g$t(-t4t Q!tЅ]r!tЅ]r!Ѕ]r5 $h5 p`0Pt$gt (ttZ = :a:PrP^!:r a B^!:r}0h2 j  Bp!:^!:r a :^!:rh2 j a aE^a B z  h2 j  e^B`dU `dBz:^:^:^:^A:^AX1v UT uzq$,n.V0^2R6% I8Z:n^@NDy AF(JLNiPERVTVV,XV-NNZNumZV+ xhT4 p `#g@!p`0Pt$gt(tt ^`2^j^2p  ^b|p`0t$gtj(tt p @2 !dE!4D!< p Pt$g$t(-tmt ^2^r^:^:^A:^:^A:^A^A}p 4 p`P0 4 p`Pt$gȅt(хtmt6 (0:P :r: aA:^:^AP Baza:^ :@:A a:^!:^^:^^B^!:^A^a> Pz PX&(z*&,.0i2%40P60x, Z"4$* 46, p`Pt$gtR(t t ^2^r^:^j^=&M R!8 'tY'dX'4W'PPt$gPtr(Ytt a:^A:^^:^A:^:^2^^!z=E 4&x"h48. p `Pt$gtJ(t%t :^r^2^]2Ґ^^ & 2 & j8* 4  p`Pt$gpt(ytt p T4 p `dt(tt ^^x8 'hq4 p`Pt$gt(tt4 (08@JP :P :P :P : r : a :^:^ aA:^:^^2^ %^ B^A:^^2^^^* z"$&z($q*,.02.V4.$P$! !d=!4:!4 p Pdt(tt ^2^:^:^2^%^bQ  0 4 p`Pt$gPt2(Ytt  B 8@JP  P ʐ^2^a e^a B^:^^A^* $] 99*Pq^^@  4 rp td4P$ $h4:0 p`Pd,t(5t_t ^2^A:^:^2^%^A1  %- a p`0Pt$gt(tt d2 :=:PjP9)`t 9!t dT  r!  r!4@  %s!@  %st d 4 R!  T 4 !?t!t!U@t!!U@t!t!U@t!!?t!T 4 !?t  r`0! tT t!t 60t$gt(sst6$ $t$d$4$PdHt(Qt]t 7>hyB  p`0Pdt(tt ^aU0 4I> p`Pt$g̍t(Սtt ^:^:^2^^%^^B^:^:^av - E p`0Pt$g\t(et{t =:PrP5^ZB!4)$p`Pt$gt()st$;;x$hdT4p 4Rp`Pdt(tth,N!d//Xr!d//Xr!//Xr!t d433r!td433r!33r!T P4T4r!tT4a4t!4 a44t!d 44̏t!44̏t!a44t!T4a4t!P4T4r-  p`0Pt$gXt(att6 =: N>h.hLA>h.hL:PArPaN:0N:@\Ar@\AⰛ0NB@\z@\U>B^! :@:A^EF@hjtT RB he5x}1I 1"-$h&m"&"l&L( 02d462=(J(*Z,4.!4`MM%s!`MM%s!4MM%s!MM%s!t4 NN's!t4 NN's!NN's$d4 pt$g(t(1ts `]!4p`Pt$gXt(1tatAFd 4 R pdt(tt`h,^@6Jg.h>4r p`PdĒt(͒tth,^`6Ug.h L:^ 4 p`Pdt(t&t `:`Q6!4P\g\r!P\g\r!4\\r!\\r!t\]Lr!\]Lrp!4_J_t!  dTJ__t!J__t!dT4_J_t 4R p`Pd(t(1t8t b@vxq!4bb%s!bb%s!4PcbcLs!PcbcLs!4PcbcLs!  d4ddLs!ddLs% 4 p`Pt$gt(tos `]08( d4 pt$g t(ts 4qf p`PdPt(Ytt ]B^AzA<^6ggtT^B^A:^:^2^A \a(ez|4 " q%& = p`P0t$gt(tt =:PjP !5djzl* 4 p`P#g* 4 p`Pt$gpt(ytt !: E] -  p`0Pt$gtb(Ŗt>t$  >0h.@h5:^:P ! P ! P ! B^z<^ >`hg tT=A B@ :P rP &8 9d|4"(DMB "p"$- w p`0Pt$gt(tt =A :ParP A:@ :a::2h6h55AB^*qB zH  F:UU4""t42`! Tɋ$t!ɋ$t* 4 p`Pt$gt(!fsty$-  p`0Pt$gt(Řt t = :P rP! a : : :^A :^:^:^A, Y=x  F 90 Q|2( d)4&" p Pt$ght(qtxt P Qd4 p-  p`0Pt$gt(tt = :A :P rP ^A:`d 5`d B! z! :^ :^:^&n .]eZ`4 p`Pt$ghtb(qt~t `2@i& 4 p`Pt$gtr(tt P=z6%x&!h'Q p`0Pt$gtR(tt =:PrPlB p0#gx!dT0t!0t T 4 2 p `dt(ttt  4 2pdt(tis%t4 Pt$gt(t t ^@2^nh*:!U j!t@iVir!@iVirC2^*_%x`!ha p`0Pt$glt(utt =:PrP^:^A:^A:^:^ Q z =5-  p`0Pt$gt(t:t = :P rP! A :^A :^:^:^ AAnAxMQ  ''h #t$#d##4"#Pdt(tt@hM $ $t $d$4$PdНt(ٝttph6h 96%xB!hC p`0Pt$g t()t|t = :P! rP A :a! :^2^!^!:^:^2^:^:5iE=aU z n .* nf( +  p`0Pt$gt(-ttU0 4" p`Pt$g$t(-tϟt@ ^:@a:A ^:@:1Qu^B@!:A1UP BP : a>db^`2^ʐ^E aA@HR^2^^6(^*F(*j,:.,0*4,R6%<*@66x.,l 4 2l42,td4P% 4" p`Pt$gDt(MtTt P `]!T4 p `t$g|tB(tt PD p`P0t$gtJ(tȠt `bN# 4 p`Pt$gtZ(tt c " p`P0t$g0t(9tFth*cMl% & p`0Pt$gtt"(}tt :c Ua 4 p#g@0t$gġtR(͡tԡt + `.  4 p#g@p`0Pt$g tj(t"t c2 T  4 R `P!t 0t!0td 4 Rpdt(tth d p#g@!4`  t!`  tr0dܢt(I|tt!t" #r!" #r( 4 p`Pt$g4tz(=tUt`h*pxZpx2 Q@ 4 p`Pt$gtj(tth*px2`m2 !T4 p `t$gԣtZ(ݣtth*pxpD6%xD!hE p`0Pt$g t2()tt : : r2% A Bc:+ a : pj+ A:!:!,^5z 5|q 8(hU'  p`0Pt$gt(tt :a:D\25'"xh' p`0Pt$gHt(Qtt !: >0hf`h A 2+ A::c2  : cU !B 6Jn % <B(e  <B N(z '  p`0Pt$gt(%tSt N2c!:2 @2+ + * ]2 p`P0t$gtJ(tt `zT 4 p`Pt$gtB(Ŧțt Pz( h p`0Pt$gtj(tt 2 ` F(p`P0t$g8tr(AtHt D0t$gdtZ(mttt `l0d4 pt$gtZ(tt c@2 j D#d4 p Pt$gtb(tt p2 `2+ l0N p`P0t$g(tJ(1t8t c`v T4 p `d`t(itoth,(r`P0!t pYZxt!pYZxt!p`0Pt$gȨt(Ѩtth*c2 2+ ,lu#dT4 p#g@Rp!4 ^*_t!4 ^*_t;*h%&tU&dT&4S&LPt$gttB(}tt :!:c: :cj :a} `M 8 3 "h> p`0Pt$gt(t t :Arc: ! x0 mi- td4Pt$gdtb(mtzt c2`bU 2 $t#$d"$4!$Pt$gt(t֪t 2+ :!)-N ( d$4# p Pt$gt(t3t + !:a:1 #ht3d241,Pt$gptB(ytt ca: ::A::z) = " l/ !hd8472 p Pt$gtr(tt :+ :rAFU!$p`0t$g8tb(AtNt 2` VL$L0t$gptb(ytt + dBp`0Pt$gtr(tƬt cp2 `2+ + J*. t! d 4 Pt$gt(tt :A  Q  p`0Pt$gDtj(Mt`t `2p2px B 4 p`Pt$gtZ(tt px2P;*h'&tY&dX&4W&PPt$gܭtb(t,t + :+ rcA:: :+  a::!.%]= F`&U 2 $ x 2 =V 2 !hdF4E@ p Pt$gt(tt : !:A:Ac!: |D\,  D:)x%h !dJ!4I!B p Pt$gt(tPt  : A: !:cArc: acXA-q Z>* td4Pt$gt(tt + 22c`j\:>( d4 p Pt$gt(t+t cP2 @2+ + + :c2 2>*I @ J  4 pt$g\tR(etIth&dT4pt$gtz(tt q%t4 Pt$gİt(Ͱtt c2 2 f5Pdt( tt@#h.P#h^0#hY.2 PdUh/ !h' p`0Pt$g0t(9tOt A:crnl%  p`0Pt$gt(tt ^: N`6#h  h(r9(x@$hA 4  p `Pt$gt(tt4 P :@ ! >$h.$h :c:^c: :c!:^Ac B a :+ au@ Bc: A A :cA:  :c:  :+ :aFi L n0  V0)  v |D $&P*},D0*02042 $%4Pdt(t.t%h. %h.0%h~%h10(p0Pt$gTt2(]tqt P 2^2 l. tdT4dt(tt `:.Q<" p`P0t$gt(tt`%h*cY  0!4@_%s!@_%sT 4 R p `dLt(Ut\t pxP!t d@Ur!@Urd42pdt(s@&s 4 p`Pdеt(qsٵt>y" "h4p`Pdt(tt  : M!4p%s!p%s% %d%T%4%!pdpt(ytt :Y  0!11 )tdTpt!  t!t! tdTpt  00d t(s)t0 h  p`0PdPt(Ytt ^24:PAPA>%h %h%h* 6 >P3 "h'P p`0Pt$gķtb(ͷt5t ^6%h*^p2@2P:p4Aj"Bڠ"B:@p2PAPA:^p2@2&h &:P.b!|@ $`. ,(P. d4430 pd|t(stbX0.(X0!4e^p`Pt$gt(tt ^2@p2A^!:@2 tX 6 4(X65 $x h4>8 p `Pt$g(t(1tFt ^2@:A N0, 4:4 p`Pt$gtt(}tt ^:@a:IN0, 4:4 p`Pt$gĹt(}t͹t N0. d;484 p Pt$gt(}ttN04 #t;#d:#48#4Pt$g8t(1tAta N00 4>6 p`Pt$gtt(ss}tP.) xh4% pt$gt(Mtt r$d!4  pt$gt(Mtthn!4#p`Pt$gt(Mttp T4 `#gP!t`--(t!`--(t!t`--(t!d//Xr!//XrT4 r p `!4KFp`Pt$gлt"(ٻtt P :^2@p2 xD 4 p#gH9(x $h! 4O D p `Pt$gHt(Qtt00&h.@&h.P&h.`&h.&h.&h.&h*^2@2A`A:PaePaB^2@2A ]ABPPAB^2@2A ]:Pa8 HM"$q.$0t".L&*@,J&*@,$`.,pP@ * 4-" p`Pt$gPt(Ytt PB`A:!:Z> 'h*PPEj 9n'  p`0Pt$gȽt(ѽt޽t <2PjH!"p`0Pt$g t(t(t `22ld4pdLt(Ut\t p*H1%  p`0Pt$gt(tt ^2^`jY:h*UQbj42 pdԾt(stMs!4 PSSNs!4 PSSNsRp`0d$t(-t3t'h6&!t`UxUr!`UxUr 4r p`Pd|t(tt `:X`y65 4r p`Pdt(ſtҿt `:`:HHx@h4 p`P! tT cct!cctP#gH!4 t de&f0t!e&f0t  r `P!t4h ipt!h ipt! dTlmt!lmt"E p`P0t$gt(tt : a`2 a 2(xb2 !d]!4\!V p Pt$gHt(Qtmt P :P :P @2P NFBd3420 pdt(stX0- M p`0Pt$gtR(t/t `<0<`<0!:^6p(h.(hU 6 ^B@:AH . \y.# 4 p`Pt$ghtb(qtwt(had 4 Rpdt(tt d4P86(h,4 ]@V( -  p`0Pt$gt(tst((h*> :p :>  >d^4^ >)hgtTuaB! ::^r0<^a<nduЊa0M5d x4"($1r*$ `"$&b(- A p`0Pt$gt(tt =:PArP!!=jj% %Ip ` 0 Pd,t(5tt ^B:^:^^5:^:^^B^:^y v *`>dd>X- a p`0Pt$gt(tTt&d2P jP ! :^j4^ >`*h@*htT^a5 B^r4^ >`*h@*htTP 8eELRL^ t ((V U^t( "($&5&]R0t$gt+`t!4p`Pt$gt(tTAs   p`P0dt( tt P`:`B:] 4r p`PdDt(MtZt QP:OPnB@ 0!4ds!ds!4ds!tr!4t!t!r!4pLs!pLs' + p`0Pt$g4tChr=ta,,h(d(T(4(!pdtt(}tt \4^j5 F+ T4 p `t$gt(tt 08=!^4 2 p ` Pdt(tt ^4^6*h.*h6P4 R p `d@t8rMt^tUt3gNl ( > 4 p`Pdt(tt+h.+h.0+h.`+hry# p`0Pt$gt(tt 22@Fe*+p`0Pt$g$tB(-tAt p:0> >ddA  p`0d`t(itpt ]-0dt(sttd4dt(Cst$ p`P0t$gt(stj p`P0t$gtJ(t rp`P0t$gt:(sstNp' ) p`0Pt$gHt2(Qtt P& a<A<^!<^>.h..h>`.hbP! p 2]r' ' p`0Pt$gt"(tt P& 44^PaHH6b^' / p`0Pt$g tb()tot P& !<A<^aPa^5:^a:^^:^a"JF6j^vz ~ $B  ~ $B  bpP0! d`t!d`t!d`t!`t  p`P0d0t(9tKt `6db` B:4 p`P0t$gttJ(}tt  `E* 4% p`Pt$gt(tt ^:PrP!Dl4 p`Pt$gtj(Cs tm.  4 2p`P1 ? p`0Pt$gLt(Utut :^2@2d  .  00dt(tt ^2@`2JX04 R pdt(tt@/h) 4>4 p`Pd t(t.tP/h*^2@2X64r6 4d\ p ` Pddt(mtt/h*^:@2ʐ^:@2 9X64x 6  4 p`Pdt(tt pxP<@ *(' # p`0Pt$g t(t7t !:Pr^2]2A "P qr0#g8  4 Rpdsd42 pdr!t 0b0$;s!  4 b00t!b00t! 4 b00t!4 b00t! 0b0$;s!t 0b0$;sBd0t(9t?t/hdT4 pd`t(itut/h*^P=!t`ivir!`ivir' T4 p `t$gt(tt ^@6P0h.`0h.p0h.0h44> 0h*' fp( d342, p Pt$g4tR(=tXt < ]4 ] ] % 4 p`Pt$gt(tt `2P`Nr 4 rp`Pt$gt:(sstdb~b~bzd 4 r pdt(tt0h: 44 2pt$g$t(-tRt 1h6 1hZP:^2@p2Ll D L!TpCChr!dCCdt!CCdt!pCChr!TpCChr0 4F< p`Pt$gt(tHt@1h*:^A:PP:^2@2pxApxAB^2@2  @@v.,0 4<4 p`Pt$gt(tt :^2^p2^ PT42 `!tMMt!MMt d8T7460pdt(s!t^X0+ 4G> p `Pt$gTt(]txt RA:^2@268 'tq'dp'4o'hPt$gt2(tt ^:@2R :^:@2 n .,&V.p`0Pdt(!t,t1h.@hl0 4qh p`Pt$g\t2(tetyn .,&V.' ) p`0Pt$gt2(tt1h*b8b %  p`0Pt$gt(tt P2^bP X60 4YN p`Pt$g@tb(Itt ^2@2aڐa:r:PA:P8 pe$8m. td4Pdt(tt ]2P:2PP:2P ."  td4Pd,t(t5t ." + 4E< p `Pt$glt(utt R:^2@`250+ 4E< p `Pt$gt(utt!0+ 4E< p `Pt$gt(ut t!0  4 pd 4 R pdh' T4 p `t$g|t(st~4p`0dt(tt :^`j^2@2 FLh6   0dt(t t ]P2PjP$&  0d(t(t1t$x&  p`0Pd\t(et$t4 ^!:@a :^:@ :A^`%`:^:^Au^AAB^ :^A U^B^ :^A 5^AAB^ :^A  ^ B^ :^A 4^*H `>T`> T$l&J(T,=.P0T4% 4 2x4 d4 p Pdt(tt ^@:@2^:^Md8RkZ1R2J3B4:526-7(x8$h9 4| t p `Pt$gt (tEt Pa BP z0c : :! :a ^!:@: jj   l.,   4 Rpdt(tt@3h48  2`P0! t Ft!Ft! t Ft!tpߥt!tpߥt!pߥt!d ЧPt!ЧPt!d ЧPt2p#g!d 4P~pt!P~pt4p`( d"4! p Pt$gt(t t ]:P@22PP@22P " ( d"4! p Pt$gDt(tMt " '  p`0Pt$gt(tt :P &2+ 4' p `Pt$gt(tt P!B^ nm44<6p`Pdt(tBt 3h,2^pb^2@2A >Th 6 ('p ` 0 Pt$glt"(utt ^: ]Ef('p ` 0 Pt$gt"(uttIf!t d4`xlr!`xlr!t d4lr!lr4p`0d0t(9tat <A:^`j^2@2 Fh 6   4 pdt(tt &  4 pdt(tt &+ P p`0Pt$gtr(tYt ^2@::^2@:ڐ%B^2@2P:^2@20J$y-$E. ,n.,( T .,( T .,. 4 pt$gtJ(tt Pbr  4 Rpdt(^st4D  4 pdt(tt &d 4 R pd$t(-t3t4h>@4r p ` PdTt(]tjt 2^@X ( d"4! p Pt$gt(tt " ( d"4! p Pt$gt(tt" 0 4eZ p`Pt$g t()tt( ^B:^:^P :: :Pr^A:@2l Bl:Pr^A:@2^AB@28.DU.6 - rr=rarQ5 @"&@(@ 4 rpt$gt:(sstb~)462 `Pt$g@t(Itdt PB^2@`2|||r|^|DDD  4 Rpdt(fst4@  4 p`Pdt(tt4h,2^`8T  00dt(st iXD$h0) 4+" p`Pt$g>A6B1xC-hD)p`0PdLt(Utt ^2@`2 ^a:@2A ^B@2YVL$f@"f L $$x p`P!; ;-h 4P;;t!P;;t>>B6C1xD-hE)p`0Pd,t(5twt ^A:@2 ^:@2 ^AB@2YeVL"f@"f L &&h p0!) )x  dT0GGt!0GGt$d)4($ pt$gthrt(NNBFC>D6E1xF-hG)p`0Pd@t(Itt ^!:@2 ^:@2 ^!B@2 VL"f@"f L )) p`P! xh 4SlTt!SlTtVVFNGFH>I6J1xK-hL)p`0Pd(t(1tvt ^!:@: ^:@a:^!B@:TL"d@"d L 2 2h pP0!))xd __t! __t  hR0' T4 p `t$gt(%+st~  rp`0d0t(9tFt 2^@@( d 4 p Pt$gpt(ytt ]2P@2P2P@2P2@" ( d$4# p Pt$gt(tt ]A:P@22P2P@:2P2 B "i(4p ` Pt$gLt(Ut\t Pt0#gX hdT4p  4 p  4 p`Pdt(tt5h,2^`8:d 4 R pdt(tt>>  00dt(st XD$h0`QKE?7 / * %x !h 4$ p `Pt$g|tB(tt , PBb4p`Pt$gtb(tt , ] |cR!J"B#:$2%-&(x'$h( 4Z R p `Pt$gt(t p Pt$gt(t&t ^p2@A:^2@A:T.,T . rp`0#g0d4 pP#g 0#g2 !dI!4F!@ p Pt$gt(tt ^p2@:A^2@:A T . ,(T . ,(T. p`0#gd42p#g 4 p#g"h 80#g/ htA4@> P#g  42 0pdt(stX02 !d!4! p Pt$gt(tt ^:@:A`2P5PBA :Pzj Z PtT.(4>8p`Pt$gTt(]tt 2^p2PP:^2@2A H` .#g8""hd 4 R p 4 rp#g0(4^Xp`Pt$gt(t t !:P fDZ3 "h,Z p`0Pt$g@t(It^t :P`j ]J$h42 pdt(tt p<`4pf6h,p 8 'h84zr p`Pt$gtr(tt :Za:ZA::PArPAz " T 4 R p `  p0!d66Ht!66Ht!d077Ht!077Ht!d7C8Ht!7C8Ht!d88Ht!88Ht!d@99Ht!@99Ht!d9S:Ht!9S:Ht$d4 p#g!d;#<Ht!;#<Ht' 'tK'dI'4H'BPdt(tt ^a:@:``2 ] j Z. #g #gr 0!4)$p`Pt$gt()s!t$d 4 R pdDt(4syKt#g 0#g2 !dI!4F!@ p Pt$gt(tt5 T . ,(T . ,(T.  4 p    d3420 pdt(st 0.r0d3420 pd,t(s5t e0.r0d3420 pd`t(sit 0.r04  pdt(tt`7h*^Pb 0#gx!t  x r! x r!t (!r! 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