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FnPEn@En@<P<0En0EnDnpDn@<P<CnCnCnCn@<P<FnFnpFn`Fn@<P<`Dn`DnCnCn@<P<PFnPFn@Fn0Fn@<P<FnFnFnFn@<P<CnCnpCn`Cn@<P<nnnn@<P<nnnn@<P<nnnn@<P<nn`nPn@<P<0o0oиj o@<P<ooPS^o@<P<АoАooo@<P<oohЏo@<P<oooo@<P<oooo@<P<0o0o0j o@<P<@o@oh0o@<P<oo0o o@<P<`o`ooo@<P<`o`oPo@o@<P< o o@jo@<P<ГoГooo@<P<oooo@<P<oooo@<P<oojo@<P<`o`oPo@o@<P<popoh`o@<P<0o0ooo@<P<ooopo@<P<oooБo@<P<oooo@<P<oooo@<P<popo`oPo@<P<oooo@<P<ooio@<P<0o0oi o@<P<oo^o@<P<зoзo^o@<P< p p p p@<P< p p p p@<P< p pRk p@<P< p pRk p@<P< p pp p` p@<P<@ p@ p0 p p@<P<@ p@ p0 p p@<P< p p p p@<P< p pp p` p@<P< p p p p@<P<P}qP}qP{l@}q@<P<qqqЂq@<P<}q}q}q}q@<P<qqqq@<P<pqpq`qPq@<P<PqPq@q0q@<P<}q}q{l}q@<P< q qxlq@<P<qqP8kpq@<P< q q }qq@<P<}q}qp}q`}q@<P<@q@q~q~q@<P<qqqq@<P<0}q0}q }q}q@<P<@q@qP8k0q@<P<qqP8kq@<P<}q}q{l}q@<P<qqpq`q@<P<qqP8kЅq@<P<@~q@~qP8k0~q@<P< ~q ~q~q~q@<P<|q|q|q|q@<P<p~qp~q`~qP~q@<P<qqpxlq@<P<qq{lq@<P<`q`qP8kPq@<P<@q@q0q q@<P<qq`qPq@<P<`q`q0q q@<P<pqpq9k`q@<P<qq9kpq@<P<PqPq qq@<P<qqqq@<P<PqPqk@q@<P<pqpq`qPq@<P<0q0qqq@<P<qqqq@<P<pqpqn`q@<P<PqPqqq@<P< r rrr@<P<PrPr@r0r@<P<rrrr@<P<asasbs`Xbses@esfsgsPdscs@4w@4w@4w@4w@4wZs@4w@4wsss@<P<@lslslsmsmswsnsus`stttt@<P<ptpt`tPt@<P<tttt@<P<0t0t tt P<7t`7t7t07tP<7t7t7t7t@<P<= (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 ++,,Can't happenC:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899falsetrue\\x%02xinfnanty10invalid string position...\\?\\/INVALID_ARGUMENT:, 'map/set too longunordered_map/set too longinvalid hash bucket count /\ was false.Stacktrace: narrowing_errorCpuai.onnx.trainingai.onnx.mlai.onnx.preview.trainingVIWEFAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityC:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::IDataTransfer::CopyTensorAsync is not implementedC:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=C:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)ai.onnxIsTensor()Trying to get a Tensor, but got: C:\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()Integer overflowC:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowORT_LOAD_CONFIG_FROM_MODELelem_type_ != nullptrTensor sequence must contain only primitive typesC:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::GetAti < tensors_.size()IsSameDataType(tensor.Get())TensorSeq: tensor to be added has a different data type.onnxruntime::TensorSeq::Addtried creating tensor with negative value in shapeUnknown Exception, got size overflowNot able to find appropriate IDataTransfer to copy sparse datanot enough space: expected C:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cc`anonymous-namespace'::GetDataTransfer`anonymous-namespace'::ValidateFillInputArgsStrings can only reside in CPU memoryOrtApis::FillSparseTensorCootried Filling sparse tensor with negative value in values shapetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::FillSparseTensorCsrCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::FillSparseTensorBlockSparseOrtApis::UseCsrIndicesOrtApis::UseCooIndicesthe ort_value must contain a constructed tensorOrtApis::UseBlockSparseIndicesRegisterCustomOpsLibrary: Failed to load libraryUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsRegisterCustomOpsUsingFunction: Registration function was not foundRegisterCustomOpsUsingFunction: Registration function name must be specified.onnxruntime_profile_EnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledstring buffer allocation failedlengths allocation failedinput array doesn't equal tensor sizeOutput buffer allocation failedOrtValue should contain a Tensor or a Sparse Tensorelement index is out of boundsThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse dataindex is out of boundsshape is invalidoutput buffer is too small. Use GetStringTensorDataLength.offsets buffer is not equal to tensor sizeout of indexbuffer size is too small for string elementindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.input array is too shortin[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.OrtCreateValueImplSeqHelperAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrSpecified provider is not supported.Invalid build with no providers availablelocation dimensions do not match shape sizethis API does not support stringsmax_meminvalid location rangeinitial_chunk_size_bytesarena_extend_strategyinitial_growth_chunk_size_bytesmax_dead_bytes_per_chunkInvalid key found: max_power_of_two_extend_bytesThe requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s 1.16.0ORT Build Info: git-branch=, git-commit-id=, build type=RelWithDebInfo, cmake cxx flags: /DWIN32 /D_WINDOWS /EHsc /EHsc /wd26812 /Qspectre /MP -DEIGEN_HAS_C99_MATHTensor 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> > > >::checkSequence is missing type entry for its elementonnxruntime::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 >,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> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkinvalid vector subscript != len >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::DataTypeImpl::GetType() == type_OrtCreateMapMLValueonnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggerOrtSessionOptions::RegisterCustomOpsLibraryC:\a\_work\1\s\onnxruntime\core\session\abi_session_options.ccFailed to unload handle for dynamic library Failed to load dynamic library execution_mode is not valid' was not found.graph_optimization_level is not validOutput buffer is not large enough for session config entrySession config entry 'Input index: contains null pointersMaximum string length for a provider options key/value is 1024.Provider options key/value cannot be emptyQNN execution provider is not supported in this build. SNPEOpenVINOWEBNNXNNPACKAZUREUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'VitisAICPUExecutionProvideronnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughRequested resource does not exist::OrtKernelInfo input index is out of boundsNo requested allocator availableFailed to fetch a stream hosting the requested resourceOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input does not have a typeOutput buffer is not large enough for ::OrtKernelInfo input name::OrtKernelInfo output index is out of bounds::OrtKernelInfo does not have an execution provider::OrtKernelInfo cannot get a valid logger from its execution provider::OrtKernelInfo output does not have a typeOutput buffer is not large enough for ::OrtKernelInfo node nameonnxruntime::CustomOpKernel::CustomOpKernelC:\a\_work\1\s\onnxruntime\core\session\custom_ops.cc' in custom op 'Unsupported version 'custom op registered at runtimeOnly the last input to a custom op may be marked variadic.InputOutputall typesOnly the last output to a custom op may be marked variadic.i == input_count - 1onnxruntime::CreateSchema1 == undefinedinput count does not matchi == output_count - 1There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.stndonnxruntime::IsCompatible input to be of optional type input to be of variadic type input to keep same homogeneitythcustom op schemas mismatch, expecting output count does not match output to be of optional type input to keep same arity input to be of single type output to keep same arity output to be of single type output to be of variadic type output to keep same homogeneityonnxruntime::CreateCustomRegistryonnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size (C:\a\_work\1\s\include\onnxruntime\core/graph/graph.hValidating no unexpected access using an invalid node_index. Got:node_index < nodes_.size() Max:Required output at index output_ptronnxruntime::Graph::NodeAtIndexImpl is not present.Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::RequiredOutputC:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.honnxruntime::OpKernelContext::OutputIsSameDataType(tensor)C:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime_providers_shared.dllonnxruntime::LoadDynamicLibraryFromProvideronnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime::ProviderSharedLibrary::EnsureProvider_SetHostonnxruntime_providers_cuda.dllonnxruntime_providers_cuda_ut.dllGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dllonnxruntime_providers_cann.dllonnxruntime_providers_rocm.dlldevice_typeenable_vpu_fast_compileonnxruntime_providers_tensorrt.dllonnxruntime_providers_migraphx.dllcache_dircontextdevice_idnum_of_threadsnum_streamsonnxruntime::TryGetProviderInfo_CUDAenable_opencl_throttlingenable_dynamic_shapesonnxruntime::TryGetProviderInfo_ROCMSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryCANN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryCUDA execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryoneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.invalid unordered_map keyRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputRelease_State_Create_State_Compute_onnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'' op_type:kernel already mapped to existing nodeinvalid node input count: , expect: matching node is missingC:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsarg_num < static_cast(input_count_)invalid arg_num.invalid node output countKernel definition was not found for node Domain:'onnxruntime::standalone::CreateOpstandalone_Attribute type not supported yet.onnxruntime::standalone::InvokeOpOrtMemoryInfo is nullEnv is nullProvided 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 nullC:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::Releaseenv_ptr == p_instance_.get()C:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputmapped_feed_names_.size() == feed_names_.size()Size mismatch: index= it.second=Size mismatchonnxruntime::IOBinding::SynchronizeOutputsonnxruntime::IOBinding::SynchronizeInputsonnxruntime::SyncProvidersp_provideronnxruntime::IOBinding::BindOutputImplmapped_output_names_.size() == output_names_.size()bad castinvalid stoi argumentstoi argument out of range]Device:[DeviceType: MemoryType: DeviceId:name: id: OrtMemType: OrtAllocatorType:OrtMemoryInfo:[CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProvideronnxruntime::ExecutionProviders::AddProvider has already been registered.C:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraphIsGraphCaptured()C:\a\_work\1\s\onnxruntime\core/session/inference_session.hCached EP instance for graph replay is not set yet before calling ReplayGraph()IsOptionalTensor(type)Provided type is not an optional tensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorC:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorMemcpyTransformerORTM/[json.exception.] session.set_denormal_as_zerosession.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.disable_quant_qdqsession.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_basesession.debug_layout_transformationsession.intra_op_thread_affinitiessession.allow_released_opsets_onlymemory.enable_memory_arena_shrinkagesession.optimized_model_external_initializers_min_size_in_bytessession.optimized_model_external_initializers_file_namesession.disable_cpu_ep_fallbackMemcpyFromHost%Y-%m-%d_%H-%M-%Sdisable_synchronize_execution_providers value of 'save' is only valid when saving an ORT format model.saveShapeMemcpyToHost are '0' and '1'. The environment variable contained the value: Invalid value for applyC:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configThe only supported values for the environment variable offonis_model_proto_parsedModelProto needs to be parsed to check for ORT config within itstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()Flush-to-zero and denormal-as-zero are -Creating and using per session threadpools since use_per_session_threads_ is truegraph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()onnxruntime::InferenceSession::ConstructorCommonAffinity string must not be emptyDynamic block base set to session--intra-op-inter-opto.custom_join_thread_fncustom join thread function not set for intra op thread pool!to.affinity_str.empty()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 SEQUENTIALcustom join thread function not set for inter op thread poolCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: session_env.EnvCreatedWithGlobalThreadPools()onnxruntime::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 DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.It is enabled within session options which may result in lower performance.QDQ quantization is not supported while using the DML Execution Provider. Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsCan't register custom op domains with ORT for onnxruntime::InferenceSession::SaveToOrtFormatonnxruntime::InferenceSession::LoadWithLoaderThis session already contains a loaded model.Exception during loading: Failed to save ORT format model to file: Load model from failed:model_loading_uriEncountered unknown exception in LoadWithLoader()model_loading_arrayFailed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ORTmodel_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.onnxruntime::InferenceSession::TransformGraph::::operator ()post_layout_transform_step_.onnxonnxruntime::InferenceSession::TransformGraph::::operator () bytes were able to be read.onnxruntime::LoadOrtModelBytesCastFloat16Transformeronnxruntime::InferenceSession::TransformGraphThis session has already been initialized.onnxruntime::InferenceSession::LoadOrtModelWithLoaderonnxruntime::InferenceSession::LoadOrtModel::::operator () failed. Only ) has saved runtime optimizations. They will be ignored.Serialized 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 The old ORT format model (version 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.it is safe to use the CUDA Graph feature.This session cannot use the CUDA Graph feature as requested by the user as all compute graph nodes have not been partitioned to the CUDA EP. as all compute 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.As long as the intermediate shapes produced in the model using the representative input used to capture the CUDA graph, will match the shapes produced in the model for other inputs of the same shape as the representative input (common case), This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to as all the graph nodes have not been assigned to This model has shape massaging nodes that will execute on CPU. Use the CUDA Graph feature with caution. Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.Conflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session will use the CUDA Graph feature as requested by the user.Session successfully initialized.1024Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.NchwcTransformer 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::Runinput name cannot be emptyStart another run for necessary memory allocation or graph capture.model_runEncountered unknown exception in Run()unknown exceptionfetch_unique_ptrs[i] != nullptroutput name cannot be emptyNULL input supplied for input onnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelMetadataintra op thread pool must have at least one thread for RunAsynconnxruntime::InferenceSession::EndProfilingProfiler is disabled.onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputs does not support TunableOp.onnxruntime::InferenceSession::SetTuningResultsCannot find execution provider Could not write a profile because no model was loaded.Failed to load TuningResults (index=). Reason: Invalid TuningResults (index=). Correctly set TuningResults for , enable TunableOp for usingDid not find an arena based allocator registered for device-id combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Unsupported device specified in the memory arena shrink list: Unable to shrink arena: error message: The registered allocator for device-id combination is not an arena based allocator: onnxruntime::InferenceSession::CreateLoggerForRunrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasonnxruntime::InferenceSession::AddPredefinedTransformersonnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: 961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5onnxruntime::ParseStringWithClassicLocale_.jsonlist too longC:\a\_work\1\s\include\onnxruntime\core/common/parse_string.hother_errorlocaltime_s(&local_tm, &in_time_t) == 0Failed to parse value: ""onnxruntime::`anonymous-namespace'::GetCurrentTimeStringycom.microsoftorg.pytorch.atencom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwc[ShapeInferenceError] Unsupported Source/Target type= Target=Mismatch between source and target type. Source=tensor(int32)tensor(int64)tensor(float16)tensor(float)unknowntensor(uint32)tensor(uint64)tensor(float8e5m2)tensor(float8e5m2fnuz)tensor(uint8)tensor(uint16)tensor(double)tensor(bfloat16)tensor(float8e4m3fn)tensor(float8e4m3fnuz)seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))tensor(int8)tensor(int16)seq(tensor(uint8))seq(tensor(uint16))seq(tensor(double))tensor(string)tensor(bool)tensor(complex64)seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(complex128))seq(tensor(bfloat16))tensor(complex128)seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))Schema error: line Trying to register schema with name known by the checker., but its domain is notin onnx/defs/schema.h)., but it is already registered from file ) from file version: (domain: , but its version is not forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you in the inclusive range [An allocator for this device has already been registered for sharing.Exception caught: intra-opinter-opC:\a\_work\1\s\onnxruntime\core\session\environment.ccOnly CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.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.stringYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type. is not implemented in CreateAndRegisterAllocatorV2()TinputXoutputvalue at line ort_configsession_optionstuning_results, column Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: Unsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsC:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDonnxruntime::SetExecutionModeSequential modeParallel modeSetting execution_mode to Setting enable_profiling to epresultsvalidatorsSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingonnxruntime::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. Ignoring...Tuning results stored in the model file cannot be parsed. Error message: Found tuning results in the model file to be used while loading the modelgraph_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: onnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadata' not foundkey 'cannot use at() with nullobjectarraybooleancannot use key() for non-object iteratorsinvalid map keycannot get valuebinarydiscardednumberout_of_rangetype_errorinvalid_iteratorwhile parsing - ; last read: 'unexpected parse errorparse_errorsyntax error true literalfalse literalnull literal; expected invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literal']''}'':'','string literalnumber literal'[''{'invalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signend of input'[', '{', or a literalunknown tokeninvalid 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 comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid 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+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: 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+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+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+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+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 byteinvalid 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 \u001Ccannot compare iterators of different containersobject keyobject separatornumber overflow parsing 'vector too longiterator out of rangecannot use erase() with iterator does not fit current valuetype must be string, but is type must be object, but is type must be number, but is type must be array, but is AzureExecutionProvideronnxruntime::GraphTransformer::RecurseC:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hCasttoidx < input_defs.size()onnxruntime::IsIsolatedFp16NodeOnCpuInsertedCast_cast node to cast from float16 to float32 on cpudtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()C:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplonnxruntime::InsertCastTransformer::ApplyImplArmNNExecutionProviderACLExecutionProviderROCMExecutionProvidersession.enable_quant_qdq_cleanupsession.disable_double_qdq_removeroptimization.enable_gelu_approximationQDQFinalCleanupTransformerBiasDropoutFusionAttentionFusionBiasSoftmaxFusionBiasGeluFusionsession.x64quantprecisionsession.qdqisint8allowedCommonSubexpressionEliminationCastEliminationConvMulFusionConvBNFusionDivDivMulFusionConstantSharingConvConvAddFusionDynamicQuantizeMatMulFusionDropoutExpandEliminationEmbedLayerNormFusionEliminateDropoutDoubleQDQPairsRemoverGeluFusionGeluApproximationGemmSumFusionGemmActivationFusionFastGeluFusionExpandGatherToSliceFusionGatherToSplitFusionEliminateIdentityLayerNormFusionIdentityGemmTransposeFusionGemmMatmulTransposeFusionMatMulScaleFusionAddNoopEliminationMatMulActivationFusionSimplifiedLayerNormFusionMatMulIntegerToFloatFusionMatMulAddFusionAvx2WeightS8ToU8TransformerPreShapeNodeEliminationClipClipQuantRewriteMulSubWhereNotWhereFusionFuseReluClipQuickGeluFusionRocmBlasAltImplReshapeFusionQDQS8ToU8TransformerQDQPropagationTransformerReluReluQuantRewriteUnsqueezeUnsqueezeEliminationLevel_RuleBasedTransformerEliminateSliceSkipLayerNormFusionTransposeOptimizerSliceonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildonnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformerC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cc/duplicatedEnsureUniqueDQForNodeUnitonnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeAdded by DequantizeLinearonnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.ccՁVitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexMemcpyExecution type 'dup_replacements.find(&arg) == dup_replacements.end()Copy from/to host memoryC:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImpl' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessDefsonnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()C:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::ApplyBad optional accessConcatQLinearGlobalAveragePoolResizechannels_lastQLinearAveragePoolFusedConvLayout/Transpose optimization for failed: onnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered C:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccC:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.ccsplitaxisonnxruntime::GatherToSplitFusion::ApplyImplGatherSqueezeAxesInitializeronnxruntime::GatherToSliceFusion::ApplyImplaxesnum_outputsSqueeze for Fused Gather nodesSqueezeSplit for Fused Gather nodesSplitSliceAxesInitializerSlice for Fused Gather nodesUnsqueeze_UnsqueezeAxesInitializerunsqueeze_output_Unsqueeze for Fused Gather nodesRangerange_input_defs.size() == 3Could not find OrtValue with name 'endonnxruntime::ConstantFolding::ApplyImplConstantFoldingstart. Can't constant fold Unsupported output type of Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size() node 'can't constant fold C:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccQuantizeLinearUnsupported data type: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializerC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccConstant initializer NodeArg shape should not be null. NodeArg: shapeonnxruntime::`anonymous-namespace'::GetOutputNodeMergesMatMulonnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1mul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2div_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeonnxruntime::MatMulScaleFusion::ApplyImplFused MatMul and Scale_FusedMatMulAndScaleFusedMatMulalphaonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()Expected value:Slice parameter is not expected. Input index:Slice does not have enough number of inputsSlice ends is less than INT_MAXonnxruntime::AttentionFusionHelper::CheckSliceParametersC:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hinput_indices.size() == expected_values.size() && input_indices.size() > 0Gemm 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 pathReshapeFaild to match gemm pathStart MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphFaild 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 falseGemm bias is not constant initializerGemm bias shape is not expectedStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializergather input 1 value is not expectedPass MatchGemmSubgraphunsqueeze_after_gather axes value not expectedgather axis value not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedPass ValidateGemmInitializerunidir mask is not constantGemm weight is not constant initializerGemm weight shape is not expectedDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceSoftmaxFailed to find Softmax nodeStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskmask_sub const input not matchedmask_mul const input not matchedmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2Output edge count not expected for mask nodesSoftmax attribute axis is expected to be 3Output edge count not expected for SoftmaxFailed to find path for maskequal const not matched.Failed to find reshape shape path 1where const not matched.Failed to find shape pathEqualFailed to find mask pathPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find path for past_kFailed to find path for present_konnxruntime::AttentionFusionHelper::MatchPastSubgraphTransposePass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphFailed to find reshape shape path 2gather indices not matched.present_v_unsqueeze axes value not expectedpast_v_gather indices != 1present_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_concatonnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vonnxruntime::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 inputStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQPass 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, 3k_transpose has not perm attributek_transpose perm attribute not matchedStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKq_transpose perm attribute not matchedPass CheckNodesInPathQq_reshape const not matchedqk_div const not matched.onnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulStart FuseGptAttentionMask_Int32Cast mask from int64 to int32k_reshape const not matchedPass CheckNodesInPathKCheckNodesInPathQ returns falseUsing transpose optimized patternFailed to find path for qq and v are not from same Split nodeMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFaild to find path v to SplitCheckNodesInPathV return falseAttentionnum_headsMatchPastSubgraph returns falseFused Attention subgraphs k and v are not from same Split nodeCheckNodesInPathK returns falseopt_k_transpose perm attribute not matchedFailed to find path for konnxruntime::ConvertMaskToInt32C:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccqkv_biasMask shape is unknown or not 2D, or data type unknownFused an attention node for GPT.qkv_weightsunidirectionalmask_filter_valueq root should be layer normalizationq_matmul and q_add shape not matchedTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplLayerNormalizationshape of layer norm bias tensor not expectedMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFailed 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 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::FuseSubGraphepsilonconv_W_tensor_protoconv_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoC:\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::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantLeakyReluAveragePool1DQArgMaxdropDQMaxPooldropdropSplitQDQQDQSelectorActionTransformer*DQ2DQSigmoidGlobalAveragePoolC:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.honnxruntime::NodesToOptimize::GetNodeindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)Expected activation node.HardSigmoidTanhactivation_paramsbetaoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Failed to get Clip min/max constants.activationC:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesactivation != nullptrConvActivationFusionConvAddReluConvActonnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"Expected Conv then Add. with activation C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cconnxruntime::MatMulActivationFusion::ApplyImplactivation__FusedActivationFusedMatMulActivationC:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination_QuickGeluC:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.cconnxruntime::QuickGeluFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of minFuseReluClip__min_zero_constantGeluonnxruntime::GeluApproximation::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationFastGeluTotal Gelu Approximation (FastGelu) node count: BiasGelu0@ةC:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccallowzeroFused reshape node: Total fused reshape node count: startsendsConcatTrainingCannot replace concat node with initializer:onnxruntime::ReshapeFusion::ApplyImplonnxruntime::ReshapeFusion::Fuse_SubgraphtransBnew_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccnew_gemm_output_defs.size() == 1Fused Gemm with Sum_sum_transformedgemm_input_edge.src_arg_index < 2transAsum_output_edge.src_arg_index == 0!sum_input_movedgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())Sumother_sum_input != nullptronnxruntime::GemmSumFusion::SatisfyCondition and Invalid node index node != nullptrAt least one graph node must be specified in the propagation edge.src_node || dst_nodeonnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndC:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndnode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput_post_dqInserted by QDQPropagationTransformerFailed to set op schema for added Q node._dqFailed to set op schema for added DQ node.src_node != nullptronnxruntime::`anonymous-namespace'::InsertQDQPairC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cc". at NodeArg "", index: node ("Inserting Q/DQ pair between _pre_qonnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgedst_node != nullptronnxruntime::`anonymous-namespace'::GetNextPropagationEdgeonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::QDQPropagationTransformer::ApplyImpl_Int32Cast Input from int64 to int32_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputGather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeNonZeroConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherC:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.position_embeddingsmask_indexfused EmbedLayerNorm subgraphs EmbedLayerNormalizationWord embedding shape not expected.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.Failed to get initializer tensor.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplPosition embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. YieldOponnxruntime::DynamicQuantizeMatMulFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeLinearDynamicQuantizeMatMulThe model has input '' onnxruntime::FreeDimensionOverrideTransformer::ApplyImplFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.Conflicting free dimension overrides.C:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccwith a fixed dimension size which does not equal the specified override of C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplfused Matmul and Add ThresholdedReluParametricSoftplusC:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplfused Gemm with activation fused FusedGemmEluSoftplusSeluScaledTanhSoftsignUnexpected data type for QuantizeLinear input y_zero_point of C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::GetQConstantLowerUpperonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesNhwcFusedConvonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesC:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.ccNhwcFusedConvActConvAddActConvAddActivationFusionQAttentionMatMulIntegerQGemmQLinearConvQLinearMatMulweight_zp_s8_2_u8 unable to locate recurrence tensor or its zero point value,DynamicQuantizeLSTMonnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplC:\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,Fused Gemm with Transpose_transformedonnxruntime::UpdateConsumerCountcast != nullptrC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()Created a new Transpose node to interchange Cast and Transpose nodesonnxruntime::MatmulTransposeFusion::ApplyImplonnxruntime::ReorderCastAndTransposeCreated a new Cast node to interchange Cast and Transpose nodestranspose_node.InputDefs().size() == 1onnxruntime::GetTransposePermsfused MatMul and Transpose MatMul_With_TransposetransBatchAtransBatchBdeque too longConvMulFusion_W_ConvMulFusion_Mul_B_C:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoonnxruntime::ConvMulFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.cc__backwardpassonnxruntime::RocmBlasAltImpl::ApplyImplSqrtPowC:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccReduceMeanonnxruntime::SimplifiedLayerNormFusion::ApplyImplSimplifiedLayerNormalizationfused LayerNorm subgraphs stash_typeonnxruntime::LayerNormFusion::ApplyImpl0@ ȩC:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs Cast Input to floatonnxruntime::SkipLayerNormFusion::ApplyImplSkipLayerNormalization_Float0@ةreorderstridesdilationsReorderInputpadsC:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cc_nchwcoutput_channels <= nchwc_output_channelsonnxruntime::NchwcTransformerImpl::TransformConvreshapespatialNOTSETSAME_UPPERkernel_shapeauto_padgroupBuffer overflowSAME_LOWERVALIDnearest_modeflooralign_cornershalf_pixelGlobalMaxPoolonnxruntime::NchwcTransformer::ApplyImplUpsamplescaleschannelsmode_bn_nchwcReorderOutputcoordinate_transformation_modeasymmetricnearestlinearaxis is not in valid range [-C:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisIsAxisInRange(axis, tensor_rank)C:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImplis_inner_broadcastBiasSoftmax into softmax(input + bias)t΁DoubleQDQRemoved_Not eliminating output of node onnxruntime::CommonSubexpressionElimination::ApplyImpl[] because it's the graph's output.representative.output_index != kInvalidOutputIndexC:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cc" and with name "onnxruntime::QDQFinalCleanupTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cconnxruntime::`anonymous-namespace'::CleanUpNodeSequenceCleaning up back-to-back nodes: NotC:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer.cconnxruntime::TransposeOptimizer::ApplyImplTranspose optimizer failed: C:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplT1NhwcTransformerBitmaskBiasDropoutfused Add-Dropout-(Add) for onnxruntime::BiasDropoutFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBitmaskDropoutBiasDropoutonnxruntime::MatMulIntegerToFloatFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeC:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::GeluFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs onnxruntime::BiasGeluFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelu0@ة@C:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.cconnxruntime::ConstantSharing::ApplyImplTotal shared scalar initializer count: ortshared_  ConvAddFusion_Add_B_C:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_B_add_B_tensor_protoC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointq_or_dq_input_defs.size() >= 2com.microsoft.QLinearReduceMeancom.microsoft.QLinearMulcom.microsoft.QLinearSigmoidcom.microsoft.QLinearConcatcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearGlobalAveragePoolNhwcMaxPoolcom.microsoft.QLinearAddstorage_order does not match rank perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length successFailed to find initializer for name: Failed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializerNo NodeArg found for name onnxruntime::ApiTensor::Dataonnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrFailed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()onnxruntime::ApiTensor::NumElementssize >= 0C:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimization\ort_optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDimsPermutation entry out of bounds for shape Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.const_transpose_optimizersince_version.has_value()onnxruntime::GetSinceVersionForNewOpdomain == kOnnxDomainopset_import_iter != domain_to_version_map.end() domain not found in opset imports.Added in transpose optimizer_outTranspose optimizer is expected to add only onnx domain ops. Domain: provided for op: to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializernew_num_elts == old_num_eltsCannot reshape initializer dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Existing destination type is not compatible with source type.onnxruntime::ApiGraph::CopyValueInfo+ځ+ځ +ځ +(с+(с +(с+(с+(с+ہ+ہ +ہ +  +  +Ӂ +Ӂ +Ӂ  8  " Expnoop_with_empty_axesReciprocalLogReduceSumkeepdimsTanCosCoshSinhCeluSignSinHardSwishIsInfRoundShrinkIsNaNCeilAbsNegFloorBitShiftPReluLessGreaterOrAndModXorAtanhAcoshMinMaxAcosAsinAsinhAtanArgMinReduceL2HardmaxTileReduceProdReduceMinReduceL1ReduceSumSquareReduceLogSumPadReduceMaxReduceLogSumExpLessOrEqualGreaterOrEqualCastLikeMeanDepthToSpaceGridSampleSpaceToDepthMaxUnpoolLpPoolLRNGlobalLpPoolUnsupported ONNX opset: LogSoftmaxConvTransposeInstanceNormalizationonnxruntime::Initializer::InitializerC:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccInitializer must have a datatypeutils::HasDataType(tensor_proto)onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncecalled_ == 1scalers.size() == 1 || scalers.size() == num_blocksonnxruntime::Initializer::scale_by_axisInvalid other(scalers) sizeAxis must be non-negativeaxis >= 0onnxruntime::Initializer::mulonnxruntime::Initializer::divsize() == other.size()onnxruntime::Initializer::subonnxruntime::Initializer::addExpecting the same sizeExpecting the same data typedata_type() == other.data_type()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()onnxruntime::Tensor::MutableDataAsSpanC:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccmaxUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()RandomNormalLikeMultinomialRandomNormalRandomUniformLikeRandomUniformIhIxI@IXIallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoC:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()onnxruntime::TryCreateKernelTried to allocate without valid type information, ort_value index=Too many produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations) does not match produced node op in runtime optimization record (New node op (onnxruntime::SetOpSinceVersionForProducedNodes).onnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsNot enough produced nodes in the runtime optimization record.Matched onnxruntime::MatchAndProcessonnxruntime::SelectorActionRegistry::RegisterSelectorAndActionC:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccExisting registration with name inserted_in_name_to_entryMissing action Nodes to optimize are not valid, skipping action. for transformer Applying runtime optimization action opsetinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14agenerated at runtimeQLinearonnxruntime::ReplaceWithNew::Runonnxruntime::CreateReplacementNodeC:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::MergeIntoTarget::RunFailed to remove node.Failed to set node op schema.onnxruntime::ReplaceWithNew::RunForSaveonnxruntime::NodesToOptimizeIndicesBuilder::Buildtarget_node != NodesToOptimizeIndices::kEmptyNodeIndexA target node must be set.onnxruntime::GetNodesToOptimizeIndices::::operator ()node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexNode index value is too large to save to ORT format model: C:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cconnxruntime::`anonymous-namespace'::MoveInputOutputImplIndex out of rangeonnxruntime::MoveInputOutputQDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: onnxruntime::QDQ::ValidateNodeGroupDQNodesQDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.ccFlattenJsExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderQNNExecutionProviderNnapiExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderCANNExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderTvmExecutionProvideronnxruntime::GetAvailableExecutionProviderNames::::operator ()C:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.ccprovider.name.size() <= kMaxExecutionProviderNameLenMake the EP: name shorterttxց΁TT8UXցTT8TPT́́uhTUUT`ˁXonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsC:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernels does not match input rank: is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max()perm: does not align with rank of input data: [ perm size: Mismatched data types between input and output Tensors. (local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::Transpose::Computeinput_tensor_ptr != nullptronnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)center_point_boxonnxruntime::NonMaxSuppression::Computeoutput != nullptrcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsMissing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK()onnxruntime::GatherBase::GatherBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hTindonnxruntime::Gather::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccGather Tind type not supported in this build. must be within the inclusive range [indices element out of data bounds, idx=Missing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hX != nullptronnxruntime::UnsqueezeBase::PrepareComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAxes input is nullaxes_tensor != nullptrAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::ComputeAttempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputC:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hscan_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::Initialize dimensions or more but input had shape of Expected Invalid scan input: has length of bodyinfo.GetAttr("body", &proto).IsOK()onnxruntime::Scan<9>::InitC:\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_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputVScan' 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::CreateLoopStateVariablesOutput OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)info.GetAttrs("scales", scales_).IsOK()stretchkeep_aspect_ratio_policyextrapolation_value model uses the deprecated attributeyet this opset `tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floorcubic_coeff_aexclude_outside and anti-aliasing is set to exclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to or (default) or cubicinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.hantialiasceilnearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.Resize operatorUpsample operator'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 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales size should be greater than 0.onnxruntime::UpsampleBase::ParseScalesDataall values in axes should be less than rank of the data. It can only be mode attribute is onnxruntime::UpsampleBase::StringToUpsampleModenot_largernot_smaller] is not supported!keep_aspect_ratio of [onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicypytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetric] is not supportted!coordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceil, requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0input_shape_size == sizeThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size != -1onnxruntime::ReshapeHelper::ReshapeHelperC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hA dimension cannot be less than -1, got requested_shape[i] >= -1At most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()A shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size T2zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_zero_point must be null or a scalar or 1D tensor or size 1.C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.cconnxruntime::PrepareForQDQDequantizeLinear into BFLOAT16 is not implemented yet.DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computezero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::QuantizeLinear::ComputeUnsupported input type.onnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computesaturateonnxruntime::QuantizeLinear::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.honnxruntime::PadBase::PadBaseInvalid 'pads' attribute valueconstantedgereflectInvalid 'mode' attribute valuewrapdims.size() == extents.size() && dims.size() >= steps.size()Pads tensor size should be equal to twice the input dimension count axes_tensor_dims.size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkipsonnxruntime::SliceIteratorBase::Initdims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Unsupported input data type of Unexpected mode of C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::PadValueFromFloatpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]Axes tensor should be a 1D tensor onnxruntime::Pad::Computedata_rank > 0Input tensor has no dimensions'pads' has wrong number of valuesonnxruntime::PadImpldata_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroILoopBGraph in 'body' attribute of Loop should have inputs. Found:onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: All scan outputs MUST be tensorsLoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutput was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::ExecuteLoop had zero iterations and the shape of subgraph output onnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size() but has C:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cc'Loop' node has outputs so the subgraph requires Got:num_subgraph_outputs - 1 == num_outputsInconsistent shape in loop output for output. Expected:onnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Initfeeds_fetches_manager_onnxruntime::Loop::Compute'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of ' attribute.onnxruntime::If::Computeonnxruntime::IfImpl::InitializeSubgraph SessionState was not found for 'Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output If outputs which doesn't match the subgraph's outputs.num_subgraph_outputs == static_cast(num_outputs)'If' node has C:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infoinfo.GetAttr("then_branch", &proto).IsOK()then_branchelse_branchonnxruntime::If::Initinfo == nullptrinfo.GetAttr("else_branch", &proto).IsOK()then_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::SetupSubgraphExecutionInfoonnxruntime::IdentityOp<0>::ComputeUnable to get an allocatorC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<1>::Computeonnxruntime::Broadcaster::Broadcasterlargest <= 1offset % span_size_ == 0InputBroadcaster can only start at span boundary!) for tensor of length:onnxruntime::InputBroadcaster::AdvanceBystart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [onnxruntime::TensorAllocator::TensorAllocatorAttempting to broadcast an axis by a dimension other than 1. by onnxruntime::BroadcastIterator::Initaxis == 1 || axis == largestonnxruntime::BroadcastIterator::AppendC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hCan broadcast 0 by 0 or 1. is invalid.onnxruntime::Max_6::Computedata_n.Shape() == shapedata_1.Shape() == shapeonnxruntime::Mean_6::Compute!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.Tensor with shape information must be 1 dimensional.onnxruntime::ExpandBroadcastLooperonnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1fmod must have value either 0 or 1fmodonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)onnxruntime::mod_internal::CallModImpl::operator ()fmod attribute must be true for floating point typesBitwiseNotBitwiseAndBitwiseXorBitwiseOrMust have 1 or more inputsUnsupported X type: onnxruntime::Min_6::ComputeinputCount >= 1All inputs must have the same shapeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::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 >::Computeinput_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::Sum_6::Computeinput_count >= 1cur1 == end1onnxruntime::Sum_6::Computecur_out == end_outonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.Unsupported Y type: BroadcastLooper requires two tensors as input.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()onnxruntime::BroadcastLooperhelper.HaveTwoTensorInputs()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftdirectionRIGHTLEFTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftonnxruntime::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::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stod argumentstod argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastAttribute saturate is only used for cast to float 8 types.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.-INFINFNaNsnprintf_result > 0onnxruntime::`anonymous-namespace'::CastToString%.8gsnprintf() failed with return value: Failed to write value with snprintf().snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()kernel_shape[dim] > 0Pad should be smaller than kernel.Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2info.GetAttr("p", &p_).IsOK()onnxruntime::PoolProcessContext::initonnxruntime::PoolAttributes::ComputeSizePadDilationspUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypeNo kernel shape is set.C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesinfo.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()ceil_modecount_include_padonnxruntime::PoolBase::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hInput dimension cannot be less than 3.Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.`InlinedVector::at(size_type)` failed bounds checkonnxruntime::MaxPoolV8::ComputeImplonnxruntime::AveragePoolV19::ComputeUnsupported kernel dimension : onnxruntime::LpPoolV18::Computeonnxruntime::LpPoolV18::LpPoolV18onnxruntime::Pool::ComputeUnsupported pooling size : C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.hinput count mismatchSizeOptional op must have a TypeProto in the 'type' attribute if the attribute is presenttypeC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::Optionalattr->has_tp()OptionalGetElementOptionalHasElementOptionalOonnxruntime::Optional::ComputeOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.cconnxruntime::PropagateInputOrtValueToFirstOutputonnxruntime::OptionalGetElement::ComputeTrying to use OptionalGetElement on an optional type OrtValue which contains no dataThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))Invalid value in 'split' attribute. All values must be > 0If 'num_outputs' is specified, the 'split' input should not be provided.C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Invalid num_outputs value of . Size of dimension being split is Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=The split tensor must be a vector tensor.Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::SplitImpl::Computesplit_tensor->Shape().NumDimensions() == 1dst.DataType() == src.DataType()src and dst types must matchonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()C:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopyonnxruntime::StridedCopy::::operator ()last >= firstdst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()counter.current_offset == lasttotal_num_elements_to_copy >= 0copy shape must have non-negative sizeonnxruntime::StridedCopyCPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.ScatterElementsScatterCPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'. vs Indices and updates must have the same rankdata type is different from updates type is greater than input dim=Indices must have the same rank as Input. Indices rank=. Input rank=Indices vs updates dimensions differs at position=Indices type is not supported.Indices dim= at pos=nonereductionC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::ScattermuladdCPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.input shape: , indices shape: ScatterNDupdates shape: , data shape: last dimension of indices must not be larger than rank of input tensorinput tensor and indices tensor must has rank larger than 0. CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()invalid indice found, indice = Col2Imdilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.cconnxruntime::Col2Im::Computeonnxruntime::Col2Im::Col2Imstrides_.empty()strides_.size() == image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.pads_.empty()dilations_.empty()C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.hC:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptylast_loop_red_size > 0onnxruntime::ValidateMustBeOverloadedmust be overloaded.projected_index.size() > 0last_loop_size > 0onnxruntime::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() == 1An axes tensor must be a vector tensor.Output size mismatch.onnxruntime::ValidateFastReduceKRfast_shape.size() == 2Only works on matrices with two dimensions.Only works on matrices with three dimensions.fast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape[0] == output.Shape().Size()onnxruntime::ValidateFastReduceRKRfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKfast_shape.size() == 3onnxruntime::ValidateCommonFastReduceonnxruntime::ValidateNoTransposeReducecount == 1Reduction on all axes, output size should be 1.onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseselect_last_indexC:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()U@ `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_?Divide by zeroSafeIntExceptionHandler::SafeIntOnDivZeroonesidedinverseUnsupported 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.onnxruntime::DFT::Computeonnxruntime::STFT::ComputeUnsupported 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.dft_length must be a scalar value.onnxruntime::discrete_fourier_transformC:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.ccdft_length must be greater than zero.DFTSTFTratio input should have a single value.tensor->Shape().Size() == 1onnxruntime::signal::get_scalar_value_from_tensorC:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.hframe_length == window_lengthEnsure that the dft size is smaller than the signal.window_size < signal_sizesignal shape must end in 1 (real) or 2 (real, imaginary).signal_components == 1 || signal_components == 2onnxruntime::short_time_fourier_transformIf both frame_length and window are set, then the size of the window must be equal to the frame_length.Unsupported bit size.onnxruntime::bit_reverseonnxruntime::dft_bluestein_z_chirpT3MelWeightMatrixHannWindowHammingWindowBlackmanWindowoutput_datatypeperiodiclowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.ccupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Compute'is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeNo attribute with name:'Attribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::ElementWiseRangedTransform::CreateC:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.ccunknown kernel typeonnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::InitC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::InitC:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::GridSample::GridSamplemode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"borderC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.hbicubicpadding_modemode "" not supported, expect bilinear, nearest or bicubic" not supported, expect zeros, border or reflectionreflectionpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"padding_mode " does not match input batch size Only 4-D tensor is supportedgrid_dims[0] == NGrid batch size grid_dims[3] == 2zerosbilinearC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cconnxruntime::GridSample::ComputeLast dimension of grid: , expect 2Sampling ratio should be >=0, but it was sampling_ratiospatial_scalesampling_ratio_ >= 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBaseoutput_widthoutput_heightand 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 maxNumber 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 Null input X ptrRoiAlignNull batch_indices_ptrNull rois_ptrbad allocationInvalid input scale: NumDimensions() != dimension != Invalid input scale: 0th dimension != !is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))Invalid number of outputs for BN trainingonnxruntime::BatchNorm::ComputeInvalid input mean: 0th dimension != Invalid input mean: NumDimensions() != Invalid input var: NumDimensions() != Invalid input mean: Invalid input B: NumDimensions() != Invalid input scale: Invalid input B: Invalid input B: 0th dimension != C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::ComputeInvalid input var: Invalid input var: 0th dimension != onnxruntime::BatchNorm::BatchNormTraining mode only supports spatial BNmomentumonnxruntime::BatchNorm::BatchNormis_spatial_training_modeforwardonnxruntime::RNN::ComputeRNNbidirectionalreverseAffineC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cclayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNactivation_alphaallowed_directions.find(direction_) != allowed_directions.end()clipinfo.GetAttr("direction", &direction_).IsOK()layoutactivations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - activationsactivation_betahidden_sizeinfo.GetAttrs("activations", activations_).IsOK()onnxruntime::DeepCpuLstmOp::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::PrePackInvalid data type for LSTM operator of LSTM operator does not support double yetC:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::MakeDirectiononnxruntime::LSTMBase::LSTMBaseinfo.GetAttr("direction", &direction).IsOK()Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.tanhsigmoidLSTMactivation_func_names.size() == static_cast(num_directions_) * 3info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hinput_forgetclip_ > 0.fGRUactivation_func_names.size() == static_cast(num_directions_) * 2Invalid data type for GRU operator of GRU operator does not support double yetC:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeCan not get unpacked span from prepacked weightsonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_onnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= Noffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointeronnxruntime::rnn::detail::SafeRawPointerB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endcur + size <= endC + (M * ldc - (ldc - N)) <= C_endC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccInput tensor should have a rank of at least 2IsScalarOr1ElementVector(k)onnxruntime::Trilu::ComputeTriluk should be a 1-D or 0-D tensor.onnxruntime::Trilu::TriluC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hupperinfo.GetAttr("upper", &temp).IsOK()Axis tensor should be of type `int32_t` or `int64_t`CumSumAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1Dexclusiveonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::Computestarts.size()=C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computedims.size() == steps.size()onnxruntime::WritableSliceIterator::Initdims.size() == extents_.size()steps.size()=onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::InitC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13. N=Hardmax inputs N, D and N * D must be < C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=onnxruntime::Hardmax::ComputeindicesGatherNDBase PrepareForCompute: Input count mismatchbatch_dimsGatherNDC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor data type not supportedinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::Computeindices tensor must has rank larger than 0invalid index found, index = onnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!X input is required!onnxruntime::NonZero::ComputeNull input ptrinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.blocksizeDCRCRDDepthToSpace requires input depth to be a multiple of (block_size * blok_size)SpaceToDepth requires input width to be a multiple of block_sizeSpaceToDepth requires input height to be a multiple of block_sizeSpaceDepth ops require a 4-D input. Provided rank: C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseUnsupported input type in DepthToSpace op: onnxruntime::DepthToSpace::ComputeUnsupported input type in SpaceToDepth op: C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::Computeonnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedGatherElementsC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElements op: Out of range value in index tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_implGatherElements op: Unsupported tensor type, size:onnxruntime::GetIndexC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::ComputeTile doesn't have an implementation yet for the type: 'repeat' input tensor must have the same length as the 'input' tensor'repeat' input tensor must be 1 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyonnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1Dimension of input must be 1 instead of . shape=onnxruntime::Squeeze::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attribute'step' value cannot be 0'axes' has duplicates'axes' has an axis outside of the tensor dimension count!has_axes || attr_axes_.size() == attr_starts_.size()C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of Cannot slice scalarsData type for starts and ends inputs' is not supported in this build. Got Starts and steps shape mismatchStarts and axes shape mismatchStarts and ends shape mismatchEnds must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayonnxruntime::SliceBase::Computeonnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()input != nullptrC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputenew_axisC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ComputeImplData type mismatchNon concat axis dimensions must match: Axis has mismatched dimensions of input_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: got: Cannot concatenate scalarsonnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attribute0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1onnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shapeseedC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\mean_variance_normalization.ccnormalize_varianceNo valid axes are specified. This is not handled now.onnxruntime::MeanVarianceNormalization::ComputeMeanVarianceNormalizationacross_channelsX and Y must both have M * N elements.onnxruntime::`anonymous-namespace'::ComputeMeanVarianceNormalization2DX->Shape().NumDimensions() == 4onnxruntime::LRN::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccsizeinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRNC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fbiasThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Computeonnxruntime::Flatten::FlattenC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hinfo.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transB", &temp).IsOK() N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2left[i] >= 0right[i] >= 0c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasonnxruntime::MatMul::ComputeTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMulComputeHelper::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::Clip::ComputeImpl::operator ()max should be a scalar.max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.hinvalid expand shapeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cconnxruntime::Einsum::DeviceComputeEinsum op: An implementation for the input type is not supported yetMissing 'equation' attributeequationonnxruntime::Einsum::Einsuminfo.GetAttr("equation", &equation_).IsOK()EinsumC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one input->Invalid argument for depth; it's not a scalar.OneHotInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeDepth is negative.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()kC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0onnxruntime::TopkOpset11ConstructorCommononnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()sortedTopKoutput count mismatch, expected 2 outputs to be present for TopK operatork argument [] should not be greater than specified axis dim value [input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueinput count mismatch, expected 1 input - the tensor to be processedvalue of k must not be negativek tensor should be a 1D tensor of size 1Unsupported tensor type of UniqueData type is not supported in this build.limit in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!onnxruntime::SequenceEmpty::ComputeSequenceInsertSequenceEmptyUnsupported 'dtype' value: Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceEraseonnxruntime::CloneTensor), input tensor data type (SequenceAtonnxruntime::GetSeqIdxSSequenceLengthInvalid sequence index (onnxruntime::SequenceAt::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cc) specified for sequence of size (onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputsplit_size_sum (Invalid data type for split tensor onnxruntime::SequenceConstruct::ComputeViolation of the requirment that all input tensors must have the same data type.SequenceConstructnum_inputs >= 1SplitToSequence operator does not support ) != split_dim_size (SplitToSequence yetstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be >= 0Split should be > 0split_scalar > 0Got nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeConcatFromSequenceC:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccoutput_shapegroup count is <= 0output_paddingfilter number not equal to input channel number.Input channels is not divisible by group. num_input_channels: filter_number: Invalid input shape: X: X num_dims does not match W num_dims. channels_last: kernel_shape is not compatible with W shape. group: auto_pad == AutoPadType::NOTSETonnxruntime::ConvAttributes::ConvAttributesA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes kernel_shape: kernel_shape num_dims is not compatible with W num_dims.C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: onnxruntime::ConvTranspose::DoConvTransposeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccdim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapeonnxruntime::ConvTransposeAttributes::PrepareForComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.h*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.output and sum shape must matchonnxruntime::Conv::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.ccNot enough elements in pads. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeInput channels C is not equal to kernel channels * group. M: kernel channels: C: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Output channels M is not divisible by group.Not enough elements in strides. Expected: pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0info.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.onnxruntime::MaxUnpool::Computeoutput_shape is smaller than minimum required. output_shape:Shape must be 1 dimensional as it's tensor data of a shape inferred output shape:DetMatrix dimensions are not equal. Square matrix is expectedonnxruntime::Det::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccCompressC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()onnxruntime::Compress::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrbatch_axis != time_axistime_axis and batch_axis must have different values but both are time_axis < 2Invalid time_axis of time_axisC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axissequence_lens shape must be {batch_size}. Got:. batch_size=ReverseSequenceC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of Invalid sequence length: . Value must be in range [0,Failed to obtain detect_negativedetect_negativeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfFailed to obtain detect_positivedetect_positiveT4W_zero_point_data[i] == W_zero_point_valueQLinearConv : zero point of per-channel filter must be sameIsValidQuantParam(W_zero_point, M)QLinearConv : filter zero point shape invalidonnxruntime::QLinearConv::ComputeOffsetonnxruntime::QLinearConv::ComputeOutputScaleQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsValidQuantParam(W_scale, M)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOffsetIsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersprepacked_buffers[0].get() == nullptrQLinearConv : result scale must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.cconnxruntime::QLinearConv::ComputeNon per-tensor quantization is not supported now.C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)Must be a scalar or 1D tensor or size 1.ConvIntegerMatmulInteger : B zero point is not validC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.cconnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(a_zero_point)IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1onnxruntime::MatMulComputeHelper::Compute::::operator ()Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.ccQLinearMatmul : 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_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)onnxruntime::QLinearMatMul::Compute*localestopwordsEmpty stopwords not allowed!sw.empty()LOWERUPPERNONEattribute case_change_action has invalid valueattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizercase_change_actionattribute case_change_action is not setonnxruntime::string_normalizer::Locale::LocaleC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USis_case_sensitiveConversion ErrorConversion Error:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:StringNormalizerbad conversionSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 charsDuplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_error Output dim value: Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::UpsampleBase::ParseSizesDataaxes should be less than output_dims.size()Resize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.Input dim is zero but required output dim is non-zero. onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeResize: input/output value is nullptrUpsample: input/output value is nullptronnxruntime::Upsample::BaseComputeonnxruntime::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 modeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeUpsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeResize: input tensor's dimension does not match the scales.Either scales or sizes MUST be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeInvalid roi input index.onnxruntime::Upsample::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.onnxruntime::UpsampleNearest >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0max_gram_length is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNonemin_gram_lengthC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDFTFIDFTfIdfVectorizermode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizern-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()-gramsimpl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specified!pool_strings.empty()weights must be of equal size but ngram_indexes size: Got weights of size: Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= min_gram_length must be inbounds of ngram_counts: max_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0ngram_counts id: Input shape must have either [C] or [B,C] dimensions with B > 0.Duplicate ngram detected, size: p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGramsC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()op_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkEyeLikeEyeLike : Input tensor dimension is not 2C:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: Must have a valid input shape.onnxruntime::ConstantOfShapeBase >::PrepareComputeonnxruntime::`anonymous-namespace'::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: Must have a single dimension of 1t_proto_p->dims()[0] == 1C:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensiont_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoScan<8> spec does not support transpose of output. This should never be called.position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::ExecuteC:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueonnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Compute but has batch size of C:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Initonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::Computeroi_batch_id < batch_sizeroi_batch_id >= 0R->Shape()[1] == 5MaxRoiPoolpooled_width_ > 0pooled_height_ > 0spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()onnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shapeLpNormalizationonnxruntime::LpNorm::LpNormC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormp_ == 1 || p_ == 2op_kernel_info.GetAttr("p", &p_).IsOK()op_kernel_info.GetAttr("axis", &axis_).IsOK()Mismatch between input data and B: size of B != input channel count Invalid input B: number of dimensions is not 1: Invalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: Mismatch between input data and scale: size of scale != input channel count vs. C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("low", &low_).IsOK()lowsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikehighonnxruntime::RandomNormalLike::RandomNormalLikeonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()Invalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDC:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("scale", &scale_).IsOK()scaleinfo.GetAttr("mean", &mean_).IsOK()meanOutput type not supported in this build: num_classes is < 1batch_size is < 1Invalid data type of num_samples is < 1Could not infer data type from input tensor with data type C:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::Computeonnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsBRANCH_LTLEAFBRANCH_GTBRANCH_GTEBRANCH_LEQTreeEnsembleRegressorSUMAVERAGEMINLOGISTICBRANCH_EQSOFTMAX_ZEROSOFTMAXnodes_missing_value_tracks_truenodes_hitratesnodes_nodeidsnodes_modesbase_valuesInput shape needs to be at least a single dimension.nodes_featureidsnodes_falsenodeidsonnxruntime::ml::detail::TreeEnsembleCommon::computenodes_falsenodeids.size() == nodes_featureids.size()n_targets_or_classes > 0Unknown aggregation function in TreeEnsemble.aggregate_functiononnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::computetarget_weightstarget_weights_as_tensorn_targetspost_transformbase_values_as_tensor-C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccclass_weights_as_tensornodes_values_as_tensortarget_nodeidstarget_treeidsnodes_hitrates_as_tensortarget_weightsbase_values_as_tensornodes_truenodeidsnodes_treeidstarget_idsnodes_valuesOne path in the graph requests feature but input tensor has predictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggTreeEnsemble only works on 1D, 2D tensors.falsenode_ids[i] != i features.onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg (weights). (falsenode).truenode_ids[i] != iTarget is missing for leaf (truenode).(coor.node_id >= 0 && coor.node_id < n_nodes_)A node cannot point to itself: Unable to find node is already there.nodes_modes.size() < std::numeric_limits::max()Node in tree nodes_values.empty() || nodes_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()base_values.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()base_values.empty() || base_values_as_tensor.empty()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionit->i < (int64_t)predictions.size()C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMax::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionTreeEnsembleClassifierclasslabels_int64sbase_values_as_tensorclass_weightsclasslabels_stringsclass_treeidsclass_nodeidsX dims is empty.class_idsonnxruntime::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::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclasslabels_int64sonnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictionkeys_int64svalues_int64svalues_floatsdefault_float_Unusedkeys_stringskeys_floatsvalues_stringsInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)info.GetAttr("default_int64", &default_int_).IsOK()LabelEncoderinfo.GetAttr("default_string", &default_string_).IsOK()default_int64C:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_stringinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderclasses_stringsThe num_keys == num_values(name: attribtues in LabelEncoder However, the number of key is ) must have the same length. values is and the number of onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_value_field_name, values).IsOK()info.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2Input features_per_batch[Zipmap only supports 1D or 2D input tensors] != number of classlabels[C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpZipMapMust provide classlabels_strings or classlabels_int64s but not both.RBFLINEARPOLYC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hSVMRegressorinfo.GetAttrs("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonkernel_typekernel_paramsone_classinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()info.GetAttrs("rho", rho_).IsOK()coefficientsonnxruntime::ml::SVMRegressor::SVMRegressorrhon_supportsinfo.GetAttr("n_supports", &vector_count).IsOK()support_vectorsC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccUnexpected mode:Invalid argumentonnxruntime::ml::SVMRegressor::Computeonnxruntime::ml::SVMClassifier::ComputeUnsupported data type of classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()prob_bonnxruntime::ml::SVMClassifier::SVMClassifiervectors_per_classprob_aSVMClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hscores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of ScalerScale size: (scale_.size() == offset_.size()C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (!scale_.empty()onnxruntime::ml::ScalerOp::ScalerOpoffsetEmpty scale in attributes) or 1Either both scale and offset can be of feature size (Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOpUnknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64sNormalizerC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1MAXC:\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 interceptsLinearRegressorInput shape had more than 2 dimension. Dims=C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()targetsmulti_classLinearClassifierUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplscores_output_data.size() >= scores_output_sizeScores output is incorrect size. Expected: Found:C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifieronnxruntime::ml::CastInputToFloatshape_size == out.size()onnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerEmpty input dimensions.Empty value of imputed values.Invalid typestatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsInvalid input type:C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (FeatureVectorizerC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerDictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularystring_vocabularyinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string stof argument out of range%finvalid stof argumentNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapInvalid input type of value: 0.fUnexpected CAST_TO value of Binarizer is NaNInput data with index: thresholdArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[ inputs but Scan was only given Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::InfoC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'directions.size() == num_entriesonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'valid did not.Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::AllocateOutputonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::InitializeFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Unsupported typeop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LayerNormImpl::LayerNormImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cc and bias size of . Size of scale and bias (if provided) must match this. Got scale size of Size of X.shape()[axis:] == onnxruntime::`anonymous-namespace'::ComputeImplInput R must have shape {Input W must have shape {Input sequence_lens must have shape {Input B must have shape {Input initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=reluaffinethresholdedreluleakyreluhardsigmoidscaledtanhsoftsigneluExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountweights.quant_para_A + (M * K) <= A_endalpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fonnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU hidden gate activation function: }. Actual:Input X must have 3 dimensions only. Actual:Input P must have shape {Input initial_c must have shape {onnxruntime::LSTMBase::ComputeImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < The override dims are not compatible with given tensor's shape. Left shape: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessleft.Shape().Size() == left_shape_override.Size() Right shape override: C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.ccright.Shape().Size() == right_shape_override.Size()Right shape: Ranks of pair-wise operands must be equal. Right shape: Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_rank == right_rankEinsum op: Input shapes do not alignleft_dim == right_dimcandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: candidate_output_dims[iter] == 1onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcess Left shape override: onnxruntime::EinsumComputePreprocessor::RunNumber of subscripts in the input equation does not match number of input tensorsC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccEinsum subscripts string contains too many subscript labels when compared to the rank of the inputFound a '.' not part of an ellipsis in input: Found '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputs in the same dimensionThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z). The shape is: Another operand has a dim value of Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Einsum subscripts string contains too many subscript labels when compared to the rank of the input onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsnum_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rankThe broadcasted dimensions of the inputs are incompatibleInputs have ellipses in them but the provided output subscript does not contain an ellipsis...Found '.' not part of an ellipsis in the output subscript providedFound a '.' not part of an ellipsis in the output subscript providedOutput subscript contains letters not seen in the inputsOutput subscript contains repeated lettersRank of the input must match number of subscript labels corresponding to the inputEinsum op: The candidate output does not match the actual output's shapeData types of the inputs must match for MatMulonnxruntime::EinsumOp::MatMulinput_1.DataType() == input_2.DataType()input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Only 1 batch dimension is allowed for MatMulinput_shape_1_override[0] == input_shape_2_override[0]Batch dimension should match for MatMul;input_shape_1_override[2] == input_shape_2_override[1]Incompatible matrix dimensions for matMulEinsum op: Exception during MatMul operation: onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyoutput.SizeInBytes() == input.SizeInBytes()The innermost dims should have the same dim value to parse the diagonal elementsC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsinput_dims[rank - 2] == input_dims[rank - 1] for input shape Einsum op: Unsupported data type for Diagonal rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Cannot parse the diagonal elements along dims The rank of the input must match permutation size for Transposeonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalonnxruntime::EinsumOp::IsTransposeRequiredinput_rank == permutation.size()onnxruntime::EinsumOp::TransposeLength of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: Inverseonnxruntime::contrib::SkipLayerNorm::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::Computeepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNorminput is expected to have 3 or 2 dimensions, got last two dimensions of skip needs to be same as inputskip is expected to have 3 or 2 dimensions, got skip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchScaleonnxruntime::contrib::Scale::ScaleC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("beta", &beta_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::contrib::ImageScaler::ImageScalerC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()ImageScalerBias size (Input is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (DynamicSlice) + bottomBorder () needs to be greater than or equal to the topBorder (Attribute border needs to be specified with four border elements, got input dimensions instead) needs to be greater than or equal to the leftBorder (Input's width (Input's height () + rightBorder (Croponnxruntime::contrib::Crop::Compute) + scale_[0] () + scale_[1] (C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.hinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_sizemax_ngram_sizemin_ngram_size_ > 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.honnxruntime::contrib::BifurcationDetector::BifurcationDetectorsrc_tokens_len >= prev_suffix_match_idx_datamax_ngram_size_ >= min_ngram_size_max_ngram_size_ > 0info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()BifurcationDetectorpred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)onnxruntime::contrib::BifurcationDetector::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.honnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockinfo.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_sizeinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()token_id < vocab_sizengram_size_ > 0Tidscores_dims[0] == batch_sizescores_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::ComputeNGramRepeatBlockuse_approximationC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::ComputeThe second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: onnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()metricInput shape dimensions mismatch:onnxruntime::contrib::CDist::CDisteuclideansqeuclideanC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hCDistCropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskblk_quant_typeRight hand side of MatMulFpQ4 must be a 2D matrix!b_shape.NumDimensions() == 2Operator MatMulFpQ4 not yet supported on this hardware platform.buf_size > 0Second input of MatMulFpQ4 must be a 1D blob!blob_shape.NumDimensions() == 1onnxruntime::contrib::MatMulFpQ4::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_fpq4.ccQuantized and packed blob size differ from expected!(size_t)blob_len == buf_sizeMatMulFpQ4TransposeMatMulsizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0keysonnxruntime::contrib::MurmurHash3::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccInvalid assumption of output element sizepositiveMurmurHash3COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator () is out of bounds of lhs_right: SparseToDenseMatMulCurrently supporting only 2-D matricesonnxruntime::contrib::SparseToDenseMatMul::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccExpecting the same number NNZ == size of Inner indicesOuter size must be M + 1Currently support only COO and CSR(x64) formats vs inner_B: Can not multiply A and B as inner dimension does not match. inner_A: Expecting COO 2-D indices shapeExpecting 2xValues == indicesonnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO k index: is out of bounds of out_left: COO m index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()embedding_sizeconv_window_sizechar_embedding_size conv filter size: Conv kernal size 1 does not match conv_window_size attribute . Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. embedding_size attribute: Conv filter size does not match embedding_size attribute. conv kernal size 1: conv_window_size attribute: WordConvEmbedding Conv kernal size 2 : Char embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueTokenizermarkmincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valuemincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Expecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()Input dimensions are either [C] or [N][C] allowedInput string contains invalid utf8 charsInput string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp}, actural: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.ccAttention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {static_cast(activation_func_names.size()) == num_directions_ * 3C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTM}, Got: Attention memory layer weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{], while found!, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImpl, aw_attn_size}. Got:Attention v weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {init_run_decoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_past_present_share_buffer mode must be same for init decoder and decoder subgraphesdecoder_session_stateSubgraph SessionState was not found for 'decoder' attribute.decoder_feeds_fetches_manager_onnxruntime::contrib::transformers::Sampling::Computeonnxruntime::contrib::transformers::Sampling::Initparameters_.model_type == IGenerationParameters::kModelTypeGptencoderC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.ccNode input should be a scalar. Got shape of Sampling is requiredonnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfogpt_subgraph_ == nullptrNot Implementedinit_run_gpt_subgraph_ == nullptrinit_decoderinfo.GetAttr("encoder", &proto).IsOK()info.GetAttr("decoder", &proto).IsOK()decoderInput 'presence_mask' shape[1] shall be vocab_size, got input_ids and presence_mask must have the same batch_sizeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchGpt::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.honnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializemin_lengthmax_lengthonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsInput 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'input_ids' is expected to have 2 dimensions, got onnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenInput 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'presence_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'prefix_vocab_mask' is expected to be 2 dimensions, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got input_ids and prefix_vocab_mask must have the same batch_sizepast_present_share_bufferqkv_hidden_sizesonnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0do_rotaryC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hMultiHeadAttentionInput 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_key' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length)Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 0 should be batch_size, got Input 'query' and 'key' shall have same dim 0 (batch size)Input 'key' is expected to have 3, 4, or 5 dimensions, got Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvInput 'query' and 'key' shall have same dim 2 (hidden_size)past_sequence_length tensor must be of one element when past_present_share_buffer is setInput 'past_value' dimension 3 should be same as head_size, got Input 'query' is expected to have 3 dimensions when key is given, got Input 'past_key' and 'past_value' shall be both present or both absent'bias' is not allowed for packed kv. Input 'bias' is expected to have 1 dimension, got Input 'value' is expected to have 3 or 4 dimensions, got Input 'key_padding_mask' shape shall be (batch_size) or (batch_size, kv_sequence_length)Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyExpect 'value' be none when 'key' has packed kv format.Expect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kvInput 'query' is expected to have 3 or 5 dimensions when key is empty, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 0 should be batch_size or 1, got Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'query' and 'value' shall have same dim 0 (batch_size)Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Packed KV not implemented for CPUPacked QKV of shape (B, L, N, 3, H) not implemented for CPUInput 'query' is expected to have 3 or 5 dimensions, got onnxruntime::contrib::MultiHeadAttention::MultiHeadAttentionC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.cconnxruntime::contrib::MultiHeadAttention::Compute4D mask in attention cpu kernel is not supportedonnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AttentionCPUBase::ApplyAttentiononnxruntime::contrib::MaybeTransposeToBNSHAndAddBiasonnxruntime::contrib::AddBiasReshapeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.cconnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearch::InitGreedySearchonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK() Axis is C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.hAxis must be within range [].An axis tensor must be a scalar tensor.onnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()ExpandDimsaxis <= X_NumDims && axis >= -X_NumDimsonnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccencoder_feeds_fetches_manager_Model type is not supported.Subgraph SessionState was not found for 'encoder' attribute.encoder_session_statewhisper_decoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::Computeparameters_.model_type == IGenerationParameters::kModelTypeGpt || parameters_.model_type == IGenerationParameters::kModelTypeT5 || parameters_.model_type == IGenerationParameters::kModelTypeWhisperonnxruntime::contrib::transformers::BeamSearch::InitBeamSearchwhisper_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)Encoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::BeamSearchGpt::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchGpt::Executelength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.num_beamsnum_return_sequencesonnxruntime::contrib::transformers::BeamSearchBase::InitializeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.honnxruntime::contrib::transformers::BeamSearchWhisper::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_whisper.hC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Executeparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchT5::Executeonnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchWhisper::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::Attention::ComputeSampleOponnxruntime::contrib::QGemm::CheckInputsQGemm : zero point of input a must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::ComputeTBTCQGemm : scale of y must be null or a scalar or 1D tensor of size 1TAQGemm : zero point and scale of input b should have same shape sizeQGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NTYZTYGamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QEmbedLayerNorm::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.ccinput_shape.Size() > 0 || N == 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cconnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonInput RecurrentWeight point must be constantDynamicQuantizeLSTM : Weight zero point must be zeroR_scaleW_scaleR_zero_pointW_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePackinput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.cconnxruntime::contrib::QAttention::ComputeQLinearMulQLinearAddtensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImpltensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1QLinearSoftmaxonnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13onnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxopset must be existed in attributes of QlinearSoftmaxC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.cconnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedQLinearSigmoidQLinearLeakyReluinput x_zero_point must be a scalar or 1D tensor of size 1 if giventensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if giventensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinear Pooling unsupported pooling size!Input x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearAveragePool::ComputeImplC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.cc inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)There must be input_def_count == kExpected_input_countonnxruntime::contrib::QLinearWhere::QLinearWhereC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.cctensor_z_scale->IsDataType()Wrong input type encountered for zero point of quantized input @QLinearWhereWrong input type encountered for zero point input def of x, y, ztensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Input scale is not float for quantized input x @ 2tensor_x_scale->IsDataType()onnxruntime::contrib::QLinearWhere::ComputeInput scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7 onnxruntime::contrib::QLinearConcat::QLinearConcatC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.ccInput scale is not float for input def @Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()input_count_x3 >= 3 && input_count_x3 % 3 == 0onnxruntime::contrib::QLinearConcat::ComputeInput scale is not float for quantized input @Each input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0QLinearConcatC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::contrib::QLinearGlobalAveragePool::ComputeMatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.cconnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countchannels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsampleinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderInput::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'X_rank == 4Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Computeonnxruntime::ml::GetVectorAttrsOrDefault' is specified but is empty.Attribute:'' must be a vector.Attribute 'n_dims == 1onnxruntime::ml::GetNumberOfElementsAttrsOrDefaultC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cc'.(for attribute 'Unexpected type (proto.data_type() == proto_type' has one dimension but is empty.n_elements > 0Input 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 row[ [seqno= rows[..onnxruntime::contrib::BahdanauAttention::PrepareMemorynot support normalize yet.onnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_ is not in (0, C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps prefix.size() == narrow(prefix_length)C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.cconnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processfilter_valuetop_ppresence_penaltymin_tokens_to_keepvocab_sizecustomseed >= 0Seed must be >= 0eos_token_idmodel_typedecoder_start_token_idpad_token_idtemperatureno_repeat_ngram_sizeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.cconnxruntime::contrib::transformers::SamplingParameters::ParseFromInputssubgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph output 0 (logits) shall be float or float16 data typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph input 1 shall be named as present_0, got: subgraph logits output is expected to have 3 dimension, got subgraph output 0 shall be named as logits, got: present_0subgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typesubgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph input 0 (input_ids) shall have int32 typeSetup must be called before CreateInitialFeedsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.ccinput_ids_shape.NumDimensions() == 2session_state_ != nullptronnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedssubgraph input 3 shall be named as past_0, got: subgraph past state is expected to have 5 dimension, got subgraph input 2 shall be named as attention_mask, got: past_0subgraph past state dimension 4 shall have a positive value for hidden size per headlogitssubgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headsInvalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)input_idsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).onnxruntime::contrib::transformers::GptSubgraph::Validatesubgraph input 1 shall be named as position_ids, got: attention_masksubgraph input 0 shall be named as input_ids, got: position_idsBeamSearch op: An implementation for the input type onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsis_kv_cacheinput_features_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferoriginal_decoder_input_ids_shape.NumDimensions() == 2onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsstart_token_id >= 0element_type == DataTypeImpl::GetType()onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitslast_outputs.size() >= static_cast(1) + num_present_tensorslogits_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::SamplingCpuHelper::SampleC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.hC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elements v_hidden_size= k_hidden_size=Hidden size of Q, K and V shall be samebias_dims[0]=Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Attention cannot have both past and relative_position_biasInput 'input' is expected to have 3 dimensions, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2when past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInput 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got q_hidden_size=Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Expect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.ccDecoderMaskedSelfAttentionbeam_widthonnxruntime::contrib::transformers::Subgraph::SetupDecoderMaskedMultiHeadAttentionpast_sequence_lengthsubgraph output present_key_self_0 is expected to have 4 dimension, got C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.cconnxruntime::contrib::transformers::Subgraph::GetParametersinput_ids shall have 2 dimensions. Got dims.size() == 2onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.ccmax_length > sequence_length) shall be no more than ) shall be greater than input sequence length (max_length (context != nullptrrepetition_penalty > 0.0fmax_length <= kMaxSequenceLengthrepetition_penalty shall be greater than 0, got @onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsinput_features shall have 3 dimensions. Got early_stoppingdecoder_dims.size() == 2num_beams shall be a positive integer no more than dims.size() == 3decoder_input_ids shall have 2 dimensions. Got C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccnum_return_sequences >= 1) shall be be no more than num_beams (num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got logits_processor shall be a non-negative integer, got logits_processor >= 0num_return_sequences (num_beams >= num_return_sequencesunimplemented activation: activation_params count mismatchinput_ids and position_ids shall have same sequence_lengthbeta is expected to have 1 dimensions, got gamma is expected to have size of gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have size of input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapeposition_ids's first dimension shall be 1 or batch_sizeword_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got output_sequences != nullptrstatic_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()beam_idx == num_beams_beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)onnxruntime::contrib::transformers::BeamSearchScorer::FinalizeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamHypotheses::Outputtop_k <= beams_used_Batch can only be done if all beams have been generatednext_scores.size() == next_indices.size()onnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()decoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph output 0 shall be named as logits, got: Invalid hidden_states expension: has_hidden_state_ == falseonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesdecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesnumber of outputs expected to be 1 + 2 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers, got:decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder_attention_maskdecoder subgraph input 0 shall be named as input_ids, got: encoder_hidden_statesdecoder_masked_attention shall use with past_present_share_bufferC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.cconnxruntime::contrib::transformers::T5DecoderSubgraph::ValidateC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_decoder.cconnxruntime::contrib::transformers::WhisperDecoderSubgraph::Validateonnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeedsdecoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typedecoder subgraph input 1 shall be named as encoder_hidden_states, got: cpu_allocator shouldn't be nullptronnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeedsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.cconnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validateexpect 2 inputs, got:encoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0encoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder_input_idsnumber of outputs expected to be 2 + 4 * layers, got:expect >=6 outputs, got:encoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsexpect 3 inputs, got:encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 shall be named as encoder_attention_mask, got: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.cconnxruntime::contrib::transformers::T5EncoderSubgraph::Validateonnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeQlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1OrtTypeInfo::FromOrtValuetensor_data_type != nullptrOrtValue is TensorSequence type but has no element Tensor DataType.This TypeProto does not have ValueCase setThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeTensor types should have been handled alreadyC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccThe type is not tensor, sparse tensor, sequence, map or optional typeOrtTypeInfo::FromTypeProtoDMLHipCudaCudaPinnedHipPinnedOpenVINO_GPUSpecified device is not supported.onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\allocator.cc`anonymous-namespace'::GetIndicesTensorArgument is not a tensorOrtApis::GetTensorTypeAndShapeC:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccUnsupported indices_format passedTensor type is undefinedthe ort_value must contain a constructed tensor or sparse tensortype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeOrtSequenceTypeInfo::FromTypeProtoC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.ccC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.cctype_proto is not of optional typevalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypeOrtOptionalTypeInfo::FromTypeProtoOrtMapTypeInfo::FromTypeProtoC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypeonnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrp_type != nullptronnxruntime::Tensor::Tensortensor size overflowonnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::CalculateTensorStorageSizeC:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0tensor failed memory size calculationStrided tensor is supported for training only for now.strides.empty()onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsC:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopyTensoronnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())C:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeonnxruntime::data_types_internal::IsCompatibleonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::data_types_internal::MapTypeHelper::SetC:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::data_types_internal::SequenceTypeHelper::Setexpected a registered ONNX typevalue_proto != nullptronnxruntime::utils::ContainerChecker::ContainerCheckersparse tensor type is not currently registered or supportedMLDataType for: Invalid DataTypeImpl TypeProto definition(unknown type) is not supportedtensor type onnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainer(null)floatonnxruntime::NonTensorTypeBase::IsSequenceCompatibleNot implementedutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kOptionalTypeonnxruntime::OptionalTypeBase::IsCompatibleFloat8E4M3FNFloat8E4M3FNUZFloat8E5M2Float8E5M2FNUZint64uint64float16bfloat16int16uint16int32uint32booldoubleint8uint8 must be less than total buffer size: Values size onnxruntime::SparseTensor::AllocateBufferSparseTensor Allocation failed for size: C:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.ccthe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValuethis tensor already has populated sparse_indices dst_size: Must have the same size. Got src_size: onnxruntime::`anonymous-namespace'::CopyData to be equal to values blocks: Expecting index blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}Expecting to have at lest 3-D shape. Got:onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2Must contain BlockSparse format. Got: Format() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::AsBlockSparseExpecting one index. Got: onnxruntime::SparseTensor::UseCsrIndicesUse MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataonnxruntime::SparseTensor::MakeCsrStringsMust have the same shapeDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same type to device type: Unable to find a data transfer for copying from device type: onnxruntime::SparseTensor::CopyThis instance should not be emptyonnxruntime::SparseTensor::UseBlockSparseIndicesUse MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataonnxruntime::SparseTensor::MakeBlockSparseStringsUse MakeCooStringsonnxruntime::SparseTensor::MakeCooDataExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooStringsonnxruntime::SparseTensor::GetCooIndexDimsSparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesNot expecting an allocator setformat_data_.size() == 1U must be equal to or twice the values size: Index size: values_count == index_sizeMust contain Coo format. Got: Format() == SparseFormat::kCooonnxruntime::SparseTensor::AsCooExpecting to contain one index, got: Expecting inner index size: rows: Outer index count must be rows + 1 or zero. Got: This method does not expect allocator to be setdense shape must 2-D. Got: onnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zero the same as values size: Format() == SparseFormat::kCsrconnxruntime::SparseTensor::AsCsrExpecting two indices. Got: format_data_.size() == 2UFormat() == SparseFormat::kUndefinedThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptrMust contain Csr format. Contains: onnxruntime::TensorShape::SizeToDimensiondimension <= num_dimsonnxruntime::TensorShape::SizeFromDimensionC:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cc dimensions.Invalid dimension of for SizeFromDimension. Tensor has }dimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.{onnxruntime::TensorShape::SliceConfig with key [] already exists with value [C:\a\_work\1\s\onnxruntime\core\framework\config_options.cconnxruntime::ConfigOptions::AddConfigEntryConfig key is empty or longer than maximum length 128]. It will be overwrittenConfig value is longer than maximum length: Expecting same size spansAn OrtValue for this name has already been added: onnxruntime::SessionOptions::AddExternalInitializersinit_num == values.size()Buffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.C:\a\_work\1\s\onnxruntime\core\framework\session_options.cconnxruntime::SessionOptions::AddInitializerReceived nullptr for OrtValueReceived nullptr for name but is of type: No attribute with name: Attribute: expected to be of type: is defined.Attribute name and type don't matchNo attribute with this name is defined.Requested attribute: is expected to have type: TempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsonnxruntime::OpKernelContext::GetOrCreateOutputMLValueCPU allocator not found. Num args is onnxruntime::OpKernelContext::OutputMLValuearg_num < arg_counts.size()Invalid arg_num of C:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextkernel != nullptrOpKernel was nullframe != nullptrExecution frame was nullonnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_C:\a\_work\1\s\onnxruntime\core/framework/node_index_info.h type: */_ORT_MEM_ADDR_/* can not be writen into Tensor type TensorProtoToTensor() tensor shape mismatch!onnxruntime::utils::TensorProtoToTensorTensorProto type size to read: given file_length: External initializer: offset: onnxruntime::utils::GetExtDataFromTensorProtoutils::HasExternalData(tensor_proto) are out of bounds or can not be read in full.GetFileLength for UnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size(onnxruntime::utils::GetFileContentdata overflowSparse Indices raw data size does not match expected.Unsupported attribute value type of Sparse indices int64 data size does not match expectedonnxruntime::utils::CopySparseDataConstant node: has no data attributes in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProto, Got string tensor can not use pre-allocated bufferonnxruntime::utils::TensorProtoToMLValueThe preallocated buffer is too small. Requires tensor can't contain negative dimsstring tensor can not have raw dataTensorProtoToMLValue() must take a pre-allocated MemBuffer!Initialized tensor with unexpected type: onnxruntime::utils::UnpackTensorWithExternalDataImpl) does not match the data size() in protoTensorProto: external data size mismatch. Computed size: nullptr == p_data`anonymous-namespace'::ReadExternalDataForTensorC:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc`anonymous-namespace'::GetExternalDataInfo, external_data.length: External data type cannot be UNDEFINED or STRING.Tensor does not have external data to read from.UnpackTensor: the pre-allocated size does not match the raw data size, expected Invalid TensorProtoonnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)onnxruntime::utils::UnpackInitializerData data_type: Element_size of: is not supported.Must have a valid data typeUnsupported sparse tensor data type of cur_index == indices_data.end()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()onnxruntime::utils::SparseTensorProtoToDenseTensorProtoInvalid SparseTensor indices. Should be rank 0 or 1. Got:Invalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorSparse indices int32 data size does not match expectedInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorUnsupported type: onnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0C:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cc However the types are incompatible.Op type (Op with name (Kernel not found and type ( domain ( Encountered following errors: ( kernel is not supported in in the supported version rangeKernel found kernel However the types are incompatible. (node_version: node_version: Version mismatch. kernel_end_version: kernel start version: Failed to add kernel for kernel def can't be NULL: Conflicting with a registered kernel with op versions.static_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptronnxruntime::`anonymous-namespace'::MatchKernelDefTypesC:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cc), but the node in the model has the following type (This op has been implemented only for the following types (kernel_info != nullptronnxruntime::GetCpuPreferredNodesonnxruntime::GetCpuPreferredNodes::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.ccCandidate for fallback CPU execution: ORT optimization- Force fallback to CPU execution for node: capable of executing this node because the CPU execution path is deemed faster than overhead involved with execution on other EPs float8e5m2float8e5m2fnuzfloat8e4m3fnfloat8e4m3fnuzmetadef_id_generator_onnxruntime::IExecutionProvider::GenerateMetaDefIdIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatorC:\a\_work\1\s\onnxruntime\core\framework\execution_provider.cconnxruntime::CreateAllocatorC:\a\_work\1\s\onnxruntime\core\framework\allocator_utils.ccReceived invalid value of arena_extend_strategy Expecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()Input must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUUnsupported element size: Support 2-D matrices onlyonnxruntime::sparse_utils::DenseTensorToSparseCsrC:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::DenseTensorToSparseCoo > dense_size: Invalid index: Expecting indices to be equal the number of values or be twice as manyonnxruntime::sparse_utils::SparseCooToDenseTensorouter_num == (rows + 1)Input must be of COO formatonnxruntime::sparse_utils::SparseCsrToDenseTensorOuter indices must be M + 1. Got: onnxruntime::DataTransferManager::CopySparseTensorsonnxruntime::DataTransferManager::CopyTensorsC:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cc to There's no data transfer registered for copying tensors from data_transfer registered is nullptr.Tensor size mismatchSNPEExecutionProvideronnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedUnsupported OrtValue type to copy between device.onnxruntime::utils::FindDeviceForValueonnxruntime::utils::BatchOrCopyMLValueC:\a\_work\1\s\onnxruntime\core\framework\utils.ccFailed to find allocator for device allocator != nullptrinvalid allocator.Unsupported OrtValue type.onnxruntime::utils::ExecuteGraphonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::UpdateWithParentStreamonnxruntime::utils::CopyOneInputAcrossDevices while parent graph node running on device: Subgraph has nodes running on device: feed_streams.size() == num_feeds, this is not supported yet.copy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsInternalTestingExecutionProvider.H', but the existing argument with that formal parameter name has a different formal parameter type string.Kernel type string already exists for formal parameter name '' for node type 'Failed to find args for kernel type string 'If type constraint names are available, '. C:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.ccensure 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.Duplicate entry for kernel type str: Duplicate entry for op id: args is null.args entry is null.kernel_type_str_args entry is null.kernel_type_str is null.op_id is null.kernel_type_str_args is null.op_kernel_type_str_args is null.op_kernel_type_str_args entry is null.onnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasonnxruntime::KernelTypeStrResolver::SaveToOrtFormatOp schema must be available.onnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaonnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()onnxruntime::KernelTypeStrResolver::RegisterOpSchemaonnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStronnxruntime::ExLibLoader::LoadExternalLibA dso with name has already been loaded.Caught exception while loading custom ops with message: Failed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoaderUnloading DSO C:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: +The kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsonnxruntime::KernelUseSharedPrePackedBuffersduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?removed removable attributes '), among attributes: onnxruntime::SessionState::PruneRemovableAttributesfailed at retrieving the removable attributesfor node '' ('').onnxruntime::SessionState::CreateKernelsOnly one node should produce an output. Existing entry for Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node (Unable to write the provided PrePackedWeights instance into the containerUsing cached version of pre-packed weight for constant initializer: used in the node: which is of op type: !op_type.empty()The op type of a node cannot be emptyweights_to_be_filled_in.buffers_.size() > 0 doesn't have an implementation that can cache computed pre-packed weightsonnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()allocator_for_caching.get() != nullptronnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.C:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingsession.node_partition_config_filesession.disable_prepackingp_op_kernelentry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:onnxruntime::VerifyEachNodeIsAssignedToAnEpImplCould not find an implementation for ) node with name 'onnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::GetNodeIndexInfonode_index_info_.has_value()SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty()onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::AccumulateAllNestedSubgraphsInfosubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulatoronnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::SessionState::FinalizeSessionStateRerunning with verbose output on a non-minimal build will show node assignments.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. Node(s) placed on [ All nodes placed on []. Number of nodes: Node placementsonnxruntime::VerifyEachNodeIsAssignedToAnEpstream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamonnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamidx < num_streams_C:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.cconnxruntime::DeviceStreamCollectionImpl::CleanUponnxruntime::DeviceStreamCollectionImpl::GetStreambad variant accessFailed to find kernel for The node is not placed on any Execution Provider. (node found duplicated provider in KernelRegistryManagerC:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypenullptr != type_protoonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolveronnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferFailed to verify KernelTypeStrResolver flatbuffers data.C:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccktsronnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: Error mapping feeds: C:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxs!provider_type_.empty()provider_type must be specified.API VERSION Create state function failed. Return value:C:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::Createonnxruntime::PartitionOrtFormatModelImplonnxruntime::PartitionOnnxFormatModelonnxruntime::InlineNodes did not return correct number of compiled functionsThe Function Style fusion is deprecated.onnxruntime::PartitionOnnxFormatModelImplNode ' with domain: was inserted using the NHWC format as requested by , 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.C:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.C:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.honnxruntime::KernelLookup::KernelLookuponnxruntime::GraphPartitioner::PartitionNo provider specified.onnxruntime::PartitionOrtFormatModelsingle_node_compute_func should have 1 element.x0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::AllocationRegionC:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.hp_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForp_int < base_int + memory_size_Could not find Region for: entry != regions_.end()onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionCould not find Region for onnxruntime::BFCArena::RegionManager::RegionFor arena_extend_strategy: memory limit: max_power_of_two_extend_bytes: initial_growth_chunk_size_bytes: bytes. BFC Arena shrunk by onnxruntime::BFCArena::Shrinkonnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use() && c1->stream == c2->streamonnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesce!c->in_use()tried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternal (actual) rounded_bytes: (requested) num_bytes: . bin_num:Extending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes=!chunk->in_use()onnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunk The total allocated bytes is now onnxruntime::BFCArena::Extend::::operator () is smaller than requested bytes of Available memory of Failed to allocate memory for requested buffer of size bytes.Extended allocation by onnxruntime::BFCArena::ExtendTotal allocated bytes: Allocated memory at size: Reserving memory in BFCArena for onnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()h != kInvalidChunkHandle max_dead_bytes_per_chunk: with following configs: initial_chunk_size_bytes: Creating BFCArena for onnxruntime::BFCArena::BFCArenaC:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc bins of max chunk size Creating BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandlecudaMallochipMallocIncorrect arena extend strategy.onnxruntime::BFCArena::Coalesce. offset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexfeeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains failed. Error:Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative value, fall back to default allocation behavior but the actual size is: Could not find OrtValue with idx 'allocator is nullptrallocator.get() != nullptronnxruntime::AsStreamBasedAllocatorC:\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()onnxruntime::IExecutionFrame::IExecutionFrame Requested shape:OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && sp_tensor.DenseShape() == *shapeinvalid index ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\a\_work\1\s\onnxruntime\core/framework/execution_frame.h does not match actual shape of Expected shape from model of onnxruntime::ExecutionFrame::VerifyOutputSizesonnxruntime::ExecutionFrame::ReleaseMLValueImpl size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework., block in memory pattern size is: For ort_value with index: onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperFailed to get allocator for alloc && alloc.get() != nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer. 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::AllocateReusedOrtValueIfNotAllocatedHelperort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: for output onnxruntime::NodeIndexInfo::Init::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::TransposeSingleAxisOutwardsC:\a\_work\1\s\onnxruntime\core\framework\transpose_helper.cc. Shape:Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::IteratorCan't slice a non-tensor OrtValue. Type was ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer::CreateC:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()onnxruntime::LibraryHandles::UnloadLibrariesC:\a\_work\1\s\onnxruntime\core\framework\library_handles.ccsource and destination buffer size mismatchonnxruntime::utils::detail::CopyLittleEndianC:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccmodel format error! Need a key for the external data infomodel format error! Need a value for the external data infolocation failedparsing lengthchecksummodel format error!model format error! Missing 'location' does not.All implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalC:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.h]}":[{"Begin executiononnxruntime::SessionScope::SessionScopeC:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.ccSequentialExecutor::ExecuteNon-zero status code returned while running launch kernel with idx stream Number of streams: onnxruntime::ExecuteThePlanop_name_fence_beforenode_indexactivation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapethread_scheduling_stats_kernel_time_fence_afterAsync Kernel Support is not implemented yet.onnxruntime::ExecuteKernel' Status Message: node. Name:'onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan!IsNonTensor(*node_output)onnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::BuildExecutionPlanonnxruntime::PlannerImpl::PartitionIntoStreamstrigger_point_it != node_to_trigger_points.end()execution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()DeviceBasedPartitioneronnxruntime::PlannerImpl::CreatePlanFailed to find node "" in node-stream mapfound_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location mapShould not have entry in kernel create info with nullptr for kernel_defonnxruntime::PlannerImpl::ComputeValueLocation::::operator ()onnxruntime::PlannerImpl::ComputeValueLocationp_kernel_defonnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end() as inputonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream reused by Only tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlan against size onnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()invalid value index: onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseonnxruntime::PlannerImpl::ComputeReuseCountCan not find the node : entry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:C:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cconnxruntime::GetKernelCreateInfonull}\u%04x: 0xincomplete UTF-8 string; last byte: 0xinvalid UTF-8 byte at index �cannot use operator[] with a numeric argument with { {}, ": []":"bytes": [[ "subtype": ], ],"subtype":{"bytes":[onnxruntime::DeviceBasedPartitioner::PartitionGraphiter != node_stream_map.end()onnxruntime::DeviceBasedPartitioner::InitializePartitioner type is not DeviceBasedPartitionerdevicesstreamsnode_names_by_stream_.size() == device_types_.size()Number of streams does not equal to number of device types!onnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception during saving DeviceBasedPartitioner config: onnxruntime::IGraphPartitioner::CreateGraphPartitionerCaught exception when reading partition config file: Use DeviceBasedPartition as defaultcannot use insert() with cannot use operator[] with a string argument with 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨ -1OrtValue indexes should have been populated.OrtValue index: from initializer_allocation_order not found among initialized tensorsCan not trace string tensorentry != initialized_tensors_to_allocate.end() bytes for entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGSkipping entry for missing optional value at idx [Memory] SessionStateInitializer statically allocates failed.Using user supplied initializer with name (Done saving initialized tensorsDeserialize tensor session.use_device_allocator_for_initializersonnxruntime::session_state_utils::ExtDataTensorProtoToTensorFailed memory size calculationDeserializeTensorProto() takes either pre-allocated buffer or an allocator!C:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires Failed to copy tensor to string tensor is not supported for copying between allocators) because the ORT planned memory location device ) is different from what is supplied (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator () is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingSubgraph input with name GraphUnsupported device allocator in the context of pre-packed weights caching: C:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.cconnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator is not foundInternal error.' is not foundMem pattern for initializer ' failedWeight buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer 'duplicated locationFailed to get allocator for location: C:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlanfunc info for node: already exist. not found.Can't use func with null ptrC:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncs!using_counters_C:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.honnxruntime::MemPatternPlanner::TraceAllocationcurrent <= buffer_size_onnxruntime::RunSinceidx < device_stream_map_->NumStreams()C:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.cconnxruntime::StreamExecutionContext::GetDeviceStreamonnxruntime::StreamExecutionContext::RecycleNodeInputsframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()ort value releasedsince == endExiting due to terminate flag being set to true.v >= 0C:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.honnxruntime::StreamExecutionContext::CountDownBarrier::Set activate notification with index onnxruntime::ActivateNotificationStep::ExecuteWaitOnEPStep: wait on notification with id: Launch kernel with node id: ActivateNotificationStep: activate notification with id: TriggerDownstreamStep: trigger downstream of trigger point: Set a barrier with id: WaitOnEPStep.wait_handle is null, count: C:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cc wait on Notification with id: wait_handle_onnxruntime::WaitOnEPStep::Execute[TypeInferenceError] NoOpConstantTypeProto must have shape for this to runC:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hshape != nullptronnxruntime::utils::GetShapeALLOW_RELEASED_ONNX_OPSET_ONLYThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullinput edgesoutput edges' Model is invalid.Duplicate constant node sparse initializer name: 'graph_proto != nullptronnxruntime::Graph::GraphDuplicate sparse_tensor_initializer: '. Please, fix your model.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()does not have the graph for key onnxruntime::Node::SaveToOrtFormatLoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatnode_arg_name cannot be null], could not find NodeArg Node::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for fbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceinput index: onnxruntime::Node::LoadEdgesFromOrtFormatonnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:onnxruntime::MergeShapeInfoC:\a\_work\1\s\onnxruntime\core\graph\graph.cc' source:Error merging shape info for output. 'Type mismatch. Current=onnxruntime::NodeArg::UpdateTypeAndShapeTensor element type mismatch. Input=Removable attributes were removed before the conversion is started.onnxruntime::Node::ToProto . Got: Optional Type mismatch. Expected: Serialization of fused function body is not currently supported, Removable attributes were removed before the node is saved.] op_type [Node [Output:Source and target must both be tensors. Falling back to lenient merge. target: , or optional typed entities , or sparse tensorsNode () does not have type information set by parent node.) ) Op () is invalid.) in node () input arg (This is an invalid model. Node (This is an invalid model. Type Error: Type ' in node () of operator (' of input parameter (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) bound to different types () of Optype (This is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: Some nodes are not included in the topological sort, graph have a cycle.onnxruntime::Graph::KahnsTopologicalSort inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires in node No Graph instance was found for attribute Node:Subgraph input missing type. inputs but subgraph has Size mismatch validating subgraph inputs. Got node.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInferenceonnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsInvalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Invalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeonnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.' is not a graph input, initializer, or output of a previous node.Invalid model. Node input '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=This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoInitializer Graph state to be loaded into must be empty.) does not exist in the graph.This is an invalid model. Graph output (Graph ctor should have created NodeArg for initializer. Missing:node_argThis is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name () 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 (_token_ as it still has output edges.onnxruntime::Graph::SaveToOrtFormatonnxruntime::Graph::RemoveNodeFailed to convert dense initializer to sparseCan't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::ToGraphProtoWithExternalInitializers'was added but does not exist. onnxruntime::Graph::ToGraphProtoexternal_stream.is_open()Cannot find NodeArgs for [initializer_node_arg != nullptrOuter scope node arg name 'onnxruntime::Graph::ToGraphProtoInternalonnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.sparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0onnxruntime::Graph::ReplaceInitializedTensorImplTrying to replace non-external initializer with external data!foundFailed to find existing initializer with name graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()Replacement tensor's dimensions do not match.Replacement tensor's data type does not match.onnxruntime::Graph::InjectExternalInitializedTensorsReplaced external initializer: ) is required but not specified.) attribute () is not a registered function/opFatal error: onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingError: Duplicate definition-site for (Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::Resolveexisting->second == &tensoronnxruntime::Graph::AddInitializedTensor but different TensorProto.AddInitializedTensor already has tensor with name ) does not match expected type () of node () type inference failed) output arg () does not have type information.This is an invalid model. Model input () of output arg (Type Error: Type (Type Error: Data in initializer ' does not match. but usage of initializer in graph expects ' has element type This is an invalid model. In Node, onnxruntime::Graph::VerifyNodeAndOpMatchType Error: Shape of initializer , Error index < data_.size()onnxruntime::ConstPointerContainer > >::atC:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.h) -> (NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()onnxruntime::Graph::LoadFromOrtFormatSparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'NodeEdge is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Node index is out of rangecallnode.TryGetFunctionProto(inlined_fp)onnxruntime::Graph::InlineFunction_inline_Node has no function body and cannot be inlined.Constant node name: insert_result.second conflicts with graph initializer. Check Specializing code. in inlined function: Input to set must exist.input->Exists() in inlined subgraph: Initializer name: ("""onnxruntime::Graph::SetInputs) : (Removing initializer ''. It is no longer used by any node.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is not used by any node and should be removed from the model. must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())outer_scope_node_arg != nullptrRemoving NodeArg 'onnxruntime::Graph::CreateFusedSubGraphNode OpType:onnxruntime::Graph::AllocateNodenullptr != func_meta_defonnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)absl::container_internal::raw_hash_map<>::atDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetknown by the checker., but it its domain is notC:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalthan the operator set version , but it its version is higheronnxruntime::GraphViewerToProtoC:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.ccnodeInvalid ExecutionOrderMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: onnxruntime::GraphViewer::GetNodesInTopologicalOrderIndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:nodeargonnxruntime::GraphViewer::GraphViewerC:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccFailed to load model with error: 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 Protobuf parsing failed.No graph was found in the protobuf.onnxruntime::Model::Load is less than 0.onnxruntime::Model::Save less than 0.onnxruntime::Model::SaveWithExternalInitializersonnxruntime::Model::SaveToOrtFormatProtobuf serialization failed. is less than 0. is till 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 onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetC:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.h is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::model_load_utils::ValidateOpsetForDomainC:\a\_work\1\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.onnxruntime::Model::ModelUnsupported model IR version: model may run depending upon legacy support of some older opset version operators.Missing model IR version., max supported IR version: are '0' and '1'. The environment variable contained the value: onnxruntime::SaveModelWithExternalInitializersLoad model system error number failed. File doesn't existonnxruntime::SaveModelGraph is null. Invalid ORT format model.Null entry in metadata_props. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatTOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.Attribute Negative values are not allowed in a shape specification should specify a shape expected to have tensor type expected to have tensor or sparse typeOutput The scale to apply.Bias applied to each channel, same size as C.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Threshold valuedataValue of betaValue of alphaC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.) + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D) + scale[0] () needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + scale[1] (hidden_prevdrop_statesseq_lengthsgatesGRUUnithiddenScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Constrain input and output types to all tensor types.extra_shapeinput_as_shapeConstrain indices to integer typesGivenTensorFillvaluesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.Attribute expected to have tensor or sparse tensor typeInputs 2 (value) shall be 3 or 4 dimensionsInputs 0 (query) shall be 3 or 5 dimensionstoken_offset shall be 2 dimensionsinput shall be 2 dimensionsWhether every token can only attend to previous tokens. Default value is 0.Number of attention headsThe past_key input shall be 4 dimensionsThe value to be filled in the attention mask. Default value is -10000.0fWhether to use rotary position embedding. Default value is 0.Corresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCustom scale will be used if specified. Default value is 1/sqrt(head_size)Error parsing node:input shall be 3 dimensionskey and value cache shall be 4 dimensions = Constant()Error unexpected extra input in node:past_valuepast_keypresent_keypresent_valuePackedMultiHeadAttentionConstrain mask, offset and sequence length to integer typesConstrain input and output to float tensors.Corresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)cache_indirectiontoken_offsetPackedAttentioncumulative_sequence_lengthInputs 2 (value) shall be 2 dimensionsInputs 0 (query) shall be 2 or 4 dimensionskeyquerypastMrelative_position_biasC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccConstrain mask index to integer typespresentqkv_hidden_sizes should have 3 elementsInvalid bias shapeInputs 0 shall be 2 dimensionsDecoderAttentionConstrain key_padding_mask to bool tensors.Constrain input and output types to float and float16 tensors.new_value_cachemask_index_typeThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)The epsilon value to use to avoid division by zero.segment_idssegment_embeddingposition_embeddingword_embeddingkey_cachekv_weightstatic_kvvalue_cachehas_layer_stateuse_pastnew_key_cachehas_key_padding_maskglobal_biasglobal_weightmaskglobalGLongformerAttentionConstrain to integer typesq_weightkey_padding_maskwindowOne sided attention windows length W, or half of total window lengthConstrain mask to integer typesweightThe NGram size.SkipSimplifiedLayerNormalizationscores_outscoresThe maximum NGram size for suffix matching.The minimum NGram size for suffix matching.Constrain scores input and output types to float tensors.RelativePositionBiasConstrain sequence_length to int tensors.skipConstrain mean and inv_std_var to float tensors.input_skip_bias_suminv_std_var 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) max_distanceMax distanceConstrain input and output types to float or half tensors.bias_tableis_bidirectionalDefault value is 0.key_lengthquery_lengthConstrain input and output integer tensors typesembedding_sumbaConstrain input and output float tensors types.X_bias = Identity (X)X_bias = Add (X, bias)oneRestorePaddingConstrain token_offset to integer typesquery_biasquery_layerrel_posGatedRelativePositionBiaseco_asequence_token_countcumulated_seq_lenRemovePaddingConstrain sequence_token_count and token_offset to integer typesmax_seq_lencur_tokenssrc_tokenspred_tokensprev_suffix_match_idxsuffix_match_idxtokensGemmFastGeluWConstrain to integer types.̼+̼+C expected to have tensor or sparse tensor type: Incompatible dimensions expected to have rank >Dimension mismatch in unification between expected to have rank but has rank is null CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) AlphaThe inner-most 2 dimensions must have the same size (mat_w: != mat_h:Input rank must be >= 2.Alpha value.padding_idxFailed to parse max_length or it is not positive integer scalarInputs 0 shall be 3 dimensions in whisper graphIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).Failed to parse num_return_sequences or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalar CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) OneAC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64early stop or notno repeat ngrams sizeThe id of the token that indicates decoding starts.The id of the padding tokenDecoder subgraph to execute in a loop.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.model type: 0 for GPT-2; 1 for encoder decoder like T5FSize of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeConstrain the output to a boolean tensor.If true, check only for NaN.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.Three interpolation modes: bilinear (default), nearest and bicubic.GridThe id of the end-of-sequence tokenConstrain output types to float tensors.Constrain input types to all tensor types.ratioresidualConstrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.isinf_onlyConstrain output 'mask' types to uint32 tensors.Constrain input 'training_mode' types to boolean tensors.If true, check only for Inf, -Inf.IsAllFiniteBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.isnan_onlyTorchEmbeddingConstrain input and output types to all numeric tensors.scale_grad_by_freqBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.true if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1apply softmax to elements for dimensions axis or higherConstrain input and output types to all numeric tensors and bool tensors.(Optional) Seed to the random generator, if not specified we will auto generate one.number of dimensions comprising the signalComplexMulIrfftConstrain input and output types to float tensorsPadsComplexMulConjSecond input does not have rank 2First input does not have rank 2ZConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.filtered_logitsConstrain input0 and output types to float tensorsmust be 0, normalization currently not supportedsignal_ndimnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)Rfftmust be 1, only one sided FFTs supportednormalizedThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartConstrain input type to float or int tensors.The value used to module the next token probabilities.Presence penalty for custom samplingMinimumber of tokens we keep per batch example in the output.All filtered values will be set to this float value.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.presence_maskModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartIf 1 custom sampling logicvocab_maskrepetition_penaltyprefix_vocab_masksequenceslogits_processorConstrain to float tensors.sequences_scoresactivation_axisWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationsparse_tensor(int64)sparse_tensor(double)sparse_tensor(float)sparse_tensor(uint32)sparse_tensor(uint64)sparse_tensor(int32)Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Quantization typeInput q4 tensors of wrong size!Constrain input B data types as data blobConstrain input matrix data types as single precision float tensorB_shapeConstrain shape of B must be int64 tensor.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationScalar multiplier for the product of the input tensors.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorinputs 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 ignoredan optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Constrain input A data types as 16-bit integer tensor4b quantization not yet supported on this hardware platform!B input for MatMul must be a 2-D matrix!Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorScalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.Input axis is invalid: activation_gammaScalar multiplier for input tensor C.roiscrop_sizebatch_indicesEpsilonConstrain types to int tensors.Constrain types to float tensors.Axis1D = Constant()Zero1D = Constant()Rank = Size (XShape)XShape = Shape (X)Input can be of any tensor type.countsidxThe 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".rois input tensor has wrong dimensionfirst input tensor has wrong dimensionConstrains input to only numeric types.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionConstrain to tensor(float).Constrain to tensor(int32).Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of array'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64xlast dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.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.Constrain input and output types to any tensor type.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightConstrain indice type to int32 or int64SequenceInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.boxesEfficientNMS_TRTConstrain output Y and scale type to float tensors.anchorsdetection_boxesnum_detectionsBackground class ID.detection_classesdetection_scoresInput 0 shall be 3 dimensionstype used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).InvStdDevConstrain mean and inv_std_var to be float tensors.Constrain output Y, scale and bias type to float tensors.Type of Mean and InvStdDev tensors.Constrain input X type to float tensors.B2D = Flatten (B)Scaled = Mul (NormalizedV, Scale2D)Scale2D = Flatten (Scale)NormalizedV = Cast (Normalized)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)Biased = Identity (Scaled)Biased = Add (Scaled, B2D)max_output_boxesInvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)Attribute 'pooled_size' must be >= 1.pooled_sizeAttribute 'max_output_boxes' must be >= 1.SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)Mean2D = ReduceMean (XU)XU = Cast (X2D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)MeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)inputsnotes(Optional) Some notes for the modelConstrain input and output types to uint8, uint16, float tensors.outputsConstrain output 'mask' types to bit-packed uint32 tensor.Constrain 'training_mode' to boolean tensor.disentangled_attentionp2c_attentionfactorScaling 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.spanMaximum relative distance, k.DLCpayload of the SNPE DLC file.target_device(Optional) Target device like CPU, DSP, etc.snpe_version(Optional) SNPE version used to convert the model.feature_map_2feature_map_1Image size.patchesfeature_map_3DisentangledAttention_TRTPyramidROIAlign_TRTPooled size.image_sizec2p_attentionc2c_attentionBox IOU threshold value.box_codingEncoding type for the boxes or anchors inputs.background_classplugin_versionVersion number of the TRT plugin.Max detections to output.iou_thresholdscore_thresholdScore threshold value.score_activationActivation function to apply to the scores input.feature_map_0MultilevelCropAndResize_TRT does not specify a valid type. should be of integer type and specify a type.Value of attribute not specifiedCan't merge shape info. Both source and target dimension have values but they differ. Source= Dimension= or UNDEFINED. Got: expected to have: C_zero_pointC_scaleThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Constrain input and output types to 8 bit signed and unsigned tensors.y_scaleConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.y_zero_point Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} inputs are expected to have tensor type.Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.Scale and Zero-point must be of rank 1{additionalDocumentation}{name}A_zero_pointA_scaleB_zero_pointB_scaleScale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is Input data type does not match the expected data typeb_zero_pointb_scalea_scaleConstrain input B data type to 8-bit integer tensor.Constrain input A, b_scale and output Y data type as float tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A data type to 8-bit integer tensor.a_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointadditionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointDequantizeBFPA 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).Constrain input type to 8-bit integer tensor.reducedReduceSumIntegerKeep the reduced dimension or not, default 1 mean keep reduced dimension.MulIntegerConstrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.Constrain the input to float and bfloat.QuantizeBFPConstrain shape and strides to uint64.Constrain y to uint8.The datatype to dequantize to.Shape of quantized tensor must be 1D.Constrain y to float and bfloat16.Constrain the input to uint8.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccx_zero_pointx_scaleThe type of BFP - must match with the BFPType enumConstrain 'y', 'x_scale' to float tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.block_dimEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.bfp_typeinitial_cinitial_hPConstrain seq_lens to integer tensor.Y_cY_hNumber of neurons in the hidden layerSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain input and output types to signed/unsigned int8 tensors.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.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.Couple the input and forget gates if 1.sequence_lensRConstrain input and output types to 8 bit tensors.Y_zero_pointY_scale'axis' must be in [ , ]. Its actual value is: opset version of corresponding SoftMax.apply softmax to elements for dimensions axis,or all dims along with axis according to op-versiondata_scaleaxis must be in [-rank, rank-1]. input rank was reduced_zero_pointreduced_scaledata_zero_pointCoefficient of leakage.QLinearReduceMeanX_zero_pointX_scaleConstrain input and output types to int8 tensors.weight_zero_pointgamma_quantposition_embedding_quantword_embedding_quantword_embedding_scalebeta_quantsegment_embedding_scaleposition_embedding_scaleConstrain output type to float32 or 8 bit tensors.Constrain output zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain input B and its zero point types to 8 bit tensors.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)weight_scaleinput_scaleinput_zero_pointconditionz_zero_pointz_scaleConstrain input A and its zero point types to 8 bit tensors.Constrain scale types to float tensors.All inputs to Concat must have same rankaxis must be in [-rank, rank)Required attribute axis is missingConstrain weights types to 8 bit tensors.T8Which axis to concat onTVSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Constrain scale types to any float tensor type.scale_Yscale_BQOrderedMatMulConstrain bias and scales to float32scale_Corder_weightcublasLt order of weight matrixHidden layer sizes of Q, K, V paths in Attentionscale_Q_gemmcublasLt order of global biasDequantizeWithOrderThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)cublasLt order of output matrixcublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Border_AcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_YcublasLt order of matrix Y and optional matrix Corder_Bscale_AConstrain input and output types to float32 tensors.mask_index_outorder_outputcublasLt order of output matrix.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.Qscale_inputQuantizeWithOrderConstrain Scale to float32 typesConstrain to float typesbeta_scalegamma_scaleposition_embedding_zero_pointword_embedding_zero_pointgamma_zero_pointsegment_embedding_zero_pointlayernorm_outbeta_zero_pointscale_global_weightscale_qkv_gemmscale_outputscale_global_gemmweight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.QOrderedLongformerAttentionBe compatible with float version.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.Constrain scales to float32order_global_weightQOrderedGeluscale_biasscale_weightscale_values_gemmcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.QOrderedLayerNormalizationQOrderedAttentionConstrain scales to float32 tensors.scale_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.order_Xquantization tensor must be int8 tensors.quantization scale must be float tensors.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.Q_weightscale_V_gemmscale_K_gemmscale_Q_weightV_weightK_weightK_biasQ_biasscale_V_weightscale_K_weightscale_QKT_softmaxscale_QKT_gemmV_biasPadding for the beginning and ending along each spatial axisauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.QLinearConvTransposenumber of groups input channels and output channels are divided into.w_scaleww_zero_pointConstrain output type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.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 shape of the convolution kernel. If not present, should be inferred from input W.Constrain bias type to 32-bit integer tensor.̼+BiasSplitGeluBiasAddgroupsThe number of groups of channels. It should be a divisor of the number of channels CThe epsilon value to use to avoid division by zero1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Activation after group normalization: 0 for None, 1 for SwishConstrain gamma and beta to float tensors.Constrain input X and output Y types to float tensors.bias shall be 1 dimensionC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccGroupNormWhether to use ceil or floor (default) to compute the output shape.The 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.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Works on NHWC layout or not? Default not.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.NhwcConvC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccPadding 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.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index: ExplicitInputs:onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::FindPathFailed since multiple edges matched:onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsAttempting to get an input that does not exist.onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:index >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size() ImplicitInputs:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameAttempting to get index by a name which does not exist:for node: std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0C:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::GetIndexFromNameitr != node_args.end() Implicit input name cannot be safely updated to in one of the subgraphs.onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphC:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.cconnxruntime::utils::SetNodeAttributeutils::HasName(attribute)AttributeProto must have a name.Attempting to load runtime optimization records for a previously loaded optimizer: onnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatruntime optimization record produced op id is null. onnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatC:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::SaveRuntimeOptimizationRecordToOrtFormatHdelimiter must not be emptyC:\a\_work\1\s\onnxruntime\core/common/string_utils.honnxruntime::utils::SplitString!delimiter.empty()invalid string_view positionC:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.cconnxruntime::fbs::utils::LoadOpIdentifierOrtFormatFailed to parse since_version from C:\a\_work\1\s\onnxruntime\core/graph/op_identifier.honnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringInvalid OpIdentifier string: TENSORSPARSE_TENSORGRAPHSTENSORSSTRINGSSPARSE_TENSORSINTFLOATUNDEFINEDINTSFLOATSGRAPHSTRINGMissing indicies for sparse initializer: Missing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dims for sparse initializer: Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.C:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveInitializerOrtFormatSaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatonnxruntime::fbs::utils::SaveSparseInitializerOrtFormatMissing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.TinoutTout is not found in model local functionsdomain_version != -1Too many inputs for op static_cast(i) < node_op_schema->inputs().size()in, domain: The given function name: onnx_func_protoC:\a\_work\1\s\onnxruntime\core\graph\function_utils.cc in function opset imports.No opset registered for domain it != opset_imports.end()onnxruntime::function_utils::IOTypeConstraintHelper::::operator ()all_tensor_types_ir4TAggregatedTypesinput_arg->Type() != nullptronnxruntime::function_utils::CreateSchemaNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()onnxruntime::function_utils::Inliner::bindonnxruntime::FunctionImpl::FunctionImplC:\a\_work\1\s\onnxruntime\core\graph\function.cc is not found or is not constant initializer.initializer != nullptrsegment_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.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionsinput_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsword_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.The past input shall be 5 dimensionslimitdeltaCan not get shape initializer data!Unsupported type:Constrain input and output types.Unsupported non-raw-data data type!invalid scales dimensioninvalid scales valueC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cctensor rank too smallmemory_seq_lensQWMWAWCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.Found out-of-range processor id in affinity string: Failed to read affinities from affinity string, thread_pool_size: Number of affinities does not equal to thread_pool_size minus one, affinities: std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0, skip affinity settingFound invalid processor id in affinity string: Received invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringSize of affinity string must be between 1 and actual_num_affinities == static_cast(options.thread_pool_size) - 1onnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setReceived null OrtThreadingOptions;Processor id must start from 1: processor_from > 0 && processor_to > 0Invalid processor interval: processor_from <= processor_toProcessor id must consist of only digits: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)onnxruntime::concurrency::ReadThreadAffinityConfigC:\a\_work\1\s\onnxruntime\core\util\thread_utils.ccdims[d_i] < d_maxonnxruntime::math::NextPositionC:\a\_work\1\s\onnxruntime\core\util\math_cpu.cc??rB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@Kbad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;bad mlas activation kind), 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!`P8PP    C:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideStringlength overflowonnxruntime::ToUTF8Stringonnxruntimeonnxruntime::logging::LoggingManager::CreateDefaultLoggerOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. C:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultISink must be provided.onnxruntime::logging::LoggingManager::LoggingManager[ONNXRuntimeError]C:\a\_work\1\s\onnxruntime\core\common\status.ccSystemErrorcode != static_cast(common::OK)onnxruntime::common::Status::StatusMODEL_LOADEDNOT_IMPLEMENTEDRUNTIME_EXCEPTIONINVALID_PROTOBUFNO_MODELENGINE_ERRORFAILNO_SUCHFILEGENERAL ERRORINVALID_GRAPHEP_FAILSUCCESSC:\a\_work\1\s\onnxruntime\core/platform/path_lib.hunknown errorsystemgeneric. Specify the number of threads explicitly so the affinity is not set., error msg: , group_id: SetThreadAffinityMask done for thread: Cannot set cross-group affinity for thread , mask: , then on , first on group GetFileSizeEx fail, errcode = open file SetThreadAffinityMask failed for thread: - , error code: , index: custom_create_thread_fn returned invalid handle. doserrno: index >= 0onnxruntime::WindowsThread::WindowsThread/\Negative thread index is not allowedonnxruntime::UnmapFileC:\a\_work\1\s\onnxruntime\core\platform\windows\env.ccCannot set affinity for thread onnxruntime::WindowsThread::ThreadMain does not exist, processor SetThreadDescriptiononnxruntimeWindowThread:_beginthreadex failed with message: kernel32.dllRaiseFailFastExceptionkernelbase.dll [%hs(%hs)] [%hs] error msg: unmap view of file failed. error code: %wsLogNtLogHrReturnNtReturnHrExceptionMsg:[%ws] CallContext:[%hs] %hs(%d) tid(%x) %08X %ws %hs!%p: (caller: %p) FailFast%hs(%u)\%hs!%p: \*onnxruntime::LoopDircore core(s) from windows system:Failed to fetch cpu info on windows consist of logical processors: Failed to calculate byte size for saving cpu info on windowsonnxruntime::WindowsEnv::InitializeCpuInfoFailed to find symbol onnxruntime::WindowsEnv::FormatLibraryFileNameFound total onnxruntime::WindowsEnv::GetCanonicalPathUNC\RemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: DeleteFile() failed - path: onnxruntime::WindowsEnv::DeleteFolder\/K32EnumProcessModules in library, error code: FreeLibrary failed with error Kernel32.dll "LoadLibrary failed with error "" when trying to load "length > buffer.size()SetFilePointerEx onnxruntime::WindowsEnv::ReadFileIntoBufferoffset < 0file_path == nullptrInvalid fd was supplied: Received negative size from stat call , mapped_offset = mapped offset must be a multiple of the allocation granularity , errcode = , allocation_granularity = onnxruntime::WindowsEnv::MapFileIntoMemoryopen file mapping ReadFile fail: unexpected endonnxruntime::Path::Parseonnxruntime::`anonymous-namespace'::ParsePathRootC:\a\_work\1\s\onnxruntime\core\common\path.ccFailed to parse path root: /\enabled_Profiler not started yetunnamed_thread_poolonnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionC:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParalleln <= num_threads_ + 1"num_run": "core": UnknownEventWaitRevokeWaitRunDistributionEnqueueDistributionMore work items than threadsonnxruntime::concurrency::ThreadPool::ParallelForn >= 0!ps_onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection!current_parallel_section.has_value()Nested parallelism not supported": {LogStart must pair with LogEnd{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}C:\a\_work\1\s\onnxruntime\core\common\threadpool.cconnxruntime::concurrency::ThreadPoolProfiler::Stop], "core": "thread_id": "", "block_size": [onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetpoints_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd!points_.empty()] }, } " : "" : "args" : {"name" :"Writing profiler data to file onnxruntime::profiling::Profiler::EndTimeAndRecordEventMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::Initializesession_logger != nullptrC:\a\_work\1\s\onnxruntime\core\common\profiler.cc"ph" : "X","ts" :"dur" :"tid" :"pid" :{"cat" : "",onnxruntime::profiling::Profiler::EndProfilingNodeSessiononnxruntime::profiling::Profiler::StartApiKernelHA@A|AxAillegal input path:unexpected failure333p= I h1*ffffffGzGK7A!uq m7c^6 02 p.9 ^ (4BP_p $@^}(NuM|DyYMV(q S A 9 = cCdeHImMSuUVwWxXyYz  '.6>GPZdoz -?Rey,E^x<Zy<^00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899< I "!9!!!#(#####$%%%%&&&&''''!'('3'D'G'L'N'S'W'c'''''4)++P+U+00=022 /l~/2<IFt HZ <G  rv  9 >  +R/ @je _vH@0ļ4&k7y؅W4Ngm=`X@xPMJ- yCx(,*42q0/'|!1錠9Y>R$n`F"x|+MraB ydiWC*fJ\)c7]`xxֲK}Yq\Ck 3T`)#+#.?0@0Ф0paP0123456789abcdefghijklmnopqrstuvwxyz Kp0%)Y : ; < > A I M N Q b   < > A G K Q p u   < > @ A G K M U b   < > A F J U b  ; > ? 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[:, ] {}{:02}{}{:02}{:02}{:04}Invalid presentation type for wchar_tMissing precision specifier.Negative precision.Precision is not an integer.Negative width.Width is not an integer.Number is too big.Precision not allowed for this argument type.0e+00Dynamic width or precision index too large.Invalid presentation type for floating-pointInvalid presentation type for integerInvalid presentation type for boolInvalid presentation type specifierFormat specifier requires numeric argument.Number is too big%H:%M:%I:%M:%S %pModifier requires an integer presentation type for boolInvalid presentation type for pointerInvalid presentation type for stringCan not switch from automatic to manual indexingInvalid fill (too long).{:L%F %T}Incompatible modifier for typeInvalid type specification.Invalid modifier specification.Invalid format string - missing type after modifier.Unknown format specifier.Invalid format string.Unmatched '}' in format string.nan(snan)nan(ind)invalid fill character '{'Invalid format string - missing type after %Invalid format string - chrono-specs must begin with conversion-specInvalid type.Precision specification invalid for non-chrono::duration type, see N4885 [time.format]/1.UTCCan not switch from manual to automatic indexingMissing '}' in format string.0B0bintegral cannot be stored in wchar_t{:0{}}falsetrueString pointer is null.Argument not found.0X0xmap_typesequence_typetensor_typeModel must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadValueInfoOrtFormatNull type info for . Invalid ORT format model.Type: is not supported currentlyNull map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.opset import domain is null. Invalid ORT format model.opset id is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatNull entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for is missing type info.onnxruntime::fbs::utils::SaveValueInfoOrtFormatWe do not support type [] for nowonnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormatC:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveSequenceTypeOrtFormatjHr8r(r' is expected to have field 'strings'' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'type_proto'' is expected to have field 'g'' is expected to have field 'sparse_tensor'' is expected to have field 't'Mismatched attribute type in 'Duplicate type constraint nameRequired attribute '' is missing. has unknown expected type' is expected to have field 'type_protos'' is expected to have field 'graphs'' is expected to have field 'tensors') than declared () in op definition.has output size not in allowed output sizes.) has output size not in allowed input sizes.) has input size not in range [min=Unrecognized attribute: for operator ' appeared multiple times.)'s output ) has more outputs ()'s input is marked single but has an empty string in the graph) has more inputs (, max=Operator '' has been deprecated since version has unsupported type has inconsistent type typestr: , has unsupported type: (outputs_.size() - 1) == iONNX Schema : failed validating the check: (inputs_.size() - 1) == iExtra unparsed input unexpected.Error parsing function body:Cannot find a function builder that satisfies the requested opset version: op_type = , opset_version = !(it.GetName().empty())complex128complex64DataTypeUtils::FromDataTypeString - Received invalid data type string 'opaquemapInvalid data type optionalInvalid tensor data type Unsuported type proto value case.sparse_tensor(map(optional(tensor(C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\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.Only one of values_*'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 setInput type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.Label encoder has only one output.An integer.A string.A list of strings. One and only one of 'value_*'s should be set.A list of floats.A list of ints.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.The size of each input in the input listThe input type must be a tensor of a numeric type.An integer vocabulary array.
One and only one of the vocabularies must be defined.A string vocabulary array.
One and only one of the vocabularies must be defined.Label encoder has only one input.Value(s) to change to.A value that needs replacing.Value(s) to change toThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.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 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' sequenceThe 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 output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.map(string, double)map(string, float)map(int64, double)map(string, int64)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The input must be an integer map to either string or float.Values greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type. The output will be of the same tensor type.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.The output is a 1-D tensor of string, float, or integer.map(int64, float)map(int64, string)The number of support vectors.Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Non of classlabels_int64s or classlabels_strings is set.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.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.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.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Class 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'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.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'.The output type will be a tensor of strings or integers, depending on which of 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].The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.Weights of the model(s).Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.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.Exactly one of 'cats_*' attributes must be provided.One of 'MAX,' 'L1,' 'L2'The output will be a tensor of strings or integers.The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]Input's shape should be 1D or 2DA float.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.A collection of weights of the model(s).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.seq(map(int64, float))seq(map(string, float))The output will be a sequence of string or integer maps to float.Child node if expression is falseChild node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The weight for each targetThe index of the target that each weight is forThe node id of each weightThe id of the tree that each node is in.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.The weight for the class in class_id.The index of the class list that each weight is for.node id that this weight is for.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.Child node if expression is false.Child node if expression is true.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.ccA list of labels.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.indices must be in [-rank, rank-1]. and Output expected to have tensor or sparse tensor typeInput: does not match type of output: type: axis must be in [-rank, rank-1].The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.output = Cast (input)C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.ccConstrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Invalid dimension value: Invalid position of 0.Target shape may not have multiple -1 dimensions.target_typeoptional(tensor(complex128))optional(tensor(complex64))The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtooptional(tensor(int8))optional(tensor(uint64))optional(tensor(uint32))optional(tensor(uint16))optional(tensor(uint8))optional(seq(tensor(complex128)))optional(seq(tensor(complex64)))optional(seq(tensor(bool)))optional(tensor(bool))optional(tensor(string))optional(tensor(double))optional(tensor(float))optional(tensor(float16))optional(tensor(int64))optional(tensor(int32))optional(tensor(int16))optional(seq(tensor(uint64)))optional(seq(tensor(uint32)))optional(seq(tensor(uint16)))optional(seq(tensor(uint8)))optional(seq(tensor(string)))optional(seq(tensor(double)))optional(seq(tensor(float)))optional(seq(tensor(float16)))optional(seq(tensor(int64)))optional(seq(tensor(int32)))optional(seq(tensor(int16)))optional(seq(tensor(int8)))transposedA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Attribute perm for Transpose has repeated value: }, input shape = {Invalid attribute perm {stepsInput and output types can be of any tensor type.updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Slice op must have either three, four or five inputs.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps'step' cannot be 0 for SliceNumber of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Input rank for starts and ends should be the same: () vs (Input steps has incorrect lengthInput axes has incorrect lengthIncorrect or missing input value for starts and endsInvalid value of attribute 'axis'. Rank= Value=Constrain output types to any tensor type.concat_resultWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Neither 'split' input nor 'num_outputs' attribute has been givenAttribute `num_outputs` value cannot be lower than 1Mismatch between the sum of 'split' () and the split dimension of the input (Mismatch between number of splits () and outputs (Both 'split' input and 'num_outputs' attribute were given(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.reshaped(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 shapesAll inputs to Concat must have same rank. Input has rank Constrain output to int64 tensor, which should be a scalar though.Constrain output to int64 tensor.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.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.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.Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".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.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).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.Constrain roi type to float or double.sizesroiConstrain repeat's type to int64 tensors.repeats'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64 This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. 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, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .Constrain input 'X' and output 'Y' to all tensor types.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Blocksize must be positiveexpandedList 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).squeezedDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Blocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalWhich 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 >= 1Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).(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).Invalid value for attribute axis Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` 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` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.inverse_indicesConstrain to all tensor types.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.(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.'sequence_lens' must have rank of 1'input' must have rank >= 2depth(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 'values' must be rank 1 tensor.Input 'depth' must have exactly one element.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.Constrain output types to boolean tensors.Constrain input types to float tensors.Constrain to any tensor type.Constrain input to only numeric types.Indices tensor must have rank >= 1Constrain input and output types to all tensor, sequence, and optional types.Constrain grid types to float tensors.Constrain input `X` and output `Y` types to all tensor types.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.Constrain to boolean tensors.(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).'axis' must be in [-rank(indices), rank(indices)-1]Axis is referred to more than once.CenterCropPadIf provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.Unexpected axis value: . Expected range [sh_diff = Sub (x_shape2, shape)x_shape2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)x_shape_alldims2 = Shape (padded_input)padded_input = Pad (input_data, pads)padded_input = Pad (input_data, pads, , axes_input)pads = Concat (pad_amount_left, pad_amount_right)pad_amount_right = Sub(pad_amount, pad_amount_left)output_datainput_dataoutput_data = Slice (padded_input, start_dims, end_dims)output_data = Slice (padded_input, start_dims, end_dims, axes_input)end_dims = Add (start_dims, shape)start_dims = Div (sh_diff, k2)Number of elements of input 'shape' () does not match the number of axes (`shape` only supports `int32_t` or `int64_t` inputsCenterCropPad op must have 2 inputs.Supported modes: `constant`(default), `reflect`, `edge`, `wrap`pad_amount_left = Div(pad_amount, k2)pad_amount = Sub(padded_sh, x_shape)padded_sh = Max(x_shape, shape)x_shape = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)x_shape_alldims = Shape (input_data)axes_input = Constant ()tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.initial_state_and_scan_inputsfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.v_initialv_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.condGraph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.Only boolC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccoptional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))optional(seq(tensor(bfloat16)))optional(tensor(bfloat16))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv9.(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).The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.ccConstrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } (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 integer or float, or /32-bit integer tensor.'y_scale' determines the output type.Attribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionConstrain input and output types to float and 8 bit tensors.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\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 `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.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Input tensor must have at least 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeThis operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Number of groups of offset. C must be divisible by offset_group. Default is 1.offset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.DeformConv 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. Dilation value along each spatial axis of the kernel. Default is 1 along each axis.Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeThe shape of the convolution kernel. If not present, should be inferred from input 'w'.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.number of groups input channels and output channels are divided into. default is 1.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.p value of the Lp norm used to pool over the input data.RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedAttribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. IndicesConstrain index tensor to int64MaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.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=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.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). The number of channels to sum overScaling parameter.The exponent.Constrain input and output types to float tensors.The bias value added to output. Default is 0. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } ) for attribute 'axis'Invalid value(Constrain input and output to all tensor types.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The lambd value for the Shrink formulation. Default is 0.5.Constrain mean and variance types to float tensors.input_meaninput_varrunning_meanrunning_varConstrain scale and bias types to float tensors.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Deviation = Sub (X3D, Mean)ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)XReshaped = Reshape (X, NewShape)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)Axes2Mean = ReduceMean (X3D, Axes2)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)num_groupsC = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)MeanOfSquare = ReduceMean (Square, Axes_1)NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Constrain input types and output Y type to float tensors.sinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalization%s:%u: %s: Assertion `%s` failed.FloatEpsilonEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.image_shapeblock_shapeConstrain input and output types to all numeric tensor types.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) 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) } Attribute pads must have an even sizeinput must have rank 3.1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value 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 is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.Input shape must have either [C] or [1,C] dimensions where C > 0string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.The number of groups of channels. It should be a divisor of the number of channels `C`.GroupNormalizationC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.cc(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 to be numerics.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.sparse_valuevalue_intvalue_intsvalue_floatvalue_floatsvalue_stringvalue_stringsAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorInput to 'Range' op should be scalars (Tensor with only one element and shape empty)X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliAll inputs to 'Range' op must be of the same typeConstrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Output type must be int32 or int64Input tensor must have rank 2Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Constrain output types to integral tensors.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.The mean of the normal distribution.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(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.Constrain output types. Strings and complex are not supported. { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to integer tensors.Constrain input to integer tensors.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input types to all numeric tensors.Constrain input types to all (non-complex) tensors.Constrain input/output to boolean tensors.Constrain input to boolean tensor.Constrain output to boolean tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccsum { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.cc { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_square = Mul(data, data) sum_square = ReduceSum(data_square, axes) sum_square_dbl = Cast (sum_square) sqrt = Sqrt(sum_square_dbl) reduced = CastLike(sqrt, data) } The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information.Constrain output type to all tensor or sequence types.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\defs.ccOptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.Type of the element in the optional outputConstrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types.Optional is expected to have an output.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } MishCoefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Constrain input and output types to signed numeric tensors.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors.Wrong op_type name for running propagation: broadcasting: (Invalid rank for Axis has less than the requested k elements.KValuesDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } K input must be a one-dimensional tensor of size 1.K input must be of type int64. X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) X_ReduceMax = ReduceMax (input) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_ReduceMax = ReduceMax (input, axes)output = Identity (input)input_less_than_min = Less (input, min)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp) { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } List of tensors for data_0Constrain input and output types to numeric tensors.slope { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } Value of alpha.Value of beta. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Constrain input and output types to floating-point tensors.const_zeroconst_oneexpanded_target = Unsqueeze (target, axes)ignore_indexConstrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.axis tensor can be int32 or int64 onlyConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.input and zero_point pair is expected to have same type.Constrain input and output types to all tensors.Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.log_prob = Identity (X_Log)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels)labelsEinsum expression string.InputsConstrain input and output types to all numerical tensor types.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Shape3D 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) Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.Rank of input targetlossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.weight_gather_temp = Gather (weight, transform_targets)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)const_one_floatconst_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_ignore_index 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) input_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)frame_lengthConstrain signal and output to float tensors.frame_length input must be scalar.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!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.signalframe_steplower_edge_hertzupper_edge_hertzConstrain to integer tensors.Constrain to float tensorsConstrain to any numerical types.dft_length input must be scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. num_mel_binssample_rateConstrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } 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) } num_mel_bins input must be scalar.size input must be a scalar.size input must be greater than 0.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. 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. A {name} window with length: size. The output has the shape: [size].HannConstrain the input size to int64_t.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.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.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.dft_lengthConstrain scalar length types to int64_t.log_probSoftmaxCrossEntropyLossis_onesided and inverse attributes cannot be enabled at the same time is invalid for a tensor of rank axis attribute value dft_length input must be a scalar.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccoutputs...Which axis to split onlength of each outputList 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`.paddingsThree modes: constant(default), reflect, edgeConstrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.rank must be greater than axis'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`constant_valueThree interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". 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).'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape 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)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. 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).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.length of each output. Values should be >= 0.Input axes has invalid data'step' cannot be 0Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid Target shape product of 0The input is not evenly splittableList 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.Constrain input and output types to all tensor and sequence types. Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Which axis to split on. Attribute value for pads is requiredAttribute pads has incorrect lengthWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted'axis' must be in [-rank(indices)-1, rank(indices)](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.Ending indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAxes 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`Two interpolation modes: nearest(default), bilinearConstrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.tilesConstrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scaleOne float, indicates the value to be filled, default is 0New shapelegacy optimization attribute.consumed_inputspositionConstrain 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.output_sequenceConstrain input types to any tensor type.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=input_sequenceC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.ccSequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index Element type of inputs are expected to be the same.Attribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.out_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMap_inSequenceMap__seqlen_cond_seqemptyThe 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.additional_inputsExpected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_body_itercount_cond_in_cond_out expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not available outputs. Expected Graph attribute inferencing returned type information for Invalid ``body`` argument. Expected a graphExpected 1 or more inputs.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Input 'split' can not be empty. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitScan input was notScan 'body' subgraph outputs should all be tensors but output axis value ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified (All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\old.ccAll Tensor types up to IRv4. was not a tensor.Mismatched type for output Mismatched tensor element type for output All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.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.then_branch and else_branch produce different number of outputs. but subgraphs produce If node has else= then=Int64 tensorAll Tensor types was Loop 'body' subgraph outputs should all be tensors but output selected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.max_output_boxes_per_classdefault 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\defs.ccMultiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. Input must be an optional-type value containing an element with type information.OptionalHasElement is expected to have 1 input.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccDilation value along each spatial axis of filter.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.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.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.STDX_varianceProcessed_STDStride along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccEX_squaredX_squaredE_XsquaredIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.The ratio of random dropoutConstrain output mask types to boolean tensors.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain mean and variance types to float tensors. It allows all float type for U.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.saved_meansaved_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. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.vardilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].First input tensor must have rank 3The 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].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.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.ccConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'x' to float or int32 tensor.Constrain input and output types to high-precision and 8 bit numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).The axis in which to compute the arg indices.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\old.cc Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The type of the output tensor is integer.'shape' input must be 1D tensorinput_gather_elementexpanded_targetloss_N1ddloss_NCddweight_gatherloss_NddC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\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).Scalar multiplier for input tensor C, the default value is 1.0.Invalid value for attribute kDimension on which to do the sort.Number of top elements to retrieveMaximum value, above which element is replaced by maxMinimum value, under which element is replaced by minScalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedCoefficient of ELU default to 1.0.Value of beta default to 0.5Value of alpha default to 0.2Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.X_shapeX_LogSM_NCDDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeX_LogPass 1 to enable broadcasting If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. If set, defines the broadcast dimensions. See doc for details.broadcastconst_one_castedsqueeze_maskweight_gather_temp_1weight_gather_tempX_NCDX_LogSMX_NDCloss_sumloss_unweightedconst_zero_target_typedweight_gather_sumtransform_targetsexpanded_target_int64input_gather_element_transformconst_zero_castedConstrain input to integral tensors.Constrain input to float tensors.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccIf set, defines the broadcast dimensions.Enable broadcastingC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.ccC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\old.cc 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) 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) 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 fowardThe sequence output for the hidden is optional if 0. Default 0.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.source map type missing key type.target optional type missing element type.Mismatched map tensor key type:target map type missing key type.target map type missing value type.source map type missing value type.Input was expected to have tensor or sparse tensor type. Got Element type of tensor or sparse tensor input was unknown source= target=Mismatched tensor element type:Mismatched type:source sequence type missing element type.Mismatched sparse tensor element type:source optional type missing element type.target sequence type missing element type. expected to have optional typeElement type of sequence input expected to have map typeElement type of optional input Value type of map input Key type of map input Mismatch between number of source and target dimensions. Source= expected to have type but instead is nullInput type was null unknown expected to have tensor or sparse tensor type. Got: expected to have sequence typeElement type of input Value type of map input was unknownKey type of map input was unknownshape input must be 1D tensorInput was expected to have either tensor, sequence, optional or map type. Got Input element type of does not match existing output type of Input was expected to have sequence type. Got Output was expected to have tensor type. Got Input was expected to have optional type. Got Element type of sequence input was unknownInput was expected to have map type. Got Element type of optional input was unknown data type is string. string content is required to be stored in repeated bytes string_data field.ParseData type mismatch for tensor: . Expected:Cannot parse data from external tensors. Please load external data into raw data for tensor: expected size does not match the actual size raw_data type cannot be string.Data size mismatch. Tensor: is undefined so it cannot be parsed. Actual:The type of tensor: ==> Context: domaingraph' is required to be non-empty.' of 'value_infoField 'nameutf8str_to_wstring: string is too long for converting to wstring.] not in lexicographic sorted order.] out of range.) must have rank 1.sparse_tensor_proto) must have a dense-rank > 0Sparse tensor values () must have INT64 type.) dimensions are not positive.Sparse tensor indices () has ) index value at position [] out of range [0, ] not in sorted order.Sparse tensor () second dimension size does not match rank of tensor.) first dimension size does not equal NNZ. values, but NNZ is ' should be stored in field '' instead of 'Unrecognized data_type (tensor name: values of data_type ') is stored externally but doesn't have a location.) should be stored in ) is 0-element but contains data!TensorProto ( tensor name: ) should contain one and only one value field.TensorProto (tensor name: STRING data (tensor name: ) should not be stored in raw_data fieldData of TensorProto ( tensor name: ) is stored externally and should not have data field.Location of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: , but the '' points outside the directory, but it doesn't exist or is not accessible.) should be file inside the float_datasetting data_type field (tensor name: string_dataint32_dataraw_dataint64_datauint64_datadouble_dataelem_type' is required but missing.value_typekey_typeUnrecognized type value case (value_info name: ): ) to UNDEFINED is not alloweddata_typeConstantFillSparse tensor initializers must have a non-empty name in initializer but not in graph input is not output of any previous nodes. sparse initializer name is not unique across initializers and sparse_initializers' of node: name: ' has been used as output names multiple times.Nodes in a graph must be topologically sorted, however input ' OpType: Op registered for ' has been used as graph input names multiple times.Bad node spec for node. Name: initGraph must be in single static assignment (SSA) form, however ' initializer name is not uniqueTensor initializers must have a non-empty name) has zero input and zero output.op_typeNodeProto (name: , type: with domain_version of No opset import for domain ' is deprecated in domain_version of No Op registered for ) has no index values.) must have rank 1 or 2.type field and data field mismatch in attribute attrAttribute (name: ) should not contain more than one value field.Warning: Model contains experimental ops:) should refer to attribute in parent node.type_protointstol argument out of rangeinvalid stol argumentError context: [ParseError at position Unexpected type in sparse-tensor element type.Expecting primitive type as map key type.Error parsing TensorProto (expected a tensor type).Unexpected type.Error parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Unexpected attribute type.Unhandled type: %dExpected character (line: Unexpected literal type.Integer value expected, but not found.Identifier expected but not found.String value expected, but not found.Value expected but not found.Incomplete string literal.producer_nameopset_importmodel_versionproducer_versionmetadata_propsdoc_string column: floatstensorsstringssparse_tensorsgraphsir_versiontype_protosopaque_typeoptional_type does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.Data for input already exists. is out of bounds.unk__Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in dimension : ((op_type:Cannot use the same name as both a subgraph initializer and subgraph input: Graph has were provided.The number of graph input cannot be smaller than the number of node inputin initializers. Graph initializer names must appear after the actual inputs: , node name: Cannot find missing input: Cannot infer type and shape for node name . No opset import for domainShape inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set. inputs but were providedNOT_SETsparse_tensor_typetype case mismatch. existing= inferred=type case unsupported. existing=key type mismatch from MapProto. existing= optype type case unsupported for symbolic shape inference. inferred=Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X' values.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. Got Dimension value inferred () is not equal to the existing dim value (Unknown value for `keep_aspect_ratio_policy`: Either `sizes` or `scales` must be provided, but not both of themNumber of elements of input 'sizes' (Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".Number of elements of input 'scales' () must be same as rank of input 'X' (Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.axes as an input and attribute cannot be specified at the same time.onnx.OperatorSetIdProtoonnx.TypeProtoonnx.FunctionProtoonnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TypeProto.Optionalonnx.TypeProto.Maponnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.ModelProtoonnx.TrainingInfoProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.SparseTensorProtoonnx.TensorProtoonnx.AttributeProtoonnx.NodeProtoonnx.ValueInfoProtoFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   DFA out of memory: SearchDFA inconsistencyprogram size pattern length SearchNFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencyendpos: startpos: Invalid RE2: text size: bytemap range list count RE2: invalid startpos, endpos pair. [Unexpected re_anchor value: ': Error parsing 'Unknown encoding C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.ccError reverse compiling 'Error compiling 'pattern too large - compile failedStack not empty.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hWalk NULLinvalid named capture groupNumCapturesWalker::ShortVisit calledtrailing \no argument for repetition operatormissing )unexpected )invalid perl operatorinvalid UTF-8invalid repetition sizebad repetition operatorno errorunexpected errorBad reference count Unexpected op in Regexp::Equal: invalid character class rangemissing ]invalid escape sequenceinvalid character classRegexp not destroyed.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccZZ8[P[[([(Z8ZYZpZZHZ`ZYshould never happenNo ranges in char classMissing case in Compiler: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccCompiler::Copy called!NlMnLmLoPcBad call to ParseState::ParsePerlFlagsMcNdBad hex digit unknown round: RE2: unexpected op: LlLtAnyLuRepetitionWalker::ShortVisit calledConcat of AddFoldedRange recurses too much.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccUnexpected opcode in short circuit: context does not contain textBad args: nsubmatch=unhandled C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.cc in AddToThreadq in stepUnhandled RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheFailed to analyze start state.Unexpected special state in RunStateOnByteStateSaver failed to restore state.unhandled opcode: DeadState in RunStateOnByteNULL state in RunStateOnByteC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.cc|GrowStack() failed: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccjob_.size() = njob_ = Unexpected opcode: Cannot use SearchOnePass for unanchored matches.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.ccUnexpected opcode in IsMatch: DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is Simplify case not handled: Malformed repeat Case not handled in ComputeSimple: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccjjdjjhjj\jj`jjjjjjpjjj(kj(kjjijiHk@jHkPjj jj0jjijij`ijpijiji@ki@kijpkjkjPkj`kkkkk0kk0kkkkk[:^graph:][:lower:][:^digit:][:graph:][:^print:][:punct:][:^lower:][:print:][:^ascii:][:blank:][:^alpha:][:ascii:][:^cntrl:][:digit:][:^blank:][:cntrl:]\S\w\D\s[:^alnum:][:alpha:]\W[:alnum:]\daz0909 09AZ__az!~ !/:@[`{~09AZazAZ09AFaf[:^xdigit:]AZaz09AZ__az ~ [:^space:][:upper:][:^punct:][:space:][:^word:][:xdigit:][:^upper:][:word:]$ؼԑp∀∀`҈#ȑˈˈ8ሀֈhx(Ԉ ׈ ׈읅҈ (0<x $∀\pو`8刀DX刀PpА(Ȉܐ׈[`ވSȐሀ<Ԉ (∀萈@숀㈀ Lj𔈀ӈ0H׈Ph((@@ ֈ  ȜPܔH蔈ĔP! 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