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Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s ╀╀ ╀(╀Can't happenD:\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...\\?\\/UNKNOWNINVALID_ARGUMENT:CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): , 'map/set too longunordered_map/set too longinvalid hash bucket count /\ was false.Stacktrace: Cpuai.onnx.trainingai.onnx.mlai.onnxai.onnx.preview.trainingnarrowing_errorAttempt to use DefaultLogger but none has been registered.VIWEFD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity is not implementedInteger overflowonnxruntime::IDataTransfer::CopyTensorAsyncD:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)IsTensor()Trying to get a Tensor, but got: D:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableTrying to get a SparseTensor, but got: IsTensorSequence()IsSparseTensor()ORT_LOAD_CONFIG_FROM_MODELTensor sequence must contain only primitive typesonnxruntime::TensorSeq::SetTypeelem_type_ != nullptri < tensors_.size()D:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.hTensorSeq: tensor to be added has a different data type.onnxruntime::TensorSeq::GetAtonnxruntime::TensorSeq::AddIsSameDataType(tensor.Get())tried creating tensor with negative value in shapeUnknown Exceptionnot enough space: expected , got `anonymous-namespace'::GetDataTransferNot able to find appropriate IDataTransfer to copy sparse dataStrings can only reside in CPU memoryD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shape`anonymous-namespace'::ValidateFillInputArgsOrtApis::FillSparseTensorCsrOrtApis::FillSparseTensorCooOrtApis::FillSparseTensorBlockSparsetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::UseCooIndicesCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::UseBlockSparseIndicesOrtApis::UseCsrIndicesUse GetStringTensor*() API to retrieve stringsthe ort_value must contain a constructed tensorRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledRegisterCustomOpsUsingFunction: Registration function was not foundno imputs were passedonnxruntime_profile_OrtApis::RunAsyncRunAsync() active adapters specified, but won't have an effectOrtApis::RunWithBindingRunWithBinding() has active adapters specified, but won't have an effectstring 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 1.20.0ORT Build Info: git-branch=HEAD, git-commit-id=c4fb724e81, build type=RelWithDebInfo, cmake cxx flags: /guard:cf /DWIN32 /D_WINDOWS /D_DISABLE_CONSTEXPR_MUTEX_CONSTRUCTOR /DWINAPI_FAMILY=100 /DWINVER=0x0A00 /D_WIN32_WINNT=0x0A00 /DNTDDI_VERSION=0x0A000000 /Qspectre /DONNXRUNTIME_ENABLE_INTEL_METEOR_LAKE_MOBILE_PLATFORM_PERF_PATCH /O2 /Ob1 /DNDEBUG /Zc:__cplusplus /EHsc /wd26812 -DEIGEN_HAS_C99_MATH -DCPUINFO_SUPPORTEDinvalid vector subscriptT utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. onnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataTensor must always contain primitive types. Found: Invalid index requested for map type.OrtCreateValueImplMapHelpervalue_type != nullptrValue type is not supported yet: Map 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 > > > > >::check != len >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::DataTypeImpl::GetType() == type_OrtCreateMapMLValueonnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggerOrtSessionOptions::RegisterCustomOpsLibraryD:\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 pointersDMLProvider options key/value cannot be emptyMaximum string length for a provider options key/value is 1024.D:\a\_work\1\s\onnxruntime\core\session\provider_registration.ccQNN execution provider is not supported in this build. OrtApis::SessionOptionsAppendExecutionProviderXNNPACKWEBNNOpenVINOSNPEVitisAIWebGPUAZUREUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'D:\a\_work\1\b\RelWithDebInfo\installed\include\google/protobuf/repeated_ptr_field.h in Output [TypeInferenceError] is null expected to have tensor or sparse tensor type: expected to have tensor or sparse type in expected to have tensor type in "). domain: "Trying to add a domain to LastReleaseVersion map, but the domain is already exist with last version: Trying to add a domain to DomainToVersion map, but the domain is already exist with version range (, domain: "CPUExecutionProvideronnxruntime::OpKernel::ComputeAsyncshape inference not yet supported for non-tensor typesD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInvalid shape infovalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypeOrtShapeInferContext::OrtShapeInferContextNo requested allocator availableFailed to fetch a stream hosting the requested resourceOrtShapeInferContext::SetOutputTypeShapesymbolic and integer dims mismatch!Attribute does not exist.Invalid attribute.Invalid contextFailed to fetch type shape info for the index.Size of data not large enough to hold the array of floats.Size of data not large enough to hold an int64.Size of data not large enough to hold a float.Attribute has no float value.Size of data not large enough to hold the string.Size of data not large enough to hold the array of strings.Attribute has no int64 value.Size of data not large enough to hold the array of int64.::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input nameUnexpected attribute type. Result buffer is not large enough::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a type::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo cannot get a valid logger from its execution provider' in custom op 'Output buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution providerInputOutputUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKerneloutputOnly the last to a custom op may be marked variadic.inputinputsoutputsi == count - 1onnxruntime::CreateSchema::::operator ()Another version of operator 'static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o)). onnxruntime allows the overloading of an operator if all versions have the same number of declared 'has a different number of No kernels to registers.ops.size() > 0defined list of typesall typesThere 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.1 == undefinedonnxruntime::CreateSchemacustom op registered at runtime input to be of optional typestinput count does not matchonnxruntime::IsCompatiblecustom op schemas mismatch, expecting input to be of variadic typendth input to be of single typeoutput count does not match input to keep same homogeneity input to keep same arity output to keep same homogeneity output to keep same arity output to be of optional type output to be of variadic type[CustomOP type inferencing error]: kernel Input: onnxruntime::InferOutputTypes output to be of single type is absent, but not optional. Op : [CustomOP type inferencing error]: no kernel def matches node inputs for Op: [CustomOP type inferencing error]: no type constraint found for output: Op: [CustomOP type inferencing error]: no type constraint found for input: onnxruntime::CreateCustomRegistry`InlinedVector::at(size_type) const` failed bounds checkinvalid unordered_map keyonnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size ( Max:D:\a\_work\1\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplDEFAULTValidating no unexpected access using an invalid node_index. Got:node_index < nodes_.size() is not present.onnxruntime::OpKernelContext::RequiredOutputD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hRequired output at index output_ptronnxruntime::OpKernelContext::OutputPlease fetch output tensor with specified shape.p_ml_valueIsSameDataType(tensor)onnxruntime::LoadDynamicLibraryFromProvideronnxruntime::ProviderSharedLibrary::EnsureProvider_SetHostD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime_providers_shared.dllGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime_providers_cann.dllonnxruntime_providers_rocm.dllonnxruntime_providers_cuda.dllonnxruntime_providers_cuda_ut.dllonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_vitisai.dllnum_of_threadscache_dironnxruntime_providers_migraphx.dlldevice_typedisable_dynamic_shapesenable_npu_fast_compile option is deprecated. Skipping this optioncontextenable_opencl_throttlingload_configmodel_priorityonnxruntime::OrtOpenVINOProviderOptionsToOrtOpenVINOProviderOptionsV2num_streamsTensorRT Provider not available, can't get interface for itonnxruntime::GetProviderInfo_TensorRTenable_qdq_optimizeronnxruntime::TryGetProviderInfo_TensorRTThe custom op domain name AddTensorRTCustomOpDomainToSessionOption is already in session option.SessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.CUDA execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT execution provider is not enabled in this build.oneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.CANN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared librarysession_optionsSessionOptionsAppendExecutionProvider_VitisAI: Failed to load shared libraryRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputRelease_State_Create_State_Compute_' op_type:kernel already mapped to existing nodematching node is missingD:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'invalid arg_num.invalid node output countinvalid node input count: , expect: standalone_Attribute type not supported yet.onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsarg_num < static_cast(input_count_)onnxruntime::standalone::InvokeOpKernel definition was not found for node Domain:'onnxruntime::standalone::CreateOpPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided allocator is nullOrtMemoryInfo is nullEnv is nullProvided OrtMemoryInfo is nullCudaD:\a\_work\1\s\onnxruntime\core\session\lora_adapters.ccCUDA provider is not enabled in this buildDML provider is not enabled in this buildUnsupported device allocatorExpecting on device allocator for LoraAdapteronnxruntime::lora::GetDataTransferparams != nullptronnxruntime::lora::CreateOrtValueOnDeviceAdapter is not loaded yet.onnxruntime::lora::LoraAdapter::InitializeParamsValuesParams absentOrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccenv_ptr == p_instance_.get()onnxruntime::IOBinding::BindInputD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cc it.second=onnxruntime::IOBinding::SynchronizeInputsonnxruntime::IOBinding::SynchronizeOutputsSize mismatchmapped_output_names_.size() == output_names_.size() index=Size mismatch:mapped_feed_names_.size() == feed_names_.size()onnxruntime::SyncProvidersonnxruntime::IOBinding::BindOutputImplp_providerunknown errorgenericbad cast*systeminvalid stoi argumentstoi argument out of range DeviceId: MemoryType:DeviceType:Device:[]OrtMemoryInfo:[ OrtAllocatorType: OrtMemType: id:name:CUDAExecutionProviderDmlExecutionProviderTensorrtExecutionProviderJsExecutionProviderROCMExecutionProviderWebGpuExecutionProvider config_options: { use_deterministic_compute: thread_pool_allow_spinning: use_per_session_threads:PRIORITY_BASEDMEMORY_EFFICIENT } session_log_verbosity_level: session_log_severity_level: session_logid: profile_file_prefix: inter_op_param: intra_op_param: graph_optimization_level: max_num_graph_transformation_steps: enable_profiling: execution_order: execution_mode:Session Options { enable_cpu_mem_arena: enable_mem_reuse: enable_mem_pattern: optimized_model_filepath:D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.hdelimiter must not be empty!delimiter.empty()onnxruntime::utils::SplitString has already been registered.Provider onnxruntime::ExecutionProviders::AddD:\a\_work\1\s\onnxruntime\core/common/string_utils.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hCould not find OrtValue with name 'Cached EP instance for graph replay is not set yet before calling ReplayGraph()Provided type is not an optional tensorIsOptionalTensor(type)onnxruntime::utils::GetElementTypeFromOptionalTensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.hProvided type is not an optional sequence tensorIsOptionalSeqTensor(type)onnxruntime::utils::GetElementTypeFromOptionalSeqTensorMemcpyTransformerORTM] [json.exception./session.load_model_formatsession.save_model_formatsession.set_denormal_as_zerosession.disable_quant_qdqsession.use_env_allocatorsoptimization.disable_specified_optimizerssession.inter_op.allow_spinningsession.intra_op.allow_spinningsession.disable_aot_function_inliningoptimization.minimal_build_optimizationssession.dynamic_block_basesession.force_spinning_stopsession.use_ort_model_bytes_directlysession.use_ort_model_bytes_for_initializerssession.debug_layout_transformationsession.disable_cpu_ep_fallbacksession.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytessession.strict_shape_type_inferencesession.allow_released_opsets_onlysession.intra_op_thread_affinitiesgpu_graph_id%Y-%m-%d_%H-%M-%Smemory.enable_memory_arena_shrinkagedisable_synchronize_execution_providers value of 'save' is only valid when saving an ORT format model.applyInvalid value for The environment variable contained the value: MemcpyFromHostMemcpyToHostShapesaveD:\a\_work\1\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: are '0' and '1'. The only supported values for the environment variable Reading the provided model for the ORT configonnxruntime::FinalizeSessionOptionsDone Adding ETW Sink to logger with severity level: Removing ETW Sink from loggerDone Removing ETW Sink from loggeronsession_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::GetSeverityAdding ETW Sink to logger with severity level: onnxruntime::InferenceSession::ConstructorCommon::::operator ()status.IsOK()onnxruntime::InferenceSession::ConstructorCommon;Creating and using per session threadpools since use_per_session_threads_ is trueoffFlush-to-zero and denormal-as-zero are onnxruntime::InferenceSession::ConstructorCommon::::operator ()Could not finalize session options while constructing the inference session. Error Message: Affinity string must not be empty!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fn--intra-opsession-Dynamic block base set to When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()onnxruntime::InferenceSession::TraceSessionOptionsGiven model could not be parsed while creating inference session. Error message: -inter-opcustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseError during EndProfiling(): onnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessiononnxruntime::InferenceSession::InferenceSessionCould not parse model successfully while constructing the inference sessionresultSo disabling it for this session since it uses . Having memory pattern enabled is not supported while using So making the execution mode sequential for this session since it uses the DML Execution Provider.Received nullptr for exec providerExecution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.onnxruntime::InferenceSession::SaveToOrtFormatParallel execution mode does not support the DML Execution Provider. Can't register custom op domains with ORT for onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryEncountered unknown exception in LoadWithLoader()model_loading_uri failed:Load model from Failed to save ORT format model to file: Exception during loading: This session already contains a loaded model.onnxruntime::InferenceSession::LoadWithLoaderModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Failed to load model because protobuf parsing failed.model_loading_arrayonnxruntime::InferenceSession::TransformGraphCastFloat16Transformeronnxruntime::LoadOrtModelBytes bytes were able to be read.onnxruntime::InferenceSession::TransformGraph::::operator ().onnxpost_layout_transform_step_onnxruntime::InferenceSession::TransformGraph::::operator ()ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.) has saved runtime optimizations. They will be ignored. failed. Only onnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModelWithLoaderThis session has already been initialized.The provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelonnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsThe old ORT format model (version ] is not supported in this build The ORT format model version [Missing Model. Invalid ORT format model.onnxruntime::InferenceSession::InitializeModel was not loadedModel was not loaded.Session has already been initialized.Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Initializing session.This session cannot use the graph capture feature as requested by the user as the model has control flow nodes which can't be supported by as all compute graph nodes have not been partitioned to the This session cannot use the graph capture feature as requested by the user as all compute graph nodes have not been partitioned to the it is safe to use the graph capture feature.Adding default CPU execution provider.This session will use the allocator registered with the environment.as the model has control flow nodes which can't be supported by This session cannot use the graph capture feature as requested by the user Use the graph capture feature with caution. This model has shape massaging nodes that will execute on CPU. as all the graph nodes have not been assigned to This session cannot use the CUDA Graph feature as requested by the user of the same shape as the representative input (common case), will match the shapes produced in the model for other inputs using the representative input used to capture the graph, As long as the intermediate shapes produced in the model Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerSerializing 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.1024This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA/HIP Graph feature as requested 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 contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user. Expected: Got: Invalid rank for for the following indices Session successfully initialized.Encountered unknown exception in Initialize()session_initialization Please fix either the inputs/outputs or the model.)) , expected: ( data type. Actual: (Unexpected Valid values kInput, kOutputGot invalid dimensions for index: ))( elements. has elements, but names has arg_type == ArgType::kInput || arg_type == ArgType::kOutputonnxruntime::InferenceSession::ValidateInputsOutputsfeedfetch with name: 'tensorsparse_tensor but received a sparse tensor name: Invalid but received a tensor' expected to be of type: Session was not initializedonnxruntime::InferenceSession::SetEpDynamicOptionsSession not initialized.Failed to parse the cuda graph annotation id: but received a tensor sequenceseqAt least one output should be requested.Invalid output name:Running with tag: Encountered unknown exception in Run()model_runStart another run for necessary memory allocation or graph capture. with graph annotation id: CUDA Graph for this model with tag: Replaying the captured onnxruntime::InferenceSession::Rununknown exceptionintra op thread pool must have at least one thread for RunAsynconnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::GetModelInputsinput name cannot be emptyNULL input supplied for input output name cannot be emptyfetch_unique_ptrs[i] != nullptronnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.Cannot find execution provider onnxruntime::InferenceSession::SetTuningResultsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingProfiler is disabled.Failed to load TuningResults (index=, enable TunableOp for usingCorrectly set TuningResults for does not support TunableOp.). Invalid TuningResults (index=). Reason: Did not find an arena based allocator registered for device-id combination is not an arena based allocator: The registered allocator for device-id error message: Unsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: combination in the memory arena shrink list: onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformersUnable to shrink arena: onnxruntime::InferenceSession::ShrinkMemoryArenasInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)bad conversion961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5invalid string_view positionD:\a\_work\1\s\include\onnxruntime\core/common/parse_string.hlist too long.json_onnxruntime::ParseStringWithClassicLocaleother_errorlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringFailed to parse value: "com.ms.internal.nhwcorg.pytorch.atencom.microsoftcom.microsoft.nchwccom.microsoft.experimental[ShapeInferenceError] Mismatch between inferred and declared type. Inferred= Declared=Unsupported Source/Target type=tensor(int32)tensor(uint64)tensor(float16)tensor(int64)tensor(uint32)unknowntensor(float8e5m2)tensor(float8e4m3fnuz)tensor(uint8)tensor(float8e5m2fnuz)tensor(double)tensor(float)tensor(float8e4m3fn)tensor(bfloat16)seq(tensor(uint8))seq(tensor(uint32))seq(tensor(uint16))tensor(int8)tensor(uint16)tensor(int16)seq(tensor(float16))seq(tensor(int64))seq(tensor(double))seq(tensor(float))seq(tensor(int8))seq(tensor(uint64))seq(tensor(int32))seq(tensor(int16))seq(tensor(bool))seq(tensor(string))seq(tensor(complex128))seq(tensor(complex64))tensor(bool)tensor(string)tensor(complex128)tensor(complex64)seq(tensor(bfloat16))Schema error: Trying to register schema with name (domain: , but its domain is not known by the checker., but it is already registered from file line version: ) from file , but its version is not in the inclusive range [forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).] (usually, this means you bumped the operator version but No allocator for this device has been registered for sharing.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.Exception caught: stringOnly CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.An allocator for this device has already been registered for sharing.YXConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.inter-opintra-opTD:\a\_work\1\s\onnxruntime\core\session\environment.cc is not implemented in CreateAndRegisterAllocatorV2()value, column ort_config at line Unsupported value for inter_op_num_threads: Setting intra_op_num_threads to Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for intra_op_num_threads: tuning_resultsD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to Setting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: Parallel modeSequential modeSetting enable_profiling to onnxruntime::SetEnableProfilingresultsepSetting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.ORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: validatorsThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()inter_op_num_threads option in the model file must be an integerexecution_mode option in the model file must be an integeronnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelintra_op_num_threads option in the model file must be an integerIgnoring unsupported session option in ORT config: Tuning results stored in the model file cannot be parsed. Error message: . Ignoring...graph_optimization_level option in the model file must be an integerenable_profiling option in the model file must be an integercannot use at() with onnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelkey '' not foundobjectnullbooleanarraycannot use key() for non-object iteratorsinvalid map keydiscardedbinarycannot get valuenumberinvalid_iteratortype_errorout_of_range; last read: '- ; expected unexpected parse_errorparse errorwhile parsing syntax error false literaltrue literalstring literalnull literalinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literal':''}'',''['number literal']''{'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid comment; missing closing '*/'invalid number; expected digit after exponent sign'[', '{', or a literalend of inputinvalid number; expected digit after '-'unknown tokeninvalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: missing closing quoteinvalid comment; expecting '/' or '*' after '/'invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: ill-formed UTF-8 byteinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cnumber overflow parsing 'object separatorvector too longobject keycannot compare iterators of different containerstype must be string, but is iterator does not fit current valuecannot use erase() with iterator out of rangetype must be number, but is type must be object, but is type must be array, but is AzureExecutionProviderGatherReshapeGatherElementsSqueezeResizeTileUnsqueezeFlattenExpandMaxPoolTransposeLeakyReluGlobalMaxPoolReduceMinReduceMeanArgMinArgMaxGlobalAveragePoolAveragePoolEluGeluHardSwishHardSigmoidReduceProdReduceMaxReluReduceSumAtanSqrtSinAsinSliceSigmoidSoftmaxLogSoftmaxLRNLogFloorCeilSignCosExpTanhClipSpaceToDepthAddLpNormalizationAbsRoundDepthToSpaceNegGridSamplePReluMaxConcatMulDivSubPowGemmMatMulLayerNormalizationInstanceNormalizationSplitMinConvTransposeConvLessOrEqualLessPadWhereEqualBatchNormalizationGreaterOrEqualGreaterOp version is not supported forD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.cconnxruntime::QDQ::SelectorManager::GetQDQSelectionsMultiple entries for operator is not supported. OpType=TopKonnxruntime::QDQ::SelectorManager::InitializeSelectorsMapinsertedonnxruntime::GraphTransformer::RecurseD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.htoidx < input_defs.size()cast node to cast from float16 to float32 on cpuCastInsertedPrecisionFreeCast_InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()onnxruntime::InsertCastTransformer::ApplyImpldtypeRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplonnxruntime::IsIsolatedFp16NodeOnCpuD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccACLExecutionProviderArmNNExecutionProvidersession.disable_double_qdq_removersession.enable_quant_qdq_cleanupQDQFinalCleanupTransformersession.qdqisint8allowedsession.x64quantprecisionoptimization.enable_gelu_approximationAttentionFusionBiasDropoutFusionsession.qdq_matmulnbits_accuracy_levelCastEliminationCommonSubexpressionEliminationBiasGeluFusionBiasSoftmaxFusionDoubleQDQPairsRemoverEliminateDropoutConvMulFusionDivMulFusionConvAddFusionConvBNFusionConstantSharingGatherToSliceFusionGeluApproximationFastGeluFusionGatherSliceToSplitFusionEmbedLayerNormFusionExpandEliminationDropoutDynamicQuantizeMatMulFusionGemmSumFusionGemmTransposeFusionGeluFusionGemmActivationFusionGeluFusionL1GeluFusionL2LabelEncoderLayerNormFusionL1IdentityLabelEncoderFusionEliminateIdentityMatMulIntegerToFloatFusionMatMulScaleFusionMatMulAddFusionMatMul_BatchNormalization_FusionSimplifiedLayerNormFusionMatMulActivationFusionLayerNormFusionL2LayerNormFusionClipQuantRewriteQDQPropagationTransformerPreShapeNodeEliminationAvx2WeightS8ToU8TransformerNotWhereFusionPad_FusionMatmulTransposeFusionNoopEliminationSkipLayerNormFusionEliminateSliceReshapeFusionRocmBlasAltImplQuickGeluFusionFuseReluClipQDQS8ToU8TransformerReluQuantRewriteonnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.ccLevelUnsupported optimization level: UnsqueezeElimination_RuleBasedTransformerTransposeOptimizerTransposeOptimizer_onnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildonnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer4Added by onnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeEnsureUniqueDQForNodeUnit/duplicatedDequantizeLinearD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.cconnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplVitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefs Memcpy nodes are added to the graph onnxruntime::MemcpyTransformer::ApplyImpl. It might have negative impact on performance (including unable to run CUDA graph). for Set session_options.log_severity_level=1 to see the detail logs before this message.onnxruntime::TransformerMemcpyImpl::AddCopyNodeCopy from/to host memory before Add Memcpy after ' doesn't support memcpy Execution type 'onnxruntime::TransformerMemcpyImpl::ProcessInitializersdup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.ccGraphTransformer onnxruntime::GraphTransformer::Apply with status: modified: Bad optional accessQLinearGlobalAveragePoolQLinearAveragePoolFusedConvLayout/Transpose optimization for failed: channels_lastThis transformer is already registered D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersbetablock_sizeaxisQuantizeLinear/GatherSliceToSplitFusion/Split for Fused Gather nodessplitsSqueeze for Fused Gather nodessqueeze_axessplit_outputD:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.cconnxruntime::GatherSliceToSplitFusion::ApplyImplUnsqueezeAxesInitializeraxesUnsqueeze_Unsqueeze for Fused Gather nodesunsqueeze_output_range_input_defs.size() == 3Rangeonnxruntime::GatherToSliceFusion::ApplyImplSlice for Fused Gather nodesSliceAxesInitializer': node 'Unable to constant fold. InlineIfSubgraph failed onnxruntime::ConstantFolding::ApplyImplIfcan't constant fold Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size()ConstantFoldingstartend' is expected to have a single boolean elementIf node condition initializer: `onnxruntime::ConstantFoldIfNodeD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cc. Can't constant fold Unsupported output type of Constant initializer NodeArg shape should not be null. NodeArg: shapeUnsupported data type: onnxruntime::`anonymous-namespace'::GetOutputNodeMergesFusedMatMulalphaFused MatMul and Scale/MatMulScaleFusion/onnxruntime::MatMulScaleFusion::ApplyImplonnxruntime::`anonymous-namespace'::GetScalarConstantInitializerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccdiv_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodemul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::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<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsSlice ends is less than INT_MAXGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathExpected value:Slice parameter is not expected. Input index:Start MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerunidir mask is not constantonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphFailed to find Softmax nodegather indices not matched.Pass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2Output edge count not expected for nodes in past subgraphPass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3permpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputq_transpose perm attribute not matchedPass CheckNodesInPathQStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose has not perm attributek_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKhidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.MatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kMask_Int32Cast mask from int64 to int32Start FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseFused an attention node for GPT.qkv_weightsqkv_biasMask shape is unknown or not 2D, or data type unknownonnxruntime::ConvertMaskToInt32D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs Attentionnum_headsunidirectionalmask_filter_valueFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32onnxruntime::FuseSubGraphQKFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vshape of layer norm bias tensor not expectedTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed in match v_matmul and v_add input shapeFailed in match input mask subgraphepsilonbn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_qdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantonnxruntime::QDQS8ToU8Transformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccDQMatMulToMatMulNBitsQDQSelectorActionTransformerdropSplitQDQdropdrop_no_int16_supportdrop_no_int16_support_and_positive_scaledropDQ1DQ2DQ*DQNOTSETauto_padmodepadsconstantonnxruntime::NodesToOptimize::GetNodeindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)NhwcConv and domain: NhwcFusedConvonnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::OpTypeUnsupported operator: D:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hConvActConvActivationFusionExpected activation node.D:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesactivation != nullptrFailed to get Clip min/max constants.activationactivation_paramsoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)onnxruntime::MatMulActivationFusion::ApplyImpl with activation D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cc_FusedActivationFusedMatMulActivationactivation_UnsqueezeElimination_onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination cannot remove node due to invalid axesD:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::QuickGeluFusion::ApplyImplQuickGeluD:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.cc/QuickGeluFusion/_min_zero_constantFuseReluClip_onnxruntime::FuseReluClip::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.ccminUnexpected data type for Clip 'min' input of D:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationBiasGeluonnxruntime::GeluApproximation::ApplyImplFastGeluTotal Gelu Approximation (FastGelu) node count: qrronnxruntime::ReshapeFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccTotal fused reshape node count: startsallowzeroFused reshape node: Cannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_SubgraphendsConcatTraining!sum_input_movedgraph.RemoveNode(gemm_node.Index())gemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0Sumother_sum_input != nullptrsum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())new_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::ApplytransAtransBFused Gemm with Sum/GemmSumFusion/D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccnew_gemm_output_defs.size() == 1onnxruntime::GemmSumFusion::SatisfyCondition and onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.hInvalid node index node != nullptrD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.ccQDQ insertion edge does not have a valid graph NodeArg for Expected at least one edge into which to insert QDQ pair.onnxruntime::`anonymous-namespace'::ValidateQDQInsertionEdgesonnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndnode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput at NodeArg "src node ("dst node (""._pre_q_q_to_dqInserted Q/DQ pair between onnxruntime::`anonymous-namespace'::InsertQDQPairs but expected NodeArg ] has NodeArg must have a source or a destination nodeQDQ insertion edge [0] for NodeArg ", index: node ("QDQ insertion edge [] for NodeArg onnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::PropagateQBackwarddst_node != nullptronnxruntime::`anonymous-namespace'::GetNextPropagationEdgesonnxruntime::QDQPropagationTransformer::ApplyImplFailed to set op schema for added Q node._post_dqInserted by QDQPropagationTransformer_qsrc_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdge_dqFailed to set op schema for added DQ node.deque too longonnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Input data type is not int32 or int64Failed to find path 1 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.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._Int32Cast Input from int64 to int32onnxruntime::CheckInputD:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_CastInput shape is unknown or not 2D, or data type unknownFailed to match Shape node. The parent of shape nodes are expected to be input_ids.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 position subgraph.position_embeddingsOptional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.two paths share the same shapeNonZeroSecond input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.ConstantOfShapeOutput edge count not expected for nodes in path1.Failed to get position embedding weights.Position embedding shape is not expected.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Input id is not valid. Segment id is not valid. EmbedLayerNormalizationWord embedding shape not expected.mask_indexfused EmbedLayerNorm subgraphs Failed to get initializer tensor.Position embedding shape not matched.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.onnxruntime::EmbedLayerNormFusion::ApplyImplBeta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertYieldOpDynamicQuantizeMatMulDynamicQuantizeLinearMatMulIntegerToFloatD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplonnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input '' onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.FreeDimensionOverrideTransformerwith a fixed dimension size which does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::MatMulAddFusion::ApplyImpl/MatMulAddFusion/fused Matmul and Add D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Gemm FusedGemmfused ThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccwith activation SoftplusSoftsignScaledTanhParametricSoftplusSeluUnexpected data type for QuantizeLinear input y_zero_point of onnxruntime::GetQConstantLowerUpperD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.ccConvAddActConvAddActivationFusionD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.ccExpected Conv then Add.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"onnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesonnxruntime::TryConvertDynamicQuantizeLSTMD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.cc int8 overflow might impact precision !MatMulIntegerQLinearMatMulQLinearConvweight_zp_s8_2_u8QAttentionQGemmonnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplDynamicQuantizeLSTM unable to locate recurrence tensor or its zero point value, cannot locate recurrence tensor of const int8 type,Unable transforming DynamicQuantizeLSTM operator,Fused Gemm with Transpose/GemmTransposeFusion/onnxruntime::GetTransposePermsD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()onnxruntime::UpdateConsumerCounttranspose_node.InputDefs().size() == 1transBatchBfused MatMul and Transpose Created a new Transpose node to interchange Cast and Transpose nodes_transformedonnxruntime::MatmulTransposeFusion::ApplyImpltransBatchAcast != nullptronnxruntime::ReorderCastAndTranspose/MatmulTransposeFusion/Created a new Cast node to interchange Cast and Transpose nodesConvMulFusion_W_mul_B_tensor_protoConvMulFusion_Mul_B_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::Applyonnxruntime::RocmBlasAltImpl::ApplyImpl__backwardpassD:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.ccqr prronnxruntime::LayerNormFusion::ApplyImplfused LayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cc/SimplifiedLayerNormFusion/stash_type/LayerNormFusion/SimplifiedLayerNormalizationonnxruntime::SimplifiedLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccSkipLayerNormalization_Floatonnxruntime::SkipLayerNormFusion::ApplyImplCast Input to floatqrrBuffer overflowgrouponnxruntime::NchwcTransformerImpl::TransformConvoutput_channels <= nchwc_output_channelsSAME_UPPERkernel_shapeVALIDSAME_LOWERReorderInputreorderdilationsstridesalign_cornersasymmetricnearest_modehalf_pixelnearestchannelscoordinate_transformation_modelinearReorderOutput_bn_nchwc_nchwcD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.ccspatialreshapeUpsampleflooronnxruntime::NchwcTransformer::ApplyImplscales is not in valid range [- into softmax(input + bias)is_inner_broadcastBiasSoftmaxIsAxisInRange(axis, tensor_rank)axis D:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImpl98:output_dtypeDoubleQDQRemoved_ of node Not eliminating output ] because it's the graph's output.[D:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndexonnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cconnxruntime::QDQFinalCleanupTransformer::ApplyImplFound back-to-back nodes: onnxruntime::`anonymous-namespace'::CleanUpNodeSequence with name "Notonnxruntime::TransposeOptimizer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer.ccTranspose optimizer failed: MatMulBnFusion_GemmGenerated from Matmul BatchNormalization fusionMatMulBnFusion_GemmBiasMatMulBnFusion_GemmB_matmul_b_tensorvar_tensormean_tensorbias_tensorD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_bn_fusion.cconnxruntime::MatmulBNFusion::Applyscale_tensorD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplT1NhwcTransformerfused Add-Dropout-(Add) for BitmaskBiasDropoutBiasDropoutBitmaskDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.cconnxruntime::BiasDropoutFusion::ApplyImplonnxruntime::`anonymous-namespace'::actions::BiasFusion::ValueMovestarget.GetOutputEdgesCount() == 1NMatMulNBitsFusionMatMulNBitsFuseBiasD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_nbits_fusion.cconnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplfused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErfskeys_values_default_int64onnxruntime::OpNodeProtoHelper::GetAttrD:\a\_work\1\s\include\onnxruntime\core/framework/op_node_proto_helper.honnxruntime::BiasGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Geluonnxruntime::FastGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs rqrr  Total shared scalar initializer count: onnxruntime::ConstantSharing::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.cconnxruntime::ConvAddFusion::Applyadd_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccConvAddFusion_Add_B_ConvAddFusion_B_D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointonnxruntime::QDQ::IsQOrDQScalePositiveConstantScalarq_or_dq_input_defs.size() >= 2NhwcMaxPoolcom.microsoft.QuantizeLinearstorage_ordercom.microsoft.QLinearAddcom.microsoft.DequantizeLinearcom.microsoft.QLinearConcatcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearReduceMeancom.microsoft.QLinearMulcom.microsoft.QLinearSigmoidonnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrsuccessFailed to find initializer for name: Failed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializer to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializernew_num_elts == old_num_eltsCannot reshape initializer Added in transpose optimizer_outopset_import_iter != domain_to_version_map.end() domain not found in opset imports.An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.onnxruntime::GetSinceVersionForNewOp does not match rank perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimization\ort_optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDimsPermutation entry out of bounds for shape Failed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()onnxruntime::ApiTensor::NumElementssize >= 0No NodeArg found for name onnxruntime::ApiTensor::Datasince_version.has_value()Transpose Optimizer is adding an unexpected node: Existing destination type is not compatible with source type.const_transpose_optimizeronnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type) ╀ ╀ ╀ ╀ ╀h ╀h ╀h ╀ ╀ ╀ ╀ ╀ ╀ ╀ਖ ╀ਖ ╀ਖ ╀ਖ ╀ ╀ ╀ ╀ ╀ȓ  ╀ȓ ╀ȓ  ╀  ╀ ╀ ╀ p p  p @ p ਖTanSinhShrinkCeluAsinhAcoshCoshAcosOrXorAtanhAndMeanCastLikeModBitShiftkeepdimssaturateIsInfIsNaNnoop_with_empty_axesReciprocalReduceSumSquareReduceL1ReduceLogSumReduceLogSumExpUnsupported ONNX opset: LpPoolReduceL2HardmaxMaxUnpoolGlobalLpPoolUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()RandomNormalLikeMultinomialD:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccmaxRandomNormalRandomUniformLikeRandomUniform0H𜖀ਖonnxruntime::MoveInputOutputIndex out of range input arg counts but there are only D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cc, actual input arg count: Expected at least onnxruntime::`anonymous-namespace'::MoveInputOutputImpl::::operator ()Node index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexExpected input arg count of zero for input onnxruntime::`anonymous-namespace'::MoveInputOutputImpltarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.called_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceonnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccInitializer must have a datatypeutils::HasDataType(tensor_proto)Expecting 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.empty()size() == other.size()onnxruntime::Initializer::subonnxruntime::Initializer::addExpecting the same sizeAxis must be non-negativeaxis >= 0onnxruntime::Initializer::mulonnxruntime::Initializer::divscalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)onnxruntime::Initializer::scale_by_axisInvalid other(scalers) sizeonnxruntime::Tensor::MutableDataAsSpanonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()allocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoD:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::TryCreateKernelTried to allocate without valid type information, ort_value index=D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.cconnxruntime::SelectorActionRegistry::RegisterSelectorAndActioninserted_in_name_to_entryExisting registration with name Not enough produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsonnxruntime::MatchAndProcessMatched New node op () does not match produced node op in runtime optimization record ().onnxruntime::SetOpSinceVersionForProducedNodesApplying runtime optimization action for transformer onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizationsToo many produced nodes in the runtime optimization record.Target node is not assigned to a compatible execution provider, skipping action.Nodes to optimize are not valid, skipping action.Missing action QLineargenerated at runtimeinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8accuracy_level_ >= 0 && accuracy_level_ <= 4MatMulNBits accuracy level must be between 0 and 4opsetinit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aaccuracy_levelKD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\qdq_actions.cconnxruntime::QDQ::DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsAction_Tonnxruntime::QDQ::DQMatMulToMatMulNBitsAction::ProcessNewNodeBuffered tensors map cannot be nullbitsFailed to add buffered tensor fused_DQ_MatMul_zero_point_Tonnxruntime::CreateReplacementNodeD:\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::ReplaceWithNew::RunOpenVINOExecutionProviderDnnlExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderQNNExecutionProviderNnapiExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderTvmExecutionProviderVSINPUExecutionProviderCANNExecutionProviderMake the EP: name shorteronnxruntime::GetAvailableExecutionProviderNames::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.ccprovider.name.size() <= kMaxExecutionProviderNameLen:9h8:*++H`+x++ :0+H09H++P:++Pcubiconnxruntime::RegisterOnnxOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryperm size: [ does not align with rank of input data: perm: is outside range.Attribute perm of Transpose has an invalid value. Value is repeated. does not match input rank: v >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.hMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 0Transpose not implemented for empty tensors.num_axes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)Transpose of element size not supported in this build. Size=onnxruntime::IncrementIndexAndComputeOffsetSetupD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cc(local_source >= source) && (local_source < source + num_blocks * blocksize)onnxruntime::DoTransposeImplinput_tensor_ptr != nullptronnxruntime::Transpose::Computeonnxruntime::DoTransposeEltWise(local_source >= source) && (local_source < source + num_blocks)Mismatched data types between input and output Tensors. Failed to unpack Int4x2 Tensor to an int8_t Tensoronnxruntime::UnpackInt4TensorExpected to transpose int4 tensoronnxruntime::DoTransposeInt4Failed to pack 8-bit Tensor into 4-bit Tensor(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::TypedDoTransposeEltWisecenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hcenter_point_boxscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.NonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppression::Computeoutput != nullptrboxes 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::GetThresholdsFromInputsTindonnxruntime::Gather::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccGather Tind type not supported in this build.Missing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK()onnxruntime::GatherBase::GatherBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.h must be within the inclusive range [indices element out of data bounds, idx=X != nullptronnxruntime::UnsqueezeBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.haxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensorD:\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.Missing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseonnxruntime::Unsqueeze::Computex_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.ccx_zero_point must be null or have the same rank as x for blocked quantizationzero_point_ptr == nullptr || zero_point_ptr->Shape().NumDimensions() == input_shape.NumDimensions()x_scale must be ceil(Di/block_size) on the quantize axis i for blocked quantizationscale.Shape()[i] == (input_shape[i] + quant_block_size - 1) / quant_block_sizeblock_size must be 0 for per-tensor quantization.quant_block_size == 0x_scale and x must have the same rank for blocked quantizationscale.Shape().NumDimensions() == input_shape.NumDimensions()For per axis quantization, scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimFor per axis quantization, x_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_scale and x must have the same shape despite the quantize axis for blocked quantizationscale.Shape()[i] == input_shape[i]x_zero_point and x_scale must have the same shape for blocked quantizationzero_point_ptr->Shape()[i] == scale.Shape()[i]T2onnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear >::Computeonnxruntime::QuantizeLinear >::Computeonnxruntime::QuantizeLinear::ComputeUnsupported input type.onnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Compute'block_size' must be non-negative.block_size_ >= 0onnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0DequantizeLinear into BFLOAT16 is not implemented yet.onnxruntime::DequantizeLinear::ComputeDequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::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::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearMemory allocation failed. Size=onnxruntime::IAllocator::ValidateAllocationp != nullptr || size == 0D:\a\_work\1\s\include\onnxruntime\core/framework/allocator.honnxruntime::MatMulComputeHelper::Compute::::operator ()num_dims_with_pad - 1 != num_output_dimsM_ == 1 && N_ == 1 was falsePer-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.num_dims_with_pad - 2 != num_output_dimsleft_num_dims and right_num_dims must be >= 1left operand cannot broadcast on dim left_num_dims > 2 && left_num_dims == right_num_dimsnum_dims_with_pad != num_output_dimsright operand cannot broadcast on dim D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::ComputeTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueMatMul dimension mismatchIsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeTST3IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1onnxruntime::IAllocator::ValidateAllocatorallocator != nullptredgereflectInvalid 'mode' attribute valuewrapD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.honnxruntime::PadBase::PadBaseInvalid 'pads' attribute valuedims.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:D:\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()Pads tensor size should be equal to twice the number of explicitly provided axes.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor size should be equal to twice the input dimension count pads_data.size() == narrow(2 * num_axes)Axes tensor should be a 1D tensor Unexpected mode of onnxruntime::PadBase::ComputePadsnum_axes_dims == 1onnxruntime::Pad::Computepads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of pads_data.size() == 2 * data_rankPads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]onnxruntime::PadValueFromFloatonnxruntime::PadInputWithDimValueOfZerodata_rank * 2 == pads.size()'pads' has wrong number of valuesInput tensor has no dimensionsonnxruntime::PadImpldata_rank > 0VB inputs. Found:LoopI outputs so the subgraph requires but has num_subgraph_outputs - 1 == num_outputs'Loop' node has static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infoonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: All scan outputs MUST be tensorsLoop subgraph input 1 has unknown shape: 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::Initinfo.GetAttr("body", &proto).IsOK()info_ == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph. Expected: Got:bodyInconsistent shape in loop output for output. feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of Subgraph SessionState was not found for 'body' attribute.onnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Computesession_state'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs) outputs.onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output info.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrthen_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()onnxruntime::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::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_' attribute.If nodes condition input must have exactly one element. shape=Dimension of input must be 1 instead of axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a vector tensor.onnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1onnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hinput count mismatchonnxruntime::Flatten::Flattenonnxruntime::Flatten::ComputeSizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hfinal_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputAttempt to retrieve final output before it was set.gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axes but expected Number of entries in 'scan_input_axes' was scan_output_directionsscan_input_axesinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directions dimensions or more but input had shape of Expected onnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::SetupSubgraphExecutionInfofeeds_fetches_manager_ && info_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsonnxruntime::Scan<9>::Initonnxruntime::ScanImpl::TransposeOutputScanInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::Execute. Output tensor rank was of Invalid value in scan_input_axes for input Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was ' dimension but input 'Invalid scan input: has length of Output OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsrequested_shape[i] >= -1At most one dimension can be -1.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hA dimension cannot be less than -1, got input_shape_size != -1onnxruntime::ReshapeHelper::ReshapeHelperThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size == sizeThe input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0i < input_shape.NumDimensions(), requested shape:unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeA shape tensor must be a vector tensor.onnxruntime::Reshape_1::Reshape_1D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.onnxruntime::IdentityOp<1>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatoronnxruntime::IdentityOp<0>::Computeonnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.Unsupported output datatype with size: D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::`anonymous-namespace'::ConstantOfShape::Computet_proto_p->dims()[0] == 1Must have a single dimension of 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims_size() == 1Must have a single dimensionD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hutils::HasDataType(t_proto)Unsupported value attribute datatype: !utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtostoll argument out of rangeinvalid stoll argumentstod argument out of rangeinvalid stod argumentstoull argument out of rangeinvalid stoull argumentAttribute saturate is only used for cast to float 8 types.onnxruntime::`anonymous-namespace'::Cast::CastAttribute to is not set.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorNaNsnprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().onnxruntime::`anonymous-namespace'::CastToStringsnprintf_result > 0snprintf() failed with return value: %.8gINF-INFmaxsplitStringSplitdelimiterRegexFullMatchpatternInvalid regex pattern: re_.ok()onnxruntime::RegexFullMatch::RegexFullMatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\regex_full_match.ccStringConcatdetect_positivedetect_negativeNull input ptrUnsupported approximation_algorithm: approximatetanhnonebad allocationAffineGridAffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5.for input dimension of static_cast(grid_dims.NumDimensions()) == data_dims + 2grid dimensions must be D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cconnxruntime::GridSample::ComputeLast dimension of grid: , expect onnxruntime::GridSample::GridSamplepadding_mode "bicubicbilinearzeros" not supported, expect bilinear, nearest or bicubicreflectionpadding_mode" not supported, expect zeros, border or reflectionborderonnxruntime::GridSample::ComputeOnly support GirdSample in 4-D or 5-D cases.mode "" not supported, expect linear, nearest or cubicD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.honnxruntime::GridSample::GridSampleOnly 4-D or 5-D tensor is supportedgrid_dims[grid_dims.NumDimensions() - 1] == data_dims does not match input batch size input_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5grid_dims[0] == NGrid batch size mode_ != CubicOnly support GridSample Cubic mode in 4-D cases.SafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroinverseonesidedonnxruntime::DFT::ComputeUnsupported 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.Unsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::STFT::ComputeSTFTDFTonnxruntime::discrete_fourier_transformdft_length must be a scalar value.dft_length must be greater than zero.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.cconnxruntime::dft_bluestein_z_chirpUnsupported bit size.signal_components == 1 || signal_components == 2signal shape must end in 1 (real) or 2 (real, imaginary).If both frame_length and window are set, then the size of the window must be equal to the frame_length.onnxruntime::short_time_fourier_transformEnsure that the dft size is smaller than the signal.frame_length == window_lengthwindow_size <= signal_sizetensor->Shape().Size() == 1ratio input should have a single value.D:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.honnxruntime::signal::get_scalar_value_from_tensoronnxruntime::bit_reverse@ `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_?Reduction on all axes, output size should be 1.onnxruntime::ValidateFastReduceRKRonnxruntime::ValidateNoTransposeReducecount == 1Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::CommonFastReduceCopyonnxruntime::ValidateKeepDimsfast_shape[0] == output.Shape().Size()Output size mismatch.fast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape.size() == 3Only works on matrices with three dimensions.fast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKlast_loop_size > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccmust be overloaded.projected_index.size() > 0Only works on matrices with two dimensions.onnxruntime::ValidateMustBeOverloadedonnxruntime::ValidateFastReduceKRfast_shape.size() == 2onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptylast_loop_red_size > 0onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()select_last_indexD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_kernel_base.honnxruntime::check_and_reduce_empty_set_inputaxes.empty()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseAxes input and attribute should not both be present for reduction.`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset cubic_coeff_around_prefer_floor and anti-aliasing is set to exclude_outside or 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 antialiasD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.h(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.keep_aspect_ratio_policystretch model uses the deprecated attributeextrapolation_valueonnxruntime::UpsampleBase::UpsampleBaseinfo.GetAttr("mode", &mode).IsOK()'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 'Linear' mode only supports: * 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 onnxruntime::UpsampleBase::ParseScalesDatascales size should be greater than 0.all values in axes should be less than rank of the datanearest_mode:[ceilScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than 0.onnxruntime::UpsampleBase::ScalesValidationUpsample operatorResize operatorpytorch_half_pixelonnxruntime::UpsampleBase::StringToKeepAspectRatioPolicytf_crop_and_resizetf_half_pixel_for_nncoordinate_transform_mode:[half_pixel_symmetricround_prefer_ceilonnxruntime::UpsampleBase::StringToCoordinateTransformationMode. It can only be (default) or onnxruntime::UpsampleBase::StringToUpsampleModemode attribute is not_smallernot_largerkeep_aspect_ratio of [] is not supported!((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO by Attempting to broadcast an axis by a dimension other than 1. D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Appendaxis == 1 || axis == largestonnxruntime::BroadcastIterator::Initonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Invalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::TensorAllocator::TensorAllocatorBroadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0largest <= 1onnxruntime::Broadcaster::Broadcaster is invalid.Can broadcast 0 by 0 or 1. onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:InputBroadcaster can only start at span boundary!offset % span_size_ == 0inputCount >= 1onnxruntime::Min_6::ComputeUnsupported X type: Must have 1 or more inputsdata_n.Shape() == shapeonnxruntime::Max_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeBitwiseOrBitwiseXorBitwiseAndBitwiseNotonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Compute(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfmodfmod must have value either 0 or 1input_size < std::numeric_limits::max()fmod attribute must be true for floating point typesonnxruntime::mod_internal::CallModImpl::operator ()ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::Mean_6::Computedata_1.Shape() == shapeshape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandonnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeUnsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperonnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.input_count >= 1onnxruntime::Sum_6::Computeonnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::Sum_6::Computecur1 == end1onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()directiononnxruntime::BitShift::BitShift'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'LEFTRIGHTpinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesNo kernel shape is set.ceil_modecount_include_padD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()Unknown AutoPadType Stringonnxruntime::StringToAutoPadType`InlinedVector::at(size_type)` failed bounds checkInput dimension cannot be less than 3.onnxruntime::PoolBase::Computeonnxruntime::PoolProcessContext::initD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hkernel_shape num_dims is not compatible with X num_dims.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.kernel_shape[dim] > 0Pad should be smaller than kernel.info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()onnxruntime::MaxPoolV8::ComputeImplUnsupported pooling size : onnxruntime::LpPoolV18::ComputeUnsupported kernel dimension : onnxruntime::Pool::Computeonnxruntime::LpPoolV18::LpPoolV18D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.honnxruntime::AveragePoolV19::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.hattr->has_tp()onnxruntime::Optional::OptionaltypeOptional op must have a TypeProto in the 'type' attribute if the attribute is presentonnxruntime::PropagateInputOrtValueToFirstOutputD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOptionalHasElementOptionalGetElementcan only be of type(tensor) or (seq(tensor))The TypeProto attribute in the Optional op Only Optional type OrtValues containing Tensors and Sequence Tensors are acceptableonnxruntime::Optional::ComputeOOptionalTrying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::OptionalGetElement::ComputeIf 'num_outputs' is specified, the 'split' input should not be provided.num_outputsD:\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 value in 'split' attribute. All values must be > 0splitonnxruntime::SplitImpl::Computesplit_tensor->Shape().NumDimensions() == 1The split tensor must be a vector tensor.Cannot split using values in 'split' attribute. Axis=D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cc Axis= NumOutputs=Invalid num_outputs value of . Size of dimension being split is Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape=D:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopydst.DataType() == src.DataType()src and dst types must matchonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()total_num_elements_to_copy >= 0copy shape must have non-negative sizeonnxruntime::StridedCopyonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::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 == lastCPU 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'.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'.CPU execution provider: BFloat16 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: 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'.ScatterElementsScatter 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=CPU execution provider: BFloat16 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: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.onnxruntime::ScatterDataScatterElements op: input tensor must have at least one dimensionmuladdIndices type is not supported.Indices dim= at pos=reductionD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::ScatterCPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU 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: 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 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: 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'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.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'.input tensor and indices tensor must has rank larger than 0. input shape: , indices shape: ScatterNDupdates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: last dimension of indices must not be larger than rank of input tensoronnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()D:\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 ()invalid indice found, indice = onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()Col2Imstrides_.empty()strides_.size() == image_dim_numberpads_.empty()dilations_.empty()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.honnxruntime::Col2Im::Col2Imonnxruntime::Col2Im::Computedilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.size of 'strides' 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.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.ccUperiodicoutput_datatypeMelWeightMatrixBlackmanWindowHammingWindowHannWindowonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cconnxruntime::CreateMelWeightMatrix::operator ()lowest_index >= 0 && lowest_index < num_spectrogram_binslower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Compute'is defined.No attribute with name:'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ScaledTanh::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ElementWiseRangedTransform::CreateD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.ccunknown kernel typeand will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign The existing summation for max mode and sampling ratios besides 1 is incorrect Sampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scalewill be different.D:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthsampling_ratioavg specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase::RoiAlignBaseNumber of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1RoiAlignInvalid input var: 0th dimension != Invalid input var: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.his_spatial_onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormtraining_modemomentumTraining mode only supports spatial BNforwardD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccRNNonnxruntime::RNN::Computereversebidirectionalallowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - activation_alphaactivation_betaactivationsinfo.GetAttrs("activations", activations_).IsOK()info.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNND:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()cliplayoutAffineonnxruntime::DeepCpuLstmOp::ComputeD:\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 yetsigmoidinput_forgetLSTMactivation_func_names.size() == static_cast(num_directions_) * 3D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0onnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of 'info.GetAttr("direction", &direction).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.h'. Must be one of 'forward', 'reverse', or 'bidirectional'.onnxruntime::IAllocator::ValidatedCalcMemSizeForArray * Invalid size requested for allocation: linear_before_resetD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()onnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccGRU operator does not support double yetGRUonnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of onnxruntime::rnn::detail::SafeRawConstPointercur + size <= endonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())A + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endlda >= K && ldb >= K && ldc >= NCan not get unpacked span from prepacked weightsonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_onnxruntime::DeepCpuGruOp::ComputeImplInput tensor should have a rank of at least 2IsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cconnxruntime::Trilu::Computeonnxruntime::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()TriluD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hupperAxis tensor should be 0D or 1DAxis tensor must be provided to the CumSum opCumSumAxis tensor should be of type `int32_t` or `int64_t`exclusiveonnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cc. N=Hardmax inputs N, D and N * D must be < D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=onnxruntime::Hardmax::Computeinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::ComputeindicesGatherNDBase PrepareForCompute: Input count mismatchbatch_dimsGatherNDinvalid index found, index = indices tensor must has rank larger than 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor data type not supportedX input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hCRDinfo.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseblocksizeAttribute blocksize is not set.onnxruntime::DepthToSpace::ComputeUnsupported input type in DepthToSpace op: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccUnsupported input type in SpaceToDepth op: onnxruntime::SpaceToDepth::Computeonnxruntime::DepthToSpace::DepthToSpaceDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedSpaceToDepth requires input width to be a multiple of block_sizeDepthToSpace requires input depth to be a multiple of (block_size * block_size)SpaceDepth ops require a 4-D input. Provided rank: SpaceToDepth requires input height to be a multiple of block_sizeGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'onnxruntime::GatherElements::GatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GetIndexD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.ccGatherElements op: Out of range value in index tensorGatherElements op: Unsupported tensor type, size:onnxruntime::core_implInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the first one is emptyD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.ccTile doesn't have an implementation yet for the type: onnxruntime::Tile::Compute'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor'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()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attributeStarts must be a 1-D arrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of onnxruntime::SliceBase::ComputeCannot 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::FillVectorsFromInputonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputenew_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeonnxruntime::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 scalarsinput != nullptr0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1seedonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeD:\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 shapeMeanVarianceNormalizationonnxruntime::MeanVarianceNormalization::ComputeNo valid axes are specified. This is not handled now.normalize_varianceD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\mean_variance_normalization.cconnxruntime::`anonymous-namespace'::ComputeMeanVarianceNormalization2DX and Y must both have M * N elements.across_channelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4biasbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()size_ % 2 == 1size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()sizeinfo.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transB", &temp).IsOK()left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasleft[i] >= 0right[i] >= 0 N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ >= 0 && N_ >= 0onnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.ccmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.hmin should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax 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 ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()invalid expand shape->equationMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()onnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hEinsumEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetEinsum op: An implementation for the input type OneHotInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar.onnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeDepth is negative.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeop_kernel_info.GetAttr("k", &k_temp).IsOK()kD:\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()sortedoutput count mismatch, expected 2 outputs to be present for TopK operator] should not be greater than specified axis dim value [input count mismatch, expected 1 input - the tensor to be processedk argument [k tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuevalue of k must not be negativeData type is not supported in this build.UniqueUnsupported tensor type of delta in Range operator can not be zero!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:SequenceLengthSonnxruntime::GetSeqIdxSequenceAt) specified for sequence of size (D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceAt::ComputeInvalid sequence index (Unsupported 'dtype' value: SequenceEmptySequenceInsertonnxruntime::SequenceEmpty::Compute), input tensor data type (onnxruntime::CloneTensorSequenceEraseData type of the input tensor MUST be same as that of the input sequence. Sequence data type (num_inputs >= 1SequenceConstructViolation of the requirement that all input tensors must have the same data type.onnxruntime::SequenceConstruct::Compute) != split_dim_size (SplitToSequenceInvalid data type for split tensor split_size_sum (onnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputsplit_scalar > 0Split should be > 0Invalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Got nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeConcatFromSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cckernel_shape is not compatible with W shape. X: kernel_shape num_dims is not compatible with W num_dims. channels_last: X num_dims does not match W num_dims. group: output_paddingoutput_shapeInvalid input shape: filter_number: filter number not equal to input channel number.group count is <= 0 num_input_channels: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeonnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSET W: kernel_shape: onnxruntime::ConvAttributes::ConvAttributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvTranspose::DoConvTransposeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.Input 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: Not enough elements in pads. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeoutput and sum shape must matchonnxruntime::Conv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cckernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]onnxruntime::MaxUnpool::MaxUnpoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hstrides_.size() == kernel_shape_.size()Index tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeonnxruntime::MaxUnpool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.cc inferred output shape:output_shape is smaller than minimum required. output_shape:Detonnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccMatrix dimensions are not equal. Square matrix is expectedCompressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()size overflowonnxruntime::Compress::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cconnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrbatch_axistime_axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()ReverseSequenceonnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of sequence_lens shape must be {batch_size}. Got:. batch_size=D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccbatch_axis < 2Invalid batch_axis of . Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axis != time_axistime_axis and batch_axis must have different values but both are time_axis < 2Invalid time_axis of Invalid sequence length: . Value must be in range [0,T4onnxruntime::QLinearConv::UseSharedPrePackedBuffersprepacked_buffers[0].get() == nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.cconnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeQLinearConv : result scale must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : 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)W_zero_point_data[i] == W_zero_point_valueQLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.IsValidQuantParam(W_zero_point, M)QLinearConv : filter zero point shape invalidonnxruntime::QLinearConv::ComputeOffsetonnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.ConvIntegerIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.cconnxruntime::ConvInteger::Computeonnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.cc:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::Locale - en-USis_case_sensitiveattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizerWideCharToMultiByte failed errcode = onnxruntime::string_normalizer::Utf8ConverterWindows::ComputeRequiredSizeToUtf8MultiByteToWideChar failed errcode = onnxruntime::string_normalizer::Utf8ConverterWindows::from_bytesNONEattribute case_change_action has invalid valuelocalestopwordscase_change_actionattribute case_change_action is not setLOWERUPPERonnxruntime::StringNormalizer::Computeonnxruntime::StringNormalizer::Compute::::operator ()onnxruntime::StringNormalizer::Compute::::operator ()Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStringNormalizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\string_normalizer.ccOnly one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeInvalid roi input index.onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::ComputeResize: 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.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-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.Upsample: input tensor's dimension does not match the scales.axes should be less than output_dims.size() Output dim value: Input dim value: Dimension: Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::UpsampleBase::ParseSizesDataUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionCannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeResize: input/output value is nullptrUpsample: input shape needs to be at least a single dimension.onnxruntime::Upsample::BaseComputeResize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::UpsampleNearestmax_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0max_gram_length >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_countsNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= std::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })weights must be of equal size but ngram_indexes size: ngram_indexesNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowed!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()Got weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specifiedonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDFTfIdfVectorizermode is requiredmin_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0TFIDF is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNoneonnxruntime::ngram_details::PopulateGrams id: Duplicate ngram detected, size: p.first->second->id_ == 0(items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0.-gramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()op_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hEyeLikeEyeLike : Input tensor dimension is not 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Initonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Compute but has batch size of position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::ExecuteD:\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_mlvalueMaxRoiPoolonnxruntime::RoiPool::ComputeR->Shape()[1] == 5roi_batch_id >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id < batch_sizeonnxruntime::RoiPool::RoiPoolpooled_shapepooled_shape.size() == 2D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_width_ > 0pooled_height_ > 0spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormp_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hInvalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Mismatch between input data and B: size of B != input channel count Invalid input data: number of dimensions is less than 3: vs. Invalid input scale: number of dimensions is not 1: D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::Computeinfo.GetAttr("mean", &mean_).IsOK()meanInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("scale", &scale_).IsOK()scaleinfo.GetAttr("low", &low_).IsOK()lowsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikehighonnxruntime::RandomNormalLike::RandomNormalLikeonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()onnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::MultinomialComputeCould not infer data type from input tensor with data type D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::ComputeOutput type not supported in this build: num_classes is < 1batch_size is < 1Invalid data type of num_samples is < 1onnxruntime::contrib::RegisterQuantizationKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsdefault_tensorinfo.GetAttr("default_string", &default_string_).IsOK()default_int64D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_stringclasses_stringsonnxruntime::ml::LabelEncoder::LabelEncoderkeys_stringsvalues_floatsvalues_strings_UnusedInput of tensor(int64) must have output of tensor(string)keys_floatsinfo.GetAttr("default_int64", &default_int_).IsOK()Input of tensor(string) must have output of tensor(int64)values_int64sdefault_floatkeys_int64sonnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2num_keys == num_valuesonnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2LabelEncoder could not unpack tensor attribute LabelEncoder is missing attribute onnxruntime::ml::GetAttributeonnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2values_tensorKeys and values must have the same length.keys_tensoronnxruntime::ml::GetDefaultLabelEncoder could not unpack default tensor result.IsOK()onnxruntime::ml::LabelEncoder_4,class std::allocator >,short>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,double>::LabelEncoder_4keys.size() == values.size()onnxruntime::ml::LabelEncoder_4<__int64,double>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,__int64>::LabelEncoder_4 attributes in LabelEncoder The ) must have the same length. (name: and the number of However, the number of key is onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2values is LEAFBRANCH_LTBRANCH_LEQAVERAGESUMSOFTMAXSOFTMAX_ZEROBRANCH_EQLOGISTICBRANCH_GTEBRANCH_GTInput shape needs to be at least a single dimension.base_valuesnodes_treeidsnodes_truenodeidsnodes_modesnodes_nodeidsnodes_hitratesnodes_missing_value_tracks_truenodes_falsenodeidsnodes_featureidsnodes_values_as_tensorclass_weights_as_tensortarget_weightsbase_values_as_tensornodes_hitrates_as_tensortarget_treeidstarget_nodeidsnodes_valuestarget_idsMINTreeEnsembleRegressor is already there. in tree base_values.empty()nodes_modes.size() < std::numeric_limits::max()nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()) is different from n_nodes (Number of nodes in nodes_ (A node cannot point to itself: (falsenode). (truenode).Unable to find node Node (coor.node_id >= 0 && coor.node_id < n_nodes_)onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggpredictions.size() == predictions2.size() but input tensor has One path in the graph requests feature onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg features. (weights).TreeEnsemble only works on 1D, 2D tensors.it->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressortarget_weightspost_transformD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.htarget_weights_as_tensorbase_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon::Init-n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()onnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computen_targetsaggregate_functiontarget_class_ids.size() == target_class_treeids.size()target_class_weights.empty() || target_class_ids.size() == target_class_weights.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommon::AddNodes with flags but got Tree id mismatch. Expected at position False node must always be the next node, but it isn't at index onnxruntime::ml::detail::TreeEnsembleCommon::AddNodesclass_idsclass_treeidsX dims is empty.classlabels_stringsclasslabels_int64sbase_values_as_tensorclass_nodeidsclass_weightsTreeEnsembleClassifieronnxruntime::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>::MergePredictiononnxruntime::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::TreeEnsembleCommon<__int64,float,float>::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclasslabels_int64sonnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::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::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresZipMap] != number of classlabels[Input features_per_batch[Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsonnxruntime::ml::ZipMapOp::ZipMapOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.ccMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::SVMRegressor::ComputeInvalid argumentsupport_vectorsUnexpected mode:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccone_class!coefficients_.empty()coefficientsrhoonnxruntime::ml::SVMRegressor::SVMRegressorn_supportsRBFPOLYLINEARSVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonkernel_paramskernel_typeonnxruntime::IAllocator::AllocArrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hSVMClassifierinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classUnsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0Unexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Either both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.Scale size: () != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetonnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()Scalercats_stringscats_int64sonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedUnknown Category and zeros = 0.OneHotEncoderNormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1MAXUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got Input shape had more than 2 dimension. Dims=D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressortargetsinterceptsLinearRegressorshape_size == out.size()onnxruntime::ml::CastInputToFloatD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.size() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of LinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierImputerimputed_value_floatsimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeEmpty value of imputed values.Empty input dimensions.) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizerDictVectorizeronnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()CategoryMapperInput of string must have output of int64Input of int64 must have output of string onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hnum_entries == int_categories.size()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::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccSPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSEInvalid input type of value: 0.fUnexpected CAST_TO value of map_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::mapBinarizer is NaNInput data with index: thresholdArrayFeatureExtractorInvalid argument: X input has empty dimensions.) >=Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] (Unsupported typecur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++ is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorThe subgraph in 'body' requires inputs but Scan was only given onnxruntime::scan::detail::Info::Infonum_variadic_inputs == num_subgraph_inputsSubgraph must have the shape set for all outputs but did not.onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::AllocateOutputonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.ccdirections.size() == num_entriesNumber of entries in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsvalidInvalid values in 'onnxruntime::LayerNormImpl::ComputeImpl. Size of scale and bias (if provided) must match this. Got scale size of and bias size of Size of X.shape()[axis:] == op_kernel_info.GetAttr("axis", &axis_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cconnxruntime::LayerNormImpl::LayerNormImplA + (M * K) <= A_endweights.quant_para_onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameQuantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)scaledtanhhardsigmoidleakyreluthresholdedrelusoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got elusoftsignInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInput B must have shape {Input sequence_lens must have shape {Input W must have shape {Input R must have shape {affinereluInvalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {Input X must have 3 dimensions only. Actual:}. Actual:Input initial_c must have shape {Input P must have shape {onnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessLeft shape: The override dims are not compatible with given tensor's shape. Right shape: right.Shape().Size() == right_shape_override.Size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Left shape override: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputleft_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputs Right shape: Ranks of pair-wise operands must be equal. Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()left_dim == right_dimEinsum op: Input shapes do not alignonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputAnother operand has a dim value of . The shape is: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsonnxruntime::EinsumComputePreprocessor::RunFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript provided...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand The broadcasted dimensions of the inputs are incompatibledim_iter == ranknum_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsrank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal for input shape Cannot parse the diagonal elements along dims onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal The innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Data types of the inputs must match for MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulIncompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Einsum op: Exception during MatMul operation: FDynamicTimeWarpingCurrently input rank must be 2, or (3 with first dim equal to 1), but got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tensor\dynamic_time_warping.ccInvalid trace value: rank == 2 || (rank == 3 && input_dims[0] == 1)onnxruntime::contrib::DynamicTimeWarping::Computestep must greater than zero!stepdiminfo.GetAttr("size", &temp_size).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/tensor/unfold.honnxruntime::contrib::UnfoldTensor::UnfoldTensorstep_ > 0onnxruntime::contrib::UnfoldTensor::Computedim < rankinput rank: is not bigger than attribut specified dim: input_dims[dim] >= size_dimsize: is less than unfold size:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tensor\unfold.ccUnfoldTensorInverseSkipSimplifiedLayerNormalizationonnxruntime::contrib::SkipLayerNorm::SkipLayerNormepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::ComputeLast dimension of beta and input does not matchbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchgamma is expected to have 1 dimension, got Last dimension of bias and input does not matchbias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormlast two dimensions of skip needs to be same as inputinput 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 dimensionsskip is expected to have 3 or 2 dimensions, got Scaleinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeBias size (ImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (onnxruntime::contrib::ImageScaler::ImageScalerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceAttribute border needs to be specified with four border elements, got input dimensions instead) + bottomBorder () needs to be greater than or equal to the topBorder (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) + scale_[0] () + scale_[1] (Croponnxruntime::contrib::Crop::ComputeInput's height () + rightBorder () needs to be greater than or equal to the leftBorder (Input's width (do_rotaryrotary_embedding_dimqkv_hidden_sizespast_present_share_bufferInput 'attention_bias' is expected to have 4 dimensions, got Input 'attention_bias' dimension 0 should be batch_size or 1, got Input 'attention_bias' dimension 1 should be same as number of heads or 1, got info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/attention_base.hInput 'query' is expected to have 3 or 5 dimensions, got onnxruntime::contrib::multihead_attention_helper::CheckInputsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/multihead_attention_helper.honnxruntime::contrib::DecoderMaskedMultiHeadAttention::DecoderMaskedMultiHeadAttentionoutput_qkonnxruntime::contrib::DecoderMaskedMultiHeadAttention::ApplyAttentionWithBeamsDecoderMaskedMultiHeadAttention does not support attention bias for cross-attentionpast_present_share_buffer_past_key != nullptr && past_value != nullptrIf beam width is greater than 1, then cache indirection buffer MUST be presentExpect rank of key and value be same, and either 3 or 4Input 'query', 'key' and 'value' shall have same dim 0 (batch_size)Input 'query' and 'key' shall have same dim 2 (hidden_size)Input 'key' and 'value' shall have same dim 1 (kv_sequence_length)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.hExpect rank of query be 3 for packed kvExpect rank of key be 5 for packed kvExpect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvInput 'bias' is expected to have 1 dimension, got Input 'bias' length is expected to be 2 * hidden_size + hidden_size_v, got Input 'key_padding_mask' shape is not expected.onnxruntime::contrib::AttentionCPUBase::ApplyAttentionInput 'value' shall absent when 'key' is absentInput 'past_key' and 'past_value' shall be both present or both absentPacked qkv of 3D BS3NH format is not support by MultiHeadAttentionInput 'bias' shall be empty when packed kv is usedInput 'attention_bias' dimension 2 should be same as sequence_length, got Input 'attention_bias' dimension 3 should be same as total_sequence_length, got DecoderMaskedMultiHeadAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\decoder_masked_multihead_attention.ccInput sequence length should be 1 to use DecoderMaskedMultiHeadAttention. Actual length is QK head size should be same as V head size to use DecoderMaskedMultiHeadAttentionDecoderMaskedMultiHeadAttention only supports no mask or 2D key padding mask of shape [batch, total_seq_length] currentlyonnxruntime::contrib::DecoderMaskedMultiHeadAttention::Compute4D mask in attention cpu kernel is not supportedInput 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). Input 'past_key' dimension 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got past_sequence_length tensor must be of one element when past_present_share_buffer is setInput 'past_key' dimension 0 should be batch_size, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'key' and 'value' shall have same shape (batch_size, num_heads, kv_sequence_length, head_size)Expect 'query' shape (batch_size, sequence_length, num_heads, 3, head_size) for packed qkvInput 'past_key' is expected to have 4 dimensions, got Input 'past_value' is expected to have 4 dimensions, got max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_info.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()onnxruntime::contrib::BifurcationDetector::BifurcationDetectorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_size_ > 0min_ngram_sizesrc_tokens_len >= prev_suffix_match_idx_dataonnxruntime::contrib::BifurcationDetector::Computepred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)BifurcationDetectorscores_dims[0] == batch_sizescores_dims.size() == 2NGramRepeatBlockTidonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Computeinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockinfo.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_size_ > 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_sizeuse_approximationD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::ComputeCDistonnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDisteuclideansqeuclideaninfo.GetAttr("metric", &metric).IsOK()metricThe first input of CDist kernel has wrong shape: Input shape dimensions mismatch:The second input of CDist kernel has wrong shape: D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestNumber of dimensions for crop size should be exactly 1Null crop_size_ptrCropAndResizeConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskonnxruntime::contrib::MaxpoolWithMask::ComputeQuantized and packed blob size differ from expected!(size_t)blob_len == buf_sizeOperator MatMulFpQ4 not yet supported on this hardware platform.buf_size > 0Right hand side of MatMulFpQ4 must be a 2D matrix!b_shape.NumDimensions() == 2onnxruntime::contrib::MatMulFpQ4::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_fpq4.ccSecond input of MatMulFpQ4 must be a 1D blob!blob_shape.NumDimensions() == 1blk_quant_typeMatMulFpQ4GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,int>::CopyDataAndDequantize::::operator ()onnxruntime::contrib::GatherBlockQuantized,__int64>::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,int>::GatherBlockQuantized'block_size' must be 2's power and not less than 16.block_size_ >= 16 && ((block_size_ - 1) & block_size_) == 0gather_axisquantize_axisonnxruntime::contrib::GatherBlockQuantized,int>::Computeonnxruntime::contrib::GatherBlockQuantized,__int64>::ComputeUnsupported dequantized type: onnxruntime::contrib::GatherBlockQuantized,__int64>::Computeonnxruntime::contrib::GatherBlockQuantized,int>::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\gather_block_quantized.ccindices_val >= -gather_axis_dim && indices_val < gather_axis_dimonnxruntime::contrib::GatherBlockQuantized,__int64>::PrepareForComputeonnxruntime::contrib::GatherBlockQuantized,__int64>::PrepareForComputeonnxruntime::contrib::GatherBlockQuantized,int>::PrepareForComputescales and zero_points must have the same rank.scales and zero_points must have the same shape.onnxruntime::contrib::GatherBlockQuantized,int>::PrepareForComputedata and scales do not match shapes.data and scales must have the same rank.onnxruntime::contrib::GatherBlockQuantized,__int64>::GatherBlockQuantizedonnxruntime::contrib::GatherBlockQuantized,int>::GatherBlockQuantizedonnxruntime::contrib::MatMulBnb4::ComputeMatMulBnb4Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.quant_type_ == FP4 || quant_type_ == NF4quant_typeStatus::OK() == info.GetAttr("quant_type", &quant_type_)Status::OK() == info.GetAttr("N", &N_)Status::OK() == info.GetAttr("block_size", &block_size_)onnxruntime::contrib::MatMulBnb4::MatMulBnb4D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_bnb4.ccStatus::OK() == info.GetAttr("K", &K_)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dequantize_blockwise_bnb4.honly block size 16, 32, 64, 128, 256 are supported.quant_type == FP4 || quant_type == NF4onnxruntime::contrib::DequantizeBlockwiseBnb4920k2ʾM?5=qx=$>|>:>>?9??;*??>?*>>**onnxruntime::contrib::MatMulNBits::MatMulNBitsonnxruntime::contrib::MatMulNBits::MatMulNBitsOnly 4b quantization is supported for MatMulNBits op, additional bits support is planned.nbits_ == 4D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_nbits.cconnxruntime::contrib::MatMulNBits::ComputeBUnpackedcolumn_wise_quant_onnxruntime::contrib::MatMulNBits::ComputeBUnpackedRow-wise quantization is not supported for nowonnxruntime::contrib::MatMulNBits::Computeonnxruntime::contrib::MatMulNBits::ComputeTransposeMatMulinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::ComputekeysMurmurHash3positive is out of bounds of lhs_right: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()COO m index: is out of bounds of out_left: COO k index: Currently support only COO and CSR(x64) formatsOuter size must be M + 1Expecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesExpecting COO 2-D indices shapeCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cconnxruntime::contrib::SparseToDenseMatMul::ComputeCurrently supporting only 2-D matricesSparseToDenseMatMulchar_embedding_sizeconv_window_sizeembedding_sizeConv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Conv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: WordConvEmbeddingD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2. Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: attribute mincharnum must have a positive valueattribute mincharnum is not setmincharnumattribute pad_value is not setpad_valueD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute mark is not setmarkTokenizerMatch contains invalid utf8 chars: onnxruntime::contrib::Tokenizer::SeparatorExpressionTokenizerInput string contains invalid utf8 chars:Input string contains invalid utf8 chars: Can not digest tokenexp: Can not digest separators: !char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezation!separators.empty()separators must not be empty!tokenexp.empty()Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is settokenexpseparatorsmincharnum > 0Input dimensions are either [C] or [N][C] allowedtensor(string) expected as inputonnxruntime::contrib::Tokenizer::TokenExpression, am_attn_size}, Got:Attention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{}. Got: Attention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape { found!], while Attention mechanism memory sequence lengths value must in (0, onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImpl should be a scalar. Got shape of Node input is requiredSamplingparameters_.model_type == IGenerationParameters::kModelTypeGptD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.hmax_lengthmin_lengthonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenInput 'input_features' is expected to have 3 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got onnxruntime::contrib::transformers::GreedySearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchBase::Initializeinit_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_onnxruntime::contrib::transformers::Sampling::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.ccencoderinfo.GetAttr("encoder", &proto).IsOK()init_decoderdecoderinfo.GetAttr("decoder", &proto).IsOK()gpt_subgraph_ == nullptronnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfoinit_run_gpt_subgraph_ == nullptrNot ImplementedSubgraph SessionState was not found for 'decoder' attribute.decoder_session_stateonnxruntime::contrib::transformers::Sampling::Computedecoder_feeds_fetches_manager_init_run_decoder_session_stateInput 'input_ids' is expected to have 2 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got MRotaryEmbeddingthe same shapeInputs 'cos_cache' and 'sin_cache' are expected to have specifiednum_heads must be provided if rotary_embedding_dim is head_sizerotary_embedding_dim must be less than or equal to batch_size, got Input 'position_ids' dimension 0 should be of size sequence_length, got Input 'position_ids' dimension 1 should be of size max_sequence_length, got Input 'cos_cache' dimension 0 should be same as head_size / 2 or rotary_embedding_dim / 2, got Input 'cos_cache' dimension 1 should be same as interleavedis_packed_batchingnum_heads must be provided if rotary_embedding_dim is specifiednum_heads > 0onnxruntime::contrib::RotaryEmbedding::RotaryEmbeddingonnxruntime::contrib::RotaryEmbedding::RotaryEmbeddingInput 'x' is expected to have 3 or 4 dimensions, got dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 Input 'cos_cache' is expected to have 2 dimensions, got Input 'sin_cache' is expected to have 2 dimensions, got onnxruntime::contrib::RotaryEmbedding::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\rotary_embedding.ccUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supportedonnxruntime::contrib::RotaryEmbedding::ComputeSparseAttentionInput 'past_key' dimension 0 should be batch_size Input 'past_key' shall have kv_num_heads, max_cache_sequence_length:max_cache_sequence_length should be no less than total_sequence_length:key_total_sequence_lengths must have shape (batch_size).cos_cache and sin_cache must be passed to SparseAttention when do_rotary = 1head_size shall be a multiple of 16. Got head_size = cos_cache and sin_cache dimension 0 should be same size., max_rotary_sequence_length:max_rotary_sequence_length should be no less than total_sequence_length:cos_cache dimension 1 must be <= head_size / 2 and a multiple of 8.sin_cache dimension 1 must be <= head_size / 2 and a multiple of 8.cos_cache and sin_cache dimension 1 must be the same.head_size must be a multiple of 8. Got head_size = Input 'key' and 'value' shall be both present, or both absent in the case of packed qkv.Input 'key' is expected to have 3 dimensions, got Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'key' shall have same dim 1 (sequence length)Input 'query' and 'value' shall have same shape does not match num_heads and kv_num_headspacked qkv hidden size= total_sequence_length tensor must be of one element.block_row_indices must have shape (num_layout, max_blocks + 1) where num_heads is divisible by num_layout.block_col_indices must have shape (num_layout, max_nnz), where max_nnz <= max_blocks * max_blocks., max_sequence_length deduced from block_row_indices:max_sequence_length should be no less than total_sequence_length:onnxruntime::contrib::sparse_attention_helper::CheckInputsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_helper.hInput 'past_key' and 'past_value' shall have same shapeonnxruntime::contrib::SparseAttentionBase::SparseAttentionBaseD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_base.hkv_num_headsinfo.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0rotary_interleavedsparse_block_sizeinfo.GetAttr("sparse_block_size", &sparse_block_size).IsOK()Input 'query' is expected to have 3 dimensions, got onnxruntime::contrib::SparseAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\sparse\sparse_attention.ccpast_key->DataRaw() == present_key->DataRaw() && past_value->DataRaw() == present_value->DataRaw()onnxruntime::contrib::GQAAttentionBase::GQAAttentionBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\gqa_attention_base.hsoftcapsmooth_softmaxonnxruntime::contrib::GroupQueryAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\group_query_attention.cconnxruntime::contrib::GroupQueryAttention::Computesequence_length shall be 1 when it is not prompt.GroupQueryAttentionlocal_window_sizenum_heads must be a multiple of kv_num_heads. Got num_heads % kv_num_heads == head_size must be a multiple of 8. Got head_size % 8 == Input 'value' is expected to have 3 dimensions, got Input 'query' and 'value' shall have same dim 0 (batch size)Input 'query' and 'value' shall have same dim 1 (sequence length)Input 'value' is expected to have same hidden size as key.BNSH Input 'past_key' and 'past_value' should have same dimension 2 (max sequencelength or past sequence length), got Input 'past_value' shall have kv_num_headsInput 'past_key' and 'past_value' shall be both present or both absent.seqlens_k must be shape (batch_size).head_size shall be a multiple of 16. Got head_size % 16 == cos_cache dimension 0 shall not be less than total_sequence_length.sin_cache dimension 0 shall not be less than total_sequence_length.Input 'cos_cache' and 'sin_cache' shall be both present or both absent.batch_size must be 1 when sequence_length > 1 and past context is given.ORT_DISABLE_FLASH_ATTENTIONonnxruntime::contrib::MultiHeadAttention::MultiHeadAttention", value: "Failed to parse environment variable - name: "TryParseStringWithClassicLocale(value_str, parsed_value)onnxruntime::ParseEnvironmentVariableD:\a\_work\1\s\onnxruntime\core/platform/env_var_utils.hPacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUonnxruntime::contrib::MultiHeadAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.ccFalseTrueMultiHeadAttentionGreedySearchonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.cconnxruntime::contrib::transformers::GreedySearch::Initonnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoonnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccaxis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ExpandDimsaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::ExpandDims::Compute]. Axis is onnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.hnum_beamsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.hlength_penaltynum_return_sequencesEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availableEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptywhisper_encoder_subgraph_ == nullptrt5_decoder_subgraph_ == nullptrwhisper_decoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)Subgraph SessionState was not found for 'encoder' attribute.onnxruntime::contrib::transformers::BeamSearch::Computeencoder_feeds_fetches_manager_encoder_session_stateModel type is not supported.BeamSearchdecoder_output_cross_qkparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperWhisperBeamSearchD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::Initt5_encoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfodecoder_subgraph_.past_present_share_buffer_decoder subgraph: output_cross_qk could only work with past_present_share_bufferinput_tensor_cross_qk_layer_head != nullptrMust specify input cross_qk_layer_headonnxruntime::contrib::transformers::BeamSearchWhisper::Executeonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::CheckInputs'num_return_sequences' has to be smaller or equal to 'num_beams'.onnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchGpt::Executeparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchBase::CheckInputsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Execute, vocab_size is onnxruntime::contrib::transformers::BeamSearchT5::Executeparameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizeno_speech_token_id is out of range, it is D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_whisper.honnxruntime::contrib::transformers::BeamSearchWhisper::Executedecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiononnxruntime::contrib::Attention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.ccRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding.SampleOpTBTCQGemm : scale of y must be null or a scalar or 1D tensor of size 1TATYZTYD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::Computeonnxruntime::contrib::QGemm::CheckInputsQGemm : zero point of input a must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1QGemm : 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 Nonnxruntime::contrib::QEmbedLayerNorm::ComputeBeta 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 1Gamma 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 1QEmbedLayerNormalizationWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::NhwcMaxPool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommononnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::DynamicQuantizeMatMul::ComputeInput RecurrentWeight point must be constantDynamicQuantizeLSTM : Weight zero point must be zeroD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::DynamicQuantizeLSTM::PrePack must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_scaleW_scaleR_zero_pointW_zero_pointonnxruntime::contrib::QAttention::Computeinput zero point must be a scalar or 1D tensor of size 1.input scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccQLinearMulQLinearAddMatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.ccMatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)tensor_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)onnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxopset must be existed in attributes of QlinearSoftmaxD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.cconnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedQLinearSoftmaxonnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13QLinearSigmoidQLinearLeakyReluinput 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)onnxruntime::contrib::QLinearAveragePool::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if giventensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinear Pooling unsupported pooling size!Input x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale) inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)There must be tensor_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_def_count == kExpected_input_countonnxruntime::contrib::QLinearWhere::QLinearWhereD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.ccWrong input type encountered for zero point input def of x, y, zWrong input type encountered for zero point of quantized input @QLinearWhereInput scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_z_scale->IsDataType() Input 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 == 0Each input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0onnxruntime::contrib::QLinearConcat::QLinearConcatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.cconnxruntime::contrib::QLinearConcat::ComputeInput scale is not float for quantized input @QLinearConcatonnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\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::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptrD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPool(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::Compute(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Computeinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1channels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseUnsupported mode 'X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccinfo.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICn_elements > 0' has one dimension but is empty.' is specified but is empty.' must be a vector.Attribute:'(for attribute ''.proto.data_type() == proto_typeUnexpected type (n_dims == 1Attribute 'D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cconnxruntime::ml::GetNumberOfElementsAttrsOrDefaultonnxruntime::ml::GetVectorAttrsOrDefaultonnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AddBiasReshapeonnxruntime::contrib::MaybeTransposeToBNSHAndAddBiasonnxruntime::contrib::MaybeTransposeToBNSHD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_utils.ccInputs 'mask_index' with 4D data shall have is_unidirectional set to falseInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Expect to have present state output when past state input is givenInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthonnxruntime::contrib::AttentionBase::GetPresent q_hidden_size= k_hidden_size=Input 'past' expect k_hidden_size == v_hidden_sizeInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Hidden size of Q, K and V shall be sameqkv_hidden_sizes first element should be same as the second v_hidden_size=bias_dims[0]=Inputs 'past' dimension 2 shall have length of D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.cconnxruntime::contrib::AttentionBase::CheckInputsInputs 'past' dimension 0 shall have length of 2Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 1 dimension 0 should have same length as dimension 2 of input 0hidden_size should be divisible by num_heads:qkv_hidden_sizes attribute should have 3 elementsAttention cannot have both past and attention_biasInput 'weights' is expected to have 2 dimensions, got Input 'input' is expected to have 3 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Input 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got rows[ row[.. [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemory is not in (0, Real memory steps onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccnot support normalize yet.!normalize_D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.ccprefix.size() == narrow(prefix_length)onnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processfilter_valuemin_tokens_to_keeptemperaturetop_pdecoder_start_token_idno_repeat_ngram_sizeeos_token_idpad_token_idD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.ccseed >= 0onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputsvocab_sizeSeed must be >= 0presence_penaltycustommodel_type) shall be no more than max_length <= kMaxSequenceLengthmax_length (max_length > sequence_lengthdims.size() == 2) shall be greater than input sequence length (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.ccinput_ids shall have 2 dimensions. Got repetition_penalty shall be greater than 0, got repetition_penalty > 0.0fcontext != nullptronnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs@D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.cctranslate_token_idtranscribe_token_idmodel_type == IGenerationParameters::kModelTypeWhisperonnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributeslogits_processor shall be a non-negative integer, got logits_processor >= 0num_return_sequences (num_beams >= num_return_sequencesno_timestamps_token_idbeginning_timestamp_token_idstart_of_lm_token_idno_speech_token_idextra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2decoder_input_ids shall have 2 dimensions. Got decoder_dims.size() == 2input_features shall have 3 dimensions. Got dims.size() == 3early_stoppingonnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsnum_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 extra_decoder_tensor_dims[0] == batch_sizenum_beams shall be a positive integer no more than , expecting extra_decoder_tensor first dim not same as batch_size. Got subgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 0 (logits) shall be float or float16 data typesubgraph input 2 (attention_mask) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 0 (input_ids) shall have int32 typesubgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph past state is expected to have 5 dimension, got subgraph past state cannot be nullptrsubgraph input 3 shall be named as past_0, got: past_0subgraph input 2 shall be named as attention_mask, got: attention_masksubgraph input 1 shall be named as position_ids, got: position_idssubgraph logits output is expected to have 3 dimension, got subgraph input 1 shall be named as present_0, got: present_0subgraph output 0 shall be named as logits, got: logitssubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 0 shall have length of 2session_state_ != nullptrSetup must be called before CreateInitialFeedssubgraph input 0 shall be named as input_ids, got: input_idsInvalid 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)onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).input_ids_shape.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.cconnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsBeamSearch op: An implementation for the input type CPU beam search current not support output cross QK.onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsoriginal_decoder_input_ids_shape.NumDimensions() == 2start_token_id >= 0onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.honnxruntime::contrib::SamplingCpuHelper::Sampleonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputselement_type == DataTypeImpl::GetType()input_features_shape.NumDimensions() == 3is_kv_cacheonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedslast_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitslogits_shape.NumDimensions() == 3subgraph output present_key_self_0 is expected to have 4 dimension, got onnxruntime::contrib::transformers::Subgraph::GetParametersD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.cconnxruntime::contrib::transformers::Subgraph::Setupbeam_widthpast_sequence_lengthDecoderMaskedSelfAttentionactivation_params count mismatchunimplemented activation: beta is expected to have size of beta is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shapeposition_ids's first dimension shall be 1 or batch_sizeinput_ids and position_ids shall have same sequence_lengthgamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapestatic_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()beam_idx == num_beams_beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)Batch can only be done if all beams have been generatednext_scores.size() == next_indices.size()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()onnxruntime::contrib::transformers::BeamHypotheses::Outputtop_k <= beams_used_output_scores->IsDataType()onnxruntime::contrib::transformers::BeamSearchScorer::OutputScorestarget.size() == final_scores.size()output_sequence_scores->IsDataType()onnxruntime::contrib::transformers::BeamSearchScorer::Finalizeoutput_sequences != nullptrdecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph output shall have same data type as that of encoder_hidden_statesonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsInvalid hidden_states expension: has_hidden_state_ == falsenumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 0 shall be named as input_ids, got: encoder_attention_maskdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: encoder_hidden_statesonnxruntime::contrib::transformers::T5DecoderSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.ccdecoder_masked_attention shall use with past_present_share_bufferdecoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_states.onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeedsonnxruntime::contrib::transformers::WhisperDecoderSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_decoder.cc + 2 * layers, got:number of outputs expected to be first_present_output_index_ + 3 * layers, got:When outputing cross qk, number of outputs expected to be first_present_output_index_decoder subgraph input 1 shall be named as encoder_hidden_states, got: encoder subgraph output 1 shall be named encoder_hidden_states, got: present_key_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: present_value_self_0encoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeencoder subgraph output 0 (logits) shall be float or float16 data typeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.ccexpect >=6 outputs, got:number of outputs expected to be 2 + 4 * layers, got:encoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: decoder_input_idsencoder subgraph input 2 shall be named as decoder_input_ids, got: encoder subgraph output 0 shall be named as logits, got: expect 2 inputs, got:onnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validateencoder subgraph outputs 1, 2, ... shall have same data typeonnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeedscpu_allocator shouldn't be nullptrencoder subgraph input 2 (decoder_input_ids) shall have int32 typeonnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsexpect 3 inputs, got:onnxruntime::contrib::transformers::T5EncoderSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.ccencoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeonnxruntime::contrib::QlinearBuildLookupTableD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.ccQlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1TORTD:\a\_work\1\s\onnxruntime\lora\adapter_format_utils.cconnxruntime::adapters::utils::MemoryMapAdapterFileThe buffer does not appear to be a valid lora parameter formatonnxruntime::adapters::utils::ValidateAndGetAdapterFromBytesThe buffer fails lora adapter format verificationUnsupported lora format version啀OrtTypeInfo::FromOrtValueD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccTensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeThis TypeProto does not have ValueCase setOrtTypeInfo::FromTypeProtoThe type is not tensor, sparse tensor, sequence, map or optional typeOrtValue is TensorSequence type but has no element Tensor DataType.tensor_data_type != nullptrHipPinnedOpenVINO_GPUWebGPU_BufferOpenVINO_RT_NPUWebNN_TensorCudaPinnedHipSpecified device is not supported.D:\a\_work\1\s\onnxruntime\core\framework\allocator.cc size= alignment=onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator () nmemb=Tensor type is undefinedArgument is not a tensorthe ort_value must contain a constructed tensor or sparse tensorD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShape`anonymous-namespace'::GetIndicesTensorUnsupported indices_format passedvalue_case == ONNX_NAMESPACE::TypeProto::kSequenceTypetype_proto is not of type sequence!D:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.ccOrtSequenceTypeInfo::FromTypeProtovalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypetype_proto is not of optional typeD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.ccOrtOptionalTypeInfo::FromTypeProtovalue_case == ONNX_NAMESPACE::TypeProto::kMapTypetype_proto is not of type map!D:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.ccOrtMapTypeInfo::FromTypeProtodtype_ != nullptrelt_type != nullptrCalculation for Tensor storage size overflowedtensor size overflowonnxruntime::Tensor::Tensorshape.Size() must >=0onnxruntime::Tensor::SizeInBytesTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::Tensor::CalculateTensorStorageSizeTensor shape.Size() must be >= 0D:\a\_work\1\s\onnxruntime\core\framework\tensor.ccsrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopySparseTensorsonnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopyTensorsonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetElementTypeD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::MapTypeHelper::Setonnxruntime::data_types_internal::SequenceTypeHelper::Setelem_proto != nullptronnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::SparseTensorTypeBase::GetElementTypevalue_proto != nullptrexpected a registered ONNX typethisProto->value_case() == TypeProto::ValueCase::kTensorTypep.secondutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatibleonnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())D:\a\_work\1\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleproto != nullptrOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definition(unknown type)UInt4x2tensor type is not supportedsparse tensor type MLDataType for: is not currently registered or supportedfloat16uint64Float8E4M3FNbfloat16Float8E5M2Float8E4M3FNUZInt4x2Float8E5M2FNUZdoublebooluint8int8uint16int16uint32int32onnxruntime::NonTensorTypeBase::IsSequenceCompatibleNot implementedonnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContainerfloat(null)utils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatibleonnxruntime::OptionalTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kOptionalTypethisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->optional_type())utils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::`anonymous-namespace'::CopyDataD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cc dst_size: Must have the same size. Got src_size: to device type: Unable to find a data transfer for copying from device type: onnxruntime::SparseTensor::MakeBlockSparseDataonnxruntime::SparseTensor::MakeBlockSparseStringsDestination should be emptyDestination must have a CPU allocator setonnxruntime::SparseTensor::CopyThis instance should not be empty to be equal to values blocks: Expecting index blocks: Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2onnxruntime::SparseTensor::UseBlockSparseIndicesUse MakeBlockSparseStringsExpecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}Must contain BlockSparse format. Got: Format() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::MakeCsrDataonnxruntime::SparseTensor::MakeCsrStringsExpecting to have at lest 3-D shape. Got:onnxruntime::SparseTensor::ValidateBlockSparseShapesonnxruntime::SparseTensor::AsBlockSparseExpecting one index. Got: Expecting inner index size: rows: Inner and Outer indices must either be both zero or non-zero the same as values size: onnxruntime::SparseTensor::UseCsrIndicesUse MakeCsrStringsOuter index count must be rows + 1 or zero. Got: This method does not expect allocator to be setFormat() == SparseFormat::kCsrconnxruntime::SparseTensor::AsCsrallocator_ != nullptrMust contain Csr format. Contains: dense shape must 2-D. Got: onnxruntime::SparseTensor::ValidateCsrIndicesExpecting two indices. Got: format_data_.size() == 2UUse MakeCooStringsonnxruntime::SparseTensor::MakeCooDataonnxruntime::SparseTensor::UseCooIndicesNot expecting an allocator setFormat() == SparseFormat::kUndefinedThis method should follow a call to constructor that supplies the allocatorExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooStringsformat_data_.size() == 1U must be equal to or twice the values size: onnxruntime::SparseTensor::AsCooExpecting to contain one index, got: onnxruntime::SparseTensor::GetCooIndexDimsSparse format must not be set. Already contains format: Index size: values_count == index_sizethis tensor already has populated sparse_indices must be less than total buffer size: the ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValueMust contain Coo format. Got: Format() == SparseFormat::kCooValues size onnxruntime::SparseTensor::AllocateBufferMust have the same shapeX-device copy of strings not supportedSrc and Dst must be of the same type}onnxruntime::TensorShape::Slice{onnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dimsInvalid tensor shape slice argument.dimstart <= dimend && dimend <= values_.size() for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeFromDimension dimensions. for SizeToDimension. Tensor has Config value is longer than maximum length: ]. It will be overwrittenConfig key is empty or longer than maximum length 128onnxruntime::ConfigOptions::AddConfigEntryD:\a\_work\1\s\onnxruntime\core\framework\config_options.cc] already exists with value [Config with key [ @H8@num_files == files_buffers.size()onnxruntime::SessionOptions::AddExternalInitializersFromFilesInMemory.\\.\An entry for this name has already been added: An OrtValue for this name has already been added: Expecting same size spansonnxruntime::SessionOptions::AddInitializerD:\a\_work\1\s\onnxruntime\core\framework\session_options.cc.//./init_num == values.size()onnxruntime::SessionOptions::AddExternalInitializersReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.Received nullptr for nameReceived nullptr for OrtValue is expected to have type: Requested attribute: expected to be of type: Attribute: No attribute with this name is defined.No attribute with name: but is of type: is defined.Attribute name and type don't matchonnxruntime::NodeIndexInfo::GetNodeOffsetD:\a\_work\1\s\onnxruntime\core/framework/node_index_info.hnode_offsets_index < node_offsets_size_arg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputs. Num args is Invalid arg_num of onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundCPU allocator not foundframe != nullptronnxruntime::OpKernelContext::OpKernelContextExecution frame was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValueD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.ccOpKernel was null: "file_size", " type: */_ORT_MEM_ADDR_/*corrupted protobuf data: tensor shape size() does not match the data size() in protoExternal data type cannot be STRING.onnxruntime::utils::UnpackTensorUnpackTensor: the pre-allocated size does not match the size in protodata overflowUnpackTensor: the pre-allocate size does not match the size in proto, Got string tensor requires allocator to be provided.onnxruntime::`anonymous-namespace'::ReadExternalDataForTensoronnxruntime::utils::UnpackTensorWithExternalDataImplonnxruntime::`anonymous-namespace'::TensorProtoToOrtValueImplonnxruntime::`anonymous-namespace'::GetExternalDataInfoTensor does not have external data to read from.Unexpected number of packed int4 pairsD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccTensorProto: external data size mismatch. Computed size: , external_data.length: External data type cannot be UNDEFINED or STRING.onnxruntime::`anonymous-namespace'::UnpackTensorWithRawDatanullptr == p_dataUnpackTensor: the pre-allocated size does not match the raw data size, expected Unsupported type: onnxruntime::utils::UnpackInitializerDataUnsupported sparse tensor data type of Element_size of: is not supported.onnxruntime::utils::SparseTensorProtoToDenseTensorProto data_type: onnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)Must have a valid data typeInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorSparse indices int32 data size does not match expectedSparse indices int64 data size does not match expectedInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == indices_data.end()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Unsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoConstant node: onnxruntime::utils::CopySparseDataSparse Indices raw data size does not match expected.node.output_size() == 1NodeProto for Constant should have 1 output. Got:raw_dataMissing raw data for tensor proto. Invalid tensor.Initialized tensor with unexpected type: tensor can't contain negative dims has no data attributeslengthlocationonnxruntime::utils::TensorToTensorProtoTensorProtoToTensor() tensor shape mismatch!Memory address tag is not supported by custom external data loader. does not match the tensor size: onnxruntime::utils::LoadExtDataToTensorFromTensorProtostring tensor can not have raw dataonnxruntime::utils::TensorProtoToTensorTensorProto type can not be written into Tensor type are out of bounds or can not be read in full.onnxruntime::utils::GetExtDataFromTensorProtoutils::HasExternalData(tensor_proto)onnxruntime::utils::GetFileContentExternal initializer: offset: size to read: given file_length: Invalid TensorProtoD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cconnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0Failed to add kernel for : Conflicting with a registered kernel with op versions. the since version is: kernel def can't be NULLOp type ( and type ( kernel is not supported in Encountered following errors: (Kernel found kernel However the types are incompatible.Kernel not foundOp with name ( domain ( node_version: kernel start version: kernel_end_version: type_proto != nullptr in the supported version range (node_version: However the types are incompatible. Version mismatch.),This op has been implemented only for the following types (static_cast(first_arg_idx) < actual_inputs.size()D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::`anonymous-namespace'::MatchKernelDefTypes but the node in the model has the following type ( capable of executing this nodefloat8e5m2fnuzfloat8e5m2float8e4m3fnuzORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs onnxruntime::GetCpuPreferredNodes::::operator ()Candidate for fallback CPU execution: float8e4m3fnonnxruntime::GetCpuPreferredNodeskernel_info != nullptrD:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.ccIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by D:\a\_work\1\s\onnxruntime\core\framework\allocator_utils.ccReceived invalid value of arena_extend_strategy onnxruntime::CreateAllocatorInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUExpecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::DenseTensorToSparseCsrD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccUnsupported element size: Support 2-D matrices onlyCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::DenseTensorToSparseCooExpecting indices to be equal the number of values or be twice as manyonnxruntime::sparse_utils::SparseCooToDenseTensor > dense_size: Invalid index: onnxruntime::sparse_utils::SparseCsrToDenseTensorOuter indices must be M + 1. Got: outer_num == (rows + 1)Input must be of COO formatonnxruntime::NodeUnit::InitForSingleNodeQDQ node group cannot have an output from the target node that is consumed by a Q node and a graph output. target node: onnxruntime::NodeUnit::NodeUnit] is not supportedThe QLinear op [D:\a\_work\1\s\onnxruntime\core\framework\node_unit.ccQDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: QDQ node group cannot have an output from the target node being consumed by a Q node and a non-Q node. target node: output idx:QLinearReduceMean, target node: QDQ node group cannot have DQ node that produces a graph output. DQ node: onnxruntime::QDQ::NodeGroup::CanCreateNodeGroupD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsTensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsdata_transfer registered is nullptr.SNPEExecutionProvideronnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphSubgraph has nodes running on device: onnxruntime::utils::UpdateWithParentStreamonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevices, this is not supported yet. while parent graph node running on device: feed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesnode_info.stream_index < 0node_info_vec[i].stream_index < 0onnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoUnsupported OrtValue type to copy between device.onnxruntime::utils::FindDeviceForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedUnsupported OrtValue type.Failed to find allocator for device onnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccinvalid allocator.InternalTestingExecutionProviderop_id is null.kernel_type_str_args is null.kernel_type_str_args entry is null.kernel_type_str is null.onnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasonnxruntime::KernelTypeStrResolver::SaveToOrtFormatop_kernel_type_str_args is null.op_kernel_type_str_args entry is null.onnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()onnxruntime::KernelTypeStrResolver::RegisterOpSchemaOp schema must be available.onnxruntime::KernelTypeStrResolver::RegisterNodeOpSchema' for node type 'Failed to find args for kernel type string '', 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 'D:\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.If type constraint names are available, '. Invalid ORT format model.Failed to find op_id: onnxruntime::KernelTypeStrResolver::ResolveKernelTypeStronnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrargs is null.args entry is null.Duplicate entry for kernel type str: Duplicate entry for op id: 䕀A dso with name onnxruntime::ExLibLoader::LoadExternalLibonnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while loading custom ops with message: has already been loaded.Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO used in the node: Using cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container (weights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be empty!op_type.empty() which is of op type: +allocator_for_caching.get() != nullptronnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weightsduplicated ort_value index:onnxruntime::KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node '), removable attributes removed . Do you have duplicated calls to SessionState::AddInitializedTensor function?for node 'failed at retrieving the removable attributesonnxruntime::SessionState::PruneRemovableAttributesamong attributes: onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::CreateKernels').' ('D:\a\_work\1\s\onnxruntime\core\framework\session_state.ccDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoentry != kernel_create_info_map_.cend()SaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfosession.node_partition_config_filesession.disable_prepackingentry != node_to_subgraph_ss.second.cend()p_op_kernel Attribute: Index:' OpType:Missing session state for subgraph. Node:'subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStateonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator All nodes placed on [ Node(s) placed on [Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::VerifyEachNodeIsAssignedToAnEpImplonnxruntime::VerifyEachNodeIsAssignedToAnEpNode placements]. Number of nodes: onnxruntime::SessionState::CreateSubgraphSessionState) node with name 'Could not find an implementation for SetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_.has_value()onnxruntime::SessionState::GetNodeIndexInfo for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateOnly one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMapping) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: onnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamonnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::GetStreamstream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::CleanUpidx < num_streams_D:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.ccbad variant access (node:'' ep:'Failed to find kernel for The node is not placed on any Execution Provider. in KernelRegistryManager'). found duplicated provider nullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypeonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolveronnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferktsrFailed to verify KernelTypeStrResolver flatbuffers data.D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccexternal_data_loader registered is nullptr.onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksError mapping feeds: D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: onnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsexistsonnxruntime::PartitionOnnxFormatModelImplThe Function Style fusion is deprecated. with domain:Node ', but was not selected was inserted using the NHWC format as requested by D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc 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. cannot be used with this model due to its ONNX opset not being supported by the layout transformer.onnxruntime::GetCapabilityForEPep.context_file_pathep.context_enableD:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.h!provider_type_.empty()onnxruntime::KernelLookup::KernelLookupAPI VERSION provider_type must be specified.D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.hCreate state function failed. Return value:onnxruntime::FunctionKernel::Createonnxruntime::GraphPartitioner::PartitionAOT inlining completed. (No provider specified.) pruned.) functions of (This model does not have any local functions defined. AOT Inlining is not performedonnxruntime::GraphPartitioner::InlineFunctionsAOTsingle_node_compute_func should have 1 element.onnxruntime::PartitionOrtFormatModelonnxruntime::PartitionOnnxFormatModelonnxruntime::PartitionOrtFormatModelImplFailed to generate EP context model since the file 'onnxruntime::CreateEpContextModelBoth ep_context_path and model_path are empty' exist already.onnxruntime::InlineFunctionsAOTImpl_ctx.onnx did not return correct number of compiled functionsonnxruntime::InlineNodesonnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)Creating BinForSize(bin_size) == BinFromIndex(b)D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc bins of max chunk size Creating BFCArena for onnxruntime::BFCArena::BFCArena max_dead_bytes_per_chunk: with following configs: initial_chunk_size_bytes: max_power_of_two_extend_bytes: initial_growth_chunk_size_bytes: arena_extend_strategy: memory limit: Could not find Region for onnxruntime::BFCArena::RegionManager::RegionForentry != regions_.end()onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionp_int < base_int + memory_size_Could not find Region for: p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.h0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::Coalesceonnxruntime::BFCArena::FreeAndMaybeCoalesce!c->in_use()BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in bin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinc2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c1->in_use() && !c2->in_use() && c1->stream == c2->streamonnxruntime::BFCArena::Mergeonnxruntime::BFCArena::Shrinkonnxruntime::BFCArena::DeallocateRawInternal bytes. BFC Arena shrunk by onnxruntime::BFCArena::SplitChunk The total allocated bytes is now onnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)!chunk->in_use()Failed to find a free memory block despite calling Extend. rounded_bytes=. bin_num:Extending BFCArena for (actual) rounded_bytes: (requested) num_bytes: tried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternalreserved_chunks_.find(ptr) == reserved_chunks_.end()h != kInvalidChunkHandleReserving memory in BFCArena for onnxruntime::BFCArena::Reserve size: Allocated memory at onnxruntime::BFCArena::ExtendTotal allocated bytes: bytes.Extended allocation by Available memory of Failed to allocate memory for requested buffer of size onnxruntime::BFCArena::Extend::::operator () is smaller than requested bytes of Incorrect arena extend strategy.onnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccnode_index_info and ort_value_idx_map are out of sync and cannot be usedallocator.get() != nullptronnxruntime::AsStreamBasedAllocatorCould not find OrtValue with idx 'allocator is nullptronnxruntime::IExecutionFrame::GetMLValueD:\a\_work\1\s\onnxruntime\core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_TraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::TraceFreeTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::VerifyOutputSizesonnxruntime::ExecutionFrame::ReleaseMLValueImpl does not match actual shape of Expected shape from model of Invalid allocation kind: for output We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::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.alloc && alloc.get() != nullptr!is_strided_tensorFor ort_value with index: Failed to get allocator for but the actual size is: , block in memory pattern size is: onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behavior returned nullptrTrying to allocate memory for unused optional inputs/outputsbuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrameAllocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()Fetches vector passed to GetOutputs contains failed. Error:fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Init but expected feed size: Get feed size: shape && sp_tensor.DenseShape() == *shapeinvalid index shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::TransposeSingleAxisOutwardsD:\a\_work\1\s\onnxruntime\core\framework\transpose_helper.cconnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::CreateInvalid dim0_offset of dim0_offset < dim0_sizegsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is . Shape:Insufficient dimensions to slice on Can't slice a tensor with a subbyte element type!prim_type->HasSubElems()OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer::CreateD:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccCan't slice a non-tensor OrtValue. Type was ort_value.IsTensor()D:\a\_work\1\s\onnxruntime\core\framework\library_handles.cconnxruntime::LibraryHandles::UnloadLibrariesD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.cconnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchmodel format error! Missing 'location'model format error!parsing failedmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infoonnxruntime::ExecuteThePlanNon-zero status code returned while running node. Name:'' Status Message: D:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::ExecuteKernelAsync Kernel Support is not implemented yet._fence_after_kernel_timethread_scheduling_statsoutput_type_shapeinput_type_shapeoutput_sizeparameter_sizeactivation_sizenode_index_fence_beforeop_nameSequentialExecutor::Execute{"":[]}D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalentry != nullptrAll implicit inputs should have OrtValue instances by now. does not.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= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()invalid value index: against size onnxruntime::PlannerImpl::IndexD:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cconnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: : cannot use insert() with Use DeviceBasedPartition as defaultonnxruntime::IGraphPartitioner::CreateGraphPartitionerCaught exception when reading partition config file: onnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception during saving DeviceBasedPartitioner config: node_names_by_stream_.size() == device_types_.size()Number of streams does not equal to number of device types!devicesstreamsonnxruntime::DeviceBasedPartitioner::InitializePartitioner type is not DeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::PartitionGraphiter != node_stream_map.end()Failed to find node "" in node-stream mapDeviceBasedPartitioneronnxruntime::PlannerImpl::CreatePlantrigger_point_it != node_to_trigger_points.end()execution_plan[plan_.node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()onnxruntime::PlannerImpl::BuildExecutionPlanonnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()Strided tensor is not supported in non-training build for now.onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan!IsNonTensor(*node_output)Only tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream reused by as inputonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()Be noted input buffer which has external outputs is reused. onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GetAliasMapkernel_create_info.kernel_def != nullptrKernelDef is null for node: onnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider onnxruntime::PlannerImpl::ComputeValueLocationp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defonnxruntime::PlannerImpl::ComputeValueLocation::::operator ()found_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::ComputeReuseCountCan not find the node onnxruntime::PlannerImpl::GetElementSizecannot use operator[] with a string argument with cannot use operator[] with a numeric argument with �incomplete UTF-8 string; last byte: 0xinvalid UTF-8 byte at index : 0x\u%04xnull}],"subtype":{"bytes":["subtype": ], "bytes": [[ []":, ": { {}D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.cconnxruntime::PrePackedWeights::GetHashbuffers_.size() == buffer_sizes_.size() bytes for entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGCan not trace string tensorentry != initialized_tensors_to_allocate.end()OrtValue index: from initializer_allocation_order not found among initialized tensorsort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (Failed to copy tensor to onnxruntime::session_state_utils::AllocateTensorOnDeviceOrMemoryInternal error. The preallocated buffer is too small. Requires string tensor is not supported for copying between allocatorsonnxruntime::session_state_utils::DeserializeTensorProtoWith buffered tensor, tensor proto must use external location and point to buffered tensorDeserializeTensorProto() takes either pre-allocated buffer or an allocator!onnxruntime::session_state_utils::ExtDataTensorProtoToTensorD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::AllocateBufferUsingDeviceAllocatorFromShapeAndTypesession.use_device_allocator_for_initializersGraphSubgraph input with name is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()Done saving initialized tensorsDeserialize tensor failed.Using user supplied initializer with name (Skipping entry for missing optional value at idx [Memory] SessionStateInitializer statically allocates D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.cconnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer '' failedWeight buffer for initializer '' is not foundMem pattern for initializer is not foundInternal error.D:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlanduplicated locationFailed to get allocator for location: already exist.func info for node: Can't use func with null ptr not found.onnxruntime::FuncManager::GetFuncsD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cc!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.hcurrent <= buffer_size_v >= 0onnxruntime::StreamExecutionContext::CountDownBarrier::SetD:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.hidx < device_stream_map_->NumStreams()onnxruntime::StreamExecutionContext::GetDeviceStreamD:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.ccframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()onnxruntime::StreamExecutionContext::RecycleNodeInputsExiting due to terminate flag being set to true.since == endonnxruntime::RunSince, Count: Barrier - BarrierId: WaitOnEPStep.wait_handle is nullwait_handle_onnxruntime::WaitOnEPStep::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cc wait on Notification with id: stream WaitOnEP - NotificationId: , Name: NodeIndex: LaunchKernel - activate notification with index onnxruntime::ActivateNotificationStep::ExecuteActivateNotification - NotificationId: TriggerDownstream - TriggerPointIndex: TypeProto must have shape for this to runshape != nullptronnxruntime::utils::GetShapeD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hALLOW_RELEASED_ONNX_OPSET_ONLYNoOpConstantDuplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: '. Please, fix your model.' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:node_arg) does not exist in the graph.This is an invalid model. Graph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Subgraph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (Invalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous node.Invalid model. Node input 'Some nodes are not included in the topological sort, graph have a cycle.onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) ) Op (Node () does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.Output:Source and target must both be tensors , or optional typed entities , or sparse tensors. Falling back to lenient merge. target:' source:Error merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccTensor element type mismatch. Input=Type mismatch. Current=onnxruntime::NodeArg::UpdateTypeAndShape . Got: Optional Type mismatch. Expected: Removable attributes were removed before the conversion is started.onnxruntime::Node::ToProto] op_type [Node [Serialization of fused function body is not currently supported, Removable attributes were removed before the node is saved.does not have the graph for key onnxruntime::Node::SaveToOrtFormatfbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()node_arg_name cannot be null], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:input index: onnxruntime::Node::LoadEdgesFromOrtFormatinput edgesoutput edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptronnxruntime::Graph::Graph' Model is invalid. as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodeFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProtoexternal_file_path.is_relative()onnxruntime::Graph::ToGraphProtoWithExternalInitializersexternal_stream.is_open()'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternalCannot find NodeArgs for [initializer_node_arg != nullptronnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is not used by any node and should be removed from the model.Removing initializer ''. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg ' must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != func_meta_defonnxruntime::Graph::CreateFusedSubGraphNode SinceVersion: OpType:Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)onnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::AddConstantProtoAsInitializer conflicts with graph initializer. Check that the node names have been made unique.Constant node name: insert_result.second' Unable to fetch: Unable to constant fold If node: 'onnxruntime::Graph::InlineIfSubgraph_if_ conflicts with graph initializer. Check name generation above. from graph: Initializer name: is not If node's input or previous node output in this subgraph input: Node's: for If node: Constant folded (inlined) onnxruntime::Graph::InlineFunctionProto_inlfunc_Node has no function body and cannot be inlined.callnode.TryGetFunctionProto(inlined_fp)onnxruntime::Graph::InlineFunction conflicts with graph initializer. Check Specializing code. in inlined subgraph: Input to set must exist.input->Exists()onnxruntime::Graph::SetInputs) : ((""") -> () in node () of operator (' of input parameter (This is an invalid model. Type Error: Type ' in node () bound to different types () of Optype (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatchindex=) type inference failed, inferred types: ) output arg () does not match expected type () of node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. Type Error: Shape of initializer , Error This is an invalid model. In Node, onnxruntime::Graph::VerifyNodeAndOpMatch) is not a registered function/opFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensoronnxruntime::Graph::ForThisAndAllSubgraphssparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!foundFailed to find existing initializer with name onnxruntime::Graph::ReplaceInitializedTensorImplTrying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()onnxruntime::Graph::InjectExternalInitializedTensorsReplaced external initializer: onnxruntime::Graph::InjectExternalInitializersFromFilesInMemory not found from the table user provided.External file: onnxruntime::Graph::SaveToOrtFormat_token_onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.index < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.habsl::container_internal::raw_hash_map<>::atonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cc, but it its domain is notknown by the checker., but it its version is higherthan the operator set version D:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.cconnxruntime::GraphViewerToProto@mDIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Output not found:nodeargnodeMismatch between Graph and IndexedSubGraph. Node not found: onnxruntime::GraphViewer::GetNodesInTopologicalOrderMemory efficient topological order is not enabled for non-training build.Invalid ExecutionOrder are '0' and '1'. The environment variable contained the value: D:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.honnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset onnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset ModelProto does not have a graph. is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelMissing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unsupported model IR version: , max supported IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Failed to load model with error: onnxruntime::Model::LoadNo graph was found in the protobuf.Protobuf parsing failed. less than 0.onnxruntime::Model::Save is less than 0. is less than 0.Protobuf serialization failed.onnxruntime::Model::SaveToOrtFormatonnxruntime::Model::SaveWithExternalInitializersonnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.Graph is null. Invalid ORT format model.onnxruntime::SaveModelonnxruntime::SaveModelWithExternalInitializersLoad model failed. File doesn't existsystem error number Output 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 in should specify a shape in If 1, mean and variance are computed across channels. Default is 0.Scaling valueIf 0, normalize the mean only. Default is 1.Input's shape must be 4-D'Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[1] () + scale[0] (Value of betaValue of alphaD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.The scale to apply.Bias applied to each channel, same size as C.A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Threshold valuedataConstrain input and output types to all tensor types.input_as_shapeConstrain indices to integer typesNegative values are not allowed in a shape specificationextra_shapeGivenTensorFillvaluesdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevgatesseq_lengthsGRUUnithiddenCexpected to be a tensor or a sparse tensor type in must have a non null shape in Value of attribute not specified in does not specify a valid type in should be of integer type and specify a type in Incompatible dimensions but has rank Dimension mismatch in unification between expected to have rank tensor(uint4) expected to have rank >tensor(int4)xAError parsing node: = Constant()Error unexpected extra input in node:Constrain output 'mask' types to uint32 tensors.Constrain input 'training_mode' types to boolean tensors.isnan_onlyisinf_onlyIsAllFiniteBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.If true, check only for NaN.If true, check only for Inf, -Inf.Constrain the output to a boolean tensor.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.Three interpolation modes: bilinear (default), nearest and bicubic.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.GridConstrain input types to all tensor types.size attribute in UnfoldTensor must greater than 0.Constrain output types to float tensors.size attribute in UnfoldTensor not positive and less than the dim size!size attribute in UnfoldTensor not specified!specify the dimension to unfoldspecify the step.specify the sizeAllow inputs and outputs to be any kind of tensor.Constrain to integer types.Constrain to float tensors.The id of the end-of-sequence tokenThe id of the token that indicates decoding starts.The id of the padding tokenearly stop or notno repeat ngrams sizeThe 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 T5Decoder 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 runsSize of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shaperepetition_penaltyvocab_maskprefix_vocab_masklogits_processor'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Input tensors of wrong rank (0).Pads has incorrect number of valuesInputs 0 shall be 3 dimensions in whisper graphIncompatible dimensions for matrix multiplicationFailed to parse max_length or it is not positive integer scalarInputs 0 shall be 2 dimensionsFailed 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) OneD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccAlphaAlpha value. CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) != mat_h:Input rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w:weightpadding_idxscale_grad_by_freqTorchEmbeddingConstrain input and output types to all numeric tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Constrain input and output types to all numeric tensors and bool tensors.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 higher(Optional) Seed to the random generator, if not specified we will auto generate one.residualratioConstrain input 'ratio' types to float tensors.maskConstrain output 'mask' types to boolean tensors.fc2_experts_weightsfc2_experts_biasfc3_experts_biasfc3_experts_weightsMoEConstrain input and output types to float or float16 tensors.Number of bits used in quantized weights. Default is 4 bitsexpert_weight_bitsfc1_scalesfc2_scalesConstrain weights type to uint8 tensors.fc3_scalesConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.QMoEConstrain input0 and output types to float tensorsnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)must be 0, normalization currently not supportedsignal_ndimmust be 1, only one sided FFTs supportednormalizedConstrain input and output types to float or half tensors.Rfftnumber of dimensions comprising the signalIrfftComplexMulWComplexMulConjConstrain input and output types to float tensorsPadsZSecond input does not have rank 2First input does not have rank 2Constrain input and output types to float/int tensors.Whether B should be transposedWhether A should be transposedScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Input axis is invalid: activation_gammasequencessequences_scoresConstrain input type to float or int tensors.scoresConstrain mask to integer typesConstrain to integer typesThe id of the token that indicates decoding starts (i.e. the start of transcription token id)The id of the transcribe taskThe id of the translate taskThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates LM startsThe id of the first timestampThe id of the token that indicates no timestampsIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.Must be 2 for whispercross_qk_layer_headextra_decoding_idscross_qkConstrain cross_qk to float32 tensors.non_speech_probsmodel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.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.The value used to module the next token probabilities.Minimumber of tokens we keep per batch example in the output.All filtered values will be set to this float value.If 1 custom sampling logicPresence penalty for custom samplingModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartpresence_maskfiltered_logitsActivation function to use. Choose from relu, gelu, silu and identity. Default is reluNumber of top experts to select from expert poolactivation_typenormalize_routing_weightsWhether to normalize routing weightsuse_sparse_mixerWhether to use sparse mixerrouter_probsfc1_experts_weightsfc1_experts_biasInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.SequenceConstrain to tensor(int32).Constrain to tensor(float).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 int64idxInput can be of any tensor type.countsThe distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".Constrains input to only numeric types.rois input tensor has wrong dimensionfirst input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionValue used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.roisbatch_indicescrop_sizeConstrain types to int tensors.Constrain types to float tensors.Whether B should be transposed. Float 8 only supprted transB=1.Whether A should be transposed. Float 8 only supprted transA=0.Output Type. Same definition as attribute 'to' for operator Cast.Scalar multiplier for the product of input bias C.Activation function, RELU or GELU or NONE (default).scaleAscaleBscaleYConstrain type to input A.TRConstrain type to input C.Constrain type to input B.Input/Output is a string tensorThe string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.inputs are expected to have tensor type and output type should not be null.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredConstrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorB 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).Input q4 tensors of wrong size!4b quantization not yet supported on this hardware platform!Quantization typeConstrain input matrix data types as single precision float tensorB_shapeConstrain shape of B must be int64 tensor.Constrain input B data types as data blobScalar multiplier for the product of the input tensors.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationactivation_axisWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationsparse_tensor(double)sparse_tensor(float)sparse_tensor(int32)sparse_tensor(int64)sparse_tensor(uint32)sparse_tensor(uint64)Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.both 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.last dimension of indices must not be larger and rank of data tensorConstrain indice type to int32 or int64Constrain input and output types to any tensor type.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightInvStdDevType of Mean and InvStdDev tensors.Constrain input X type to float tensors.Constrain output Y, scale and bias type to float tensors.Constrain mean and inv_std_var to be float tensors.inv_std_varEfficientNMS_TRTConstrain output Y and scale type to float tensors.boxesanchorsnum_detectionsdetection_boxesdetection_scoresBackground class ID.detection_classesEncoding type for the boxes or anchors inputs.background_classBox IOU threshold value.box_codingMax detections to output.iou_thresholdplugin_versionVersion number of the TRT plugin.score_activationActivation function to apply to the scores input.score_thresholdScore threshold value.MultilevelCropAndResize_TRTfeature_map_0feature_map_1feature_map_2feature_map_3Image size.patchesPooled size.image_sizeDisentangledAttention_TRTPyramidROIAlign_TRTc2c_attentionc2p_attentionp2c_attentiondisentangled_attentionspanMaximum relative distance, k.Constrain type for all input scales (scaleA, scaleB, scaleY).Constrain type to result type.EpsilonGemmFloat8Rank = Size (XShape)XShape = Shape (X)Axis1D = Constant()Zero1D = Constant()NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)Mean2D = ReduceMean (XU)XU = Cast (X2D)MeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)Scale2D = Flatten (Scale)NormalizedV = Cast (Normalized)B2D = Flatten (B)Scaled = Mul (NormalizedV, Scale2D)Biased = Identity (Scaled)Biased = Add (Scaled, B2D)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)max_output_boxesAttribute 'max_output_boxes' must be >= 1.Attribute 'pooled_size' must be >= 1.pooled_sizeInput 0 shall be 3 dimensionsbias shape must be knownbias shape must be [N] where N = data tensor must have rank >= 1quantize_axis must be in [-r, r-1]gather_axis must be in [-r, r-1]scales must have the same rank as datablock_size must be non-negativezero_points shape must be knowndata shape and scales shape do not matchzero points shape and scales shape do not matchzero points must have the same rank as dataThe epsilon value to use to avoid division by zero.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varConstrain quantized weight types to uint8.Constrain input and output types to float/half_float/brain_float tensors.(Optional) Which axis to block-wise quantize. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).(Optional) Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).(Optional) block size used for weight quantization. It needs to be a power of 2 and not smaller than 16.Constrain dequantized types.Constrain quantized types.Constrain indices to integer types.factorScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.DLCpayload of the SNPE DLC file.snpe_version(Optional) SNPE version used to convert the model.target_device(Optional) Target device like CPU, DSP, etc.notes(Optional) Some notes for the modelEPContextConstrain input and output types to uint8, uint16, float tensors.Usually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.payload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.main_context1: indicate ep_cache_context is the context content. 0: indicate ep_cache_context is the file path to the context content.The path is relative to this Onnx file. Default is 1.ep_cache_context(Optional) SDK version used to convert the model.embed_mode(Optional) Filename of the original ONNX model.ep_sdk_version(Optional) Hardware architecture.onnx_model_filename(Optional) partitioned graph name.hardware_architecture(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainpartition_namesourceConstrain input and output types.Constrain 'training_mode' to boolean tensor.size of each input featureConstrain output 'mask' types to bit-packed uint32 tensor.number of bits used for weight quantization (default 4)size of each output featureThe minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.zero_pointsg_idxConstrain input and output types to float/half_float tensors.Constrain quantized zero point types to uint8/int32/float16/float.Constrain quantized weight types to uint8/int32.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.the index tensor.Indicate if the ops run in training_mode, by default, False.quantization data type. 0 for FP4, 1 for NF4.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.absmaxInputs 0 (query) shall be 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsPackedAttentionkeyqueryConstrain mask, offset and sequence length to integer typesPackedMultiHeadAttentionConstrain input and output to float tensors.cache_indirectionCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Need output the cross attention MatMul(Q, K)past_keypast_valuepresent_valuepresent_keyqkConstrain qk output types to float32 tensors.key_padding_maskNumber of attention heads for qNumber of attention heads for k and vRotate using interleaved pattern. Default value is 0 (False).Use a smooth factor in softmax.Softcap value for attention weights. Default value is 0.left_window_size for local attention (like Mistral). Default value is -1 meaning unused.key and value cache shall be 4 dimensionstoken_offset shall be 2 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionsInputs 0 (query) shall be 3 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past_key input shall be 4 dimensionsHidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Number of attention headsWhether every token can only attend to previous tokens. Default value is 0.The value to be filled in the attention mask. Default value is -10000.0fCustom scale will be used if specified. Default value is 1/sqrt(head_size)Whether to use rotary position embedding. Default value is 0.Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeattention_biaspastpresentInvalid bias shapeConstrain mask index to integer typesD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.cctoken_offsetqkv_hidden_sizes should have 3 elementscumulative_sequence_lengthhas_key_padding_maskhas_layer_statenew_key_cachenew_value_cacheDecoderAttentionConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.Number of attention heads. Default value is 0. Must use with rotary_embedding_dimragged batch inputs or not. Default value is 0Custom scale will be used if specified. Default value is 1.0Rotary embedding dimension. Default value is 0.embConstrain input and output types to integer tensorsq_rotqoutput1k_rotConstrain input 0 type to float tensorsGemmaRotaryEmbeddingoutput2Constrain input and output types to float16 tensors.mask_index_typeThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)word_embeddingsegment_idssegment_embeddingposition_embeddingConstrain input and output integer tensors typesConstrain input and output float tensors types.embedding_sumbaX_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) oneX_bias = Add (X, bias)seqlens_kcos_cachesin_cachetotal_sequence_lengthConstrain mask to int tensor.Number of attention heads for key and valueScaling factor applied prior to softmax. The default value is 1/sqrt(head_size)Number of attention heads for queryNumber of tokens per sparse block. Choices: 16, 32, 64, 128Rotary use interleaved pattern or not. Default value is 0.block_col_indicesblock_row_indiceskey_total_sequence_lengthsConstrain integer type.windowOne sided attention windows length W, or half of total window lengthglobal_weightglobalglobal_biasGLongformerAttentionkv_weightq_weightkey_cachevalue_cacheuse_paststatic_kvRemovePaddingRestorePaddingConstrain token_offset to integer typesquery_biasquery_layerrel_poseco_aGatedRelativePositionBiasMax distancemax_distancebias_tableDefault value is 0.is_bidirectionalkey_lengthquery_lengthRelativePositionBiasConstrain sequence_length to int tensors.skipinput_skip_bias_sumConstrain mean and inv_std_var to float tensors.The NGram size.scores_outConstrain scores input and output types to float tensors.The maximum NGram size for suffix matching.The minimum NGram size for suffix matching.cur_tokenssrc_tokenspred_tokensprev_suffix_match_idxsuffix_match_idxtokensGemmFastGelusequence_token_countmax_seq_lenConstrain sequence_token_count and token_offset to integer typescumulated_seq_len̼+̼+Constrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNormbias shall be 1 dimensionBiasSplitGeluBiasAddThe epsilon value to use to avoid division by zeroActivation after group normalization: 0 for None, 1 for SiLU1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.The number of groups of channels. It should be a divisor of the number of channels CgroupsGroupNormD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccConstrain input X and output Y types to float tensors.Constrain gamma and beta to float tensors.Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is or UNDEFINED. Got: expected to have: Dimension=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred=reducedA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).Constrain input types to 8 bit signed and unsigned tensors.ReduceSumIntegerb_scaleConstrain output types to 32 bit tensors.MulIntegerConstrain input A, b_scale and output Y data type as float tensor.b_zero_pointa_scaleConstrain input B data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointa_zero_pointConstrain input A data type to 8-bit integer tensor.multiplicationaxis must be in [-rank, rank-1]. input rank was additionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scalereduced_zero_pointdata_zero_pointreduced_scaleX_scaleCoefficient of leakage.Y_scaleX_zero_point]. Its actual value is: Y_zero_pointConstrain input and output types to 8 bit tensors.apply softmax to elements for dimensions axis,or all dims along with axis according to op-version , 'axis' must be in [opset version of corresponding SoftMax.Constrain input and output types to signed/unsigned int8 tensors. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}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.inputs are expected to have tensor type.A_scale{additionalDocumentation}A_zero_pointB_zero_pointB_scaleC_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_zero_pointy_scaleConstrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'x', 'y_scale' to float tensors.x_scaleConstrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.x_zero_pointThe type of BFP - must match with the BFPType enumbfp_typeConstrain 'y', 'x_scale' to float tensors.Each 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.block_dimConstrain the input to float and bfloat.Constrain y to uint8.Shape of quantized tensor must be 1D.Constrain shape and strides to uint64.QuantizeBFPThe datatype to dequantize to.Constrain y to float and bfloat16.DequantizeBFPConstrain the input to uint8.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.input_scaleweight_scaleweight_zero_pointinput_zero_pointword_embedding_quantConstrain input and output types to int8 tensors.beta_quantposition_embedding_quantgamma_quantposition_embedding_scaleword_embedding_scalegamma_scalesegment_embedding_scaleword_embedding_zero_pointbeta_scalesegment_embedding_zero_pointposition_embedding_zero_pointbeta_zero_pointgamma_zero_pointmask_index_outlayernorm_outcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_inputConstrain input and output types to float32 tensors.cublasLt order of output matrix.order_outputConstrain to float typesConstrain Scale to float32 typesscale_inputQcublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixQuantizeWithOrderOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerCouple the input and forget gates if 1.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Rinitial_hsequence_lensPinitial_cY_hConstrain weights types to 8 bit tensors.Required attribute axis is missingY_cConstrain seq_lens to integer tensor.Which axis to concat onaxis must be in [-rank, rank)All inputs to Concat must have same rankTVT8Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).conditionz_scalez_zero_pointConstrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.QOrderedGeluConstrain scales to float32scale_weightorder_global_weightscale_qkv_gemmscale_biasscale_global_gemmscale_global_weightBe compatible with float version.QOrderedLongformerAttentionscale_outputinput and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.ww_zero_pointw_scaleConstrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input W.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional 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.number of groups input channels and output channels are divided into.QLinearConvTransposeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axiscublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.The output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrderorder_BcublasLt order of matrix Y and optional matrix Corder_AcublasLt order of matrix Bscale_Aorder_Yscale_Bscale_Cscale_YHidden layer sizes of Q, K, V paths in AttentionConstrain bias and scales to float32QOrderedMatMulcublasLt order of global biascublasLt order of weight matrixorder_weightscale_K_gemmscale_V_gemmscale_Q_gemmK_weightV_weightQ_weightscale_K_weightscale_V_weightscale_Q_weightV_biasscale_QKT_gemmQ_biasK_biasscale_values_gemmscale_QKT_softmaxConstrain scales to float32 tensors.QOrderedAttentionorder_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.QOrderedLayerNormalizationcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.scale_XcublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.quantization scale must be float tensors.quantization tensor must be int8 tensors.̼+D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Works on NHWC layout or not? Default not.The size of the kernel along each axis.Whether to use ceil or floor (default) to compute the output shape.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.itr != node_args.end()onnxruntime::graph_utils::GetIndexFromNamefor node: Attempting to get index by a name which does not exist:onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph in one of the subgraphs.D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.ccstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used output cannot be safely updated to Implicit input name Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputindex >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameonnxruntime::graph_utils::AddInitializerAttempting to get an input that does not exist.onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Attempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size() ExplicitInputs:. Index:onnxruntime::graph_utils::RemoveNode ImplicitInputs:Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputonnxruntime::graph_utils::FindPathonnxruntime::graph_utils::AddNodeInputFailed since multiple edges matched:onnxruntime::utils::SetNodeAttributeD:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccAttributeProto must have a name.utils::HasName(attribute)onnxruntime::SaveRuntimeOptimizationRecordToOrtFormat is null. runtime optimization record produced op idD:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatAttempting to load runtime optimization records for a previously loaded optimizer: onnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::fbs::utils::LoadOpIdentifierOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.cconnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringD:\a\_work\1\s\onnxruntime\core/graph/op_identifier.hInvalid OpIdentifier string: Failed to parse since_version from TENSORUNDEFINEDSTRINGGRAPHFLOATINTSTRINGSTENSORSFLOATSINTSSPARSE_TENSORSGRAPHSSPARSE_TENSORTensor has external data but a data reader was not provided.Missing values for sparse initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null string attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveInitializerOrtFormatSaveAttributeOrtFormat: Unsupported attribute type: String data type is not supported for on-device trainingonnxruntime::fbs::utils::SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.Missing dimensions for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatonnxruntime::fbs::utils::GetSizeInBytesFromFbsTensorUnsupported tensor data type for tensor gggggggggghhhall_tensor_types_ir4TAggregatedTypesD:\a\_work\1\s\onnxruntime\core\graph\function_utils.cc in function opset imports.No opset registered for domain input_arg->Type() != nullptronnxruntime::function_utils::CreateSchemadomain_version != -1Too many inputs for op it != opset_imports.end()onnxruntime::function_utils::IOTypeConstraintHelper::::operator ()Tinoutstatic_cast(i) < node_op_schema->inputs().size()in, domain: The given function name: Tout is not found in model local functionsonnx_func_protoNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()onnxruntime::function_utils::Inliner::bindinitializer != nullptr is not found or is not constant initializer.D:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::FunctionImpl::FunctionImplUnsupported type:Can not get shape initializer data!deltalimitUnsupported non-raw-data data type!tensor rank too smallD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueinvalid scales dimensionNumber of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.Constrain seq_lens to integral tensors.QWMWAWmemory_seq_lenssegment_ids input shall be 2 dimensionsinput_ids shall be 2 dimensionsgamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.word_embedding should have 2 dimensions and dimension size is known.The past input shall be 5 dimensionsInputs 0 shall be 3 dimensionsbeta should have 1 dimension, dimension size known, and same hidden size as word_embedding. auto_set_affinity: thread_pool_size: OrtThreadPoolParams { affinity_str: stack_size: dynamic_block_base_: allow_spinning: onnxruntime::concurrency::ReadThreadAffinityConfigstd::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must consist of only digits: set_denormal_as_zero: Invalid processor interval: processor_from > 0 && processor_to > 0Processor id must start from 1: D:\a\_work\1\s\onnxruntime\core\util\thread_utils.cc, skip affinity settingprocessor_id > 0std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_from <= processor_to, thread_pool_size: Failed to read affinities from affinity stringFound out-of-range processor id in affinity string: Found invalid processor id in affinity string: custom join thread function not setonnxruntime::concurrency::CreateThreadPoolHelperactual_num_affinities == static_cast(options.thread_pool_size) - 1Number of affinities does not equal to thread_pool_size minus one, affinities: Size of affinity string must be between 1 and Received null affinity_stringReceived invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionsdims[d_i] < d_maxD:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPosition??D:\a\_work\1\s\onnxruntime\core\mlas\lib\q4_dq.cppUnsigned quant types without zero points must allocate zero points with value 0.src_zero_points || signed_quant || dst_zero_pointsBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedColumns must be multiple of 2columns % 2 == 0BlockwiseQDQQuantizer::TransposeColumnWiseQuantizedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAligned) is not supported on this device), BIsSigned(Quant GEMM format: AIsSigned(33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KA%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;rB¥B@K;?r1:n/ <*=(*>>??bad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??bad mlas activation kindQLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!ElementSize must be power of 2 and less or equal than 16!    onnxruntime::ToUTF8Stringlength overflowonnxruntime::ToWideStringD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntimeonnxruntime::logging::LoggingManager::LoggingManagerISink must be provided.onnxruntime::logging::LoggingManager::CreateDefaultLoggerdefault_logger_id must be provided if instance_type is InstanceType::DefaultD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccDefault logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.RUNTIME_EXCEPTIONENGINE_ERRORMODEL_LOADEDINVALID_PROTOBUFFAILSUCCESSNO_MODELNO_SUCHFILEINVALID_GRAPHNOT_IMPLEMENTEDGENERAL ERROREP_FAILSystemErrorD:\a\_work\1\s\onnxruntime\core\common\status.cc[ONNXRuntimeError]onnxruntime::common::Status::Statuscode != static_cast(common::OK)ReturnNtException%hs!%p: %hs(%u)\%hs!%p: %hs(%d) tid(%x) %08X %ws(caller: %p) LogNtReturnHrFailFastLogHr [%hs] Msg:[%ws] [%hs(%hs)] CallContext:[%hs] "" when trying to load "FreeLibrary failed with error LoadLibrary failed with error onnxruntime::WindowsEnv::GetCanonicalPathGetFinalPathNameByHandle() failed: "UNC\Failed to calculate byte size for saving cpu info on windowsonnxruntime::WindowsEnv::FormatLibraryFileNameFailed to fetch cpu info on windowsonnxruntime::WindowsEnv::InitializeCpuInfoK32EnumProcessModulesKernel32.dllFailed to find symbol in library, error code: Detected L2 cache size: bytescore consist of logical processors: Found total core(s) from windows system:\*D:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDirRaiseFailFastExceptionkernelbase.dll%ws/\D:\a\_work\1\s\onnxruntime\core\platform\windows\env.ccindex >= 0Negative thread index is not allowed error msg: onnxruntime::UnmapFileunmap view of file failed. error code: kernel32.dllWindowThread:_beginthreadex failed with errno:onnxruntimeSetThreadDescriptioncustom_create_thread_fn returned invalid handle.onnxruntime::WindowsThread::WindowsThread message: doserrno:, first on group , then on , mask: Cannot set cross-group affinity for thread , processor does not existonnxruntime::WindowsThread::ThreadMainCannot set affinity for thread , index: , error code: fail, errcode = SetThreadAffinityMask failed for thread: SetThreadAffinityMask done for thread: , group_id: , error msg: . Specify the number of threads explicitly so the affinity is not set.Received negative size from stat callonnxruntime::WindowsEnv::ReadFileIntoBufferfile_path == nullptrGetFileSizeEx open file Invalid fd was supplied: onnxruntime::WindowsEnv::MapFileIntoMemory fail: unexpected end , errcode = open file mapping length > buffer.size()offset < 0ReadFile SetFilePointerEx DeleteFile() failed - path: \/RemoveDirectory() failed - path: onnxruntime::WindowsEnv::DeleteFolder , mapped_offset = , allocation_granularity = mapped offset must be a multiple of the allocation granularityonnxruntime::concurrency::ThreadPool::ParallelForMore work items than threads!ps_n >= 0!current_parallel_section.has_value()onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection": {Nested parallelism not supportedD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionn <= num_threads_ + 1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelProfiler not started yetenabled_unnamed_thread_pool{"main_thread": {LogStart must pair with LogEnd", "thread_pool_name": "}}}, "sub_threads": {onnxruntime::concurrency::ThreadPoolProfiler::StopD:\a\_work\1\s\onnxruntime\core\common\threadpool.cc"thread_id": "], "core": onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset", "block_size": [onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartpoints_.empty()!points_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd"core": "num_run": WaitRevokeUnknownEventRunWaitDistributionDistributionEnqueueUnexpected Severity of onnxruntime::logging::EtwSink::SendImplD:\a\_work\1\s\onnxruntime\core\platform\windows\logging\etw_sink.ccETW registration failed. Logging will be broken: onnxruntime::logging::EtwRegistrationManager::LazyInitializeKernelApiSessionNodeonnxruntime::profiling::Profiler::InitializeD:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::Start{"cat" : ""pid" :onnxruntime::profiling::Profiler::EndProfiling",onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file Maximum number of events reached, could not record profile event." : " : ""name" :""args" : {"ts" :"ph" : "X","tid" :"dur" :] } }, Șunexpected failureillegal input path:Failed to get error messageInvalid fill (too long).{:L%F %T}Invalid type specification.Incompatible modifier for typeInvalid format string - missing type after modifier.Invalid modifier specification.Can not switch from automatic to manual indexingFormat specifier requires numeric or pointer argument.Invalid presentation type specifierDynamic width or precision index too large.0e+00Number is too bigMissing precision specifier.Hash/sign modifier requires an arithmetic presentation typeZero modifier requires an arithmetic or pointer presentation typeInvalid presentation type for stringInvalid presentation type for pointerInvalid presentation type for integerInvalid presentation type for floating-pointInvalid presentation type for boolInvalid presentation type for wchar_tNumber is too big.{:02}:{:02}:{}{:02}{}{:02}{:04}{:02}%I:%M:%S %pNegative precision.true{:0{}}Precision is not an integer.Negative width.Precision not allowed for this argument type.Format specifier requires numeric argument.Width is not an integer.0X\t0B0xArgument not found.0bfalseintegral cannot be stored in wchar_t\u{\r\\\n\r\t\nUTC: [] , Invalid format string.Unknown format specifier.Unmatched '}' in format string.nan(ind)nan(snan)Invalid format string - missing type after %invalid fill character '{'Invalid type.Invalid format string - chrono-specs must begin with conversion-specUTCPrecision specification invalid for non-chrono::duration type, see N4950 [time.format]/1.Missing '}' in format string.Can not switch from manual to automatic indexingString pointer is null.\x{ffffffGzGK7A!uq m7c^6 02 p.9 ^333p= I h1*  '.6>GPZdoz -?Rey,E^x<Zy<^cCdeHImMSuUVwWxXyYz 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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HSI - , % )2oc0G9 Q F [K9 #k .t'JpFWUG AT$>- :*L D=  ,d%,  w_  8 (T . 7 :1K`Kp0%)Y: < A M Q b  < A G K Q p u  < > A M U b  < > F J U b ; > A M W b 14G579q-29=X^q]2Rr  '29VX`bes4<Bk,6 ,--*00ot %,Ĩ&G)15CL| v   8 ?  $ F8ps'-s/46>A;>@Wfp8BF^3=?"'/90;>C3;GQY08?1:<?G6@B@4G4j0kOooo0egn{B;u#&0D p-      $    @!        .72` (4BP_p $@^}(NuM|DyYMV(q S A 9 = 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899X0雀@雀 雀map_typetensor_typesequence_typeonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveTypeInfoOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveMapTypeOrtFormatonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::LoadTensorDimensionOrtFormatNull entry in dimensions. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for dim_param value with no name. Invalid ORT format model.onnxruntime::fbs::utils::SaveValueInfoOrtFormat is missing type info.] for nowWe do not support type [Null tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatonnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadValueInfoOrtFormatModel must have opset imports. Invalid ORT format model.. Invalid ORT format model.Null type info for is not supported currentlyType:Null sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.opset import domain is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatopset id is null. Invalid ORT format model.optional(tensor(Invalid data type DataTypeUtils::FromDataTypeString - Received invalid data type string 'opaquemapoptionalInvalid tensor data type Unsuported type proto value case.sparse_tensor(map(uint4complex128complex64 typestr: , has unsupported type: ) yields zero outputs, but got ::) takes zero inputs, but got in graph not in allowed input sizes.) has input size not in range [min=, max=Operator '' has been deprecated since version has unsupported type has inconsistent type )'s input is marked single but has an empty string in the graph) has more inputs () than declared () in op definition.has output size not in allowed output sizes.) has output size Mismatched attribute type in ''. Expected: '', actual: 'Unrecognized attribute: for operator ' appeared multiple times.)'s output ) has more outputs (Required attribute '' is missing. has unknown expected type' 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'ONNX 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 = Duplicate type constraint name!(it.GetName().empty())(outputs_.size() - 1) == iA 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].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.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The 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.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)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.The size of each input in the input list and the number of values must be the same in the LabelEncoder.At least one of values_tensor, values_strings, values_int64s, values_floats must be set.The input type was and the key type are different, which is not permitted for LabelEncoders.At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.The default tensor must be a singleton 1D tensor.The default tensor type and the value type The number of keys A float.An integer.A string.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.A list of floats.A list of ints.A list of strings.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.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 default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.The input must be a tensor of a numeric type or string. The output will be of the same tensor type.' must have ' must have type Unspecified required attribute.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.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\defs.ccIf 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 input must be an integer map to either string or float.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.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.'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 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 default value is false (0) for all nodes.Child node if expression is falseChild node if expression is trueAttribute 'nodes_splits' must have same type as input. Input type is and attribute type is Attribute 'nodes_splits' must be 1D.Attribute 'nodes_splits' is required.nodes_splitsBase values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'Attribute 'leaf_targetids' must have same length as attribute 'leaf_weights'. 'leaf_targetids' length is and 'leaf_weights' length is leaf_weightsleaf_targetidsAttribute 'membership_values' must have same type as input. Input type is membership_valuesnodes_falseleafsnodes_trueleafsIf `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.If `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.Attribute 'leaf_weights' must have same type as input. Input type is Attributes 'leaf_targetids' and 'leaf_weights' must both be set.The weight for each leaf.The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).Members to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.tree_rootsIndex into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.For each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.1 if false branch is leaf for each node and 0 if an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry1 if true branch is leaf for each node and 0 an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entryThe output will be a sequence of string or integer maps to float.ZipMap input shape should be 1D or 2D.TreeEnsembleDefines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)Indicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)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))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 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'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 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.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.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'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 default 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'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.A 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.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.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.ccInput 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.Label encoder has only one input.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.A list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.Only one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is settype: axis must be in [-rank, rank-1].indices must be in [-rank, rank-1]. and Output expected to have tensor or sparse tensor type in Input: does not match type of output: (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Dimension could not be inferred: incompatible shapesInvalid 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.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.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.reshapedWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..All inputs to Concat must have same rank. Input has rank Constrain output to int64 tensor, which should be a scalar though.Mismatch between number of splits () and outputs (Both 'split' input and 'num_outputs' attribute were givenInvalid value of attribute 'axis'. Rank= Value=Constrain output types to any tensor type.concat_resultWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).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 (Input steps has incorrect lengthInput axes has incorrect lengthIncorrect or missing input value for starts and endsSlice 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.Invalid attribute perm {stepsInput rank for starts and ends should be the same: () vs (Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.Attribute perm for Transpose has repeated value: }, input shape = {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)))optional(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(tensor(complex128))optional(tensor(complex64))Constrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.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 TensorPrototarget_typeThe 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)D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.ccgridIf align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If 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.Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.Constrain roi type to float or double.if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.Length of input 'size' is . It must be 4 for 2D or 5 for 5D.Constrain grid types to float tensors.Constrain input `X` and output `Y` types to all tensor types.Constrain input and output types to all tensor, sequence, and optional types. { # naming one: 1, one_f: 1.0, one_1d: [1], one_f_1d: [1.0] one = Constant () two = Constant () zero = Constant () four = Constant () one_1d = Constant () zero_1d = Constant () minus_one = Constant () minus_one_f = CastLike (minus_one, theta) zero_f = CastLike (zero, theta) one_f = CastLike (one, theta) two_f = CastLike (two, theta) constant_align_corners = Constant () constant_align_corners_equal_zero = Equal (constant_align_corners, zero) size_ndim = Size (size) condition_is_2d = Equal (size_ndim, four) N, C, D, H, W = If (condition_is_2d) < then_branch = g1 () => (N_then, C_then, D_then, H_then, W_then) { N_then, C_then, H_then, W_then = Split (size) D_then = Identity (one_1d) }, else_branch = g2 () => (N_else, C_else, D_else, H_else, W_else) { N_else, C_else, D_else, H_else, W_else = Split (size) } > size_NCDHW = Concat (N, C, D, H, W) theta_3d = If (condition_is_2d) < then_branch = g3 () => (theta_then) { # theta: N by 2 by 3 => N by 3 by 4 # use of thetaN23 is a way to make shape inference happy when theta is N by 3 by 4. gather_idx_6 = Constant () shape_23 = Constant () gather_idx_23 = Reshape (gather_idx_6, shape_23) shape_N23 = Concat (N, shape_23) gather_idx_N23 = Expand (gather_idx_23, shape_N23) thetaN23 = GatherElements (theta, gather_idx_N23) # N by 2 by 3 => N by 3 by 2 r1, r2 = Split (thetaN23) # N by 1 by 3 r1_ = Squeeze (r1) # N by 3 r2_ = Squeeze (r2) r11, r12, t1 = Split (r1_) # N by 1 r21, r22, t2 = Split (r2_) r11_shape = Shape (r21) float_zero_1d_ = ConstantOfShape (r11_shape) # N by 1 float_zero_1d = CastLike (float_zero_1d_, theta) float_one_1d = Add (float_zero_1d, one_f) # N by 1 R1 = Concat (r11, r12, float_zero_1d, t1) # N by 4 R2 = Concat (r21, r22, float_zero_1d, t2) R3 = Concat (float_zero_1d, float_zero_1d, float_one_1d, float_zero_1d) R1_ = Unsqueeze (R1, one_1d) # N by 1 by 4 R2_ = Unsqueeze (R2, one_1d) R3_ = Unsqueeze (R3, one_1d) theta_then = Concat (R1_, R2_, R3_) # N by 3 by 4 # theta_then = Identity (theta) }, else_branch = g4 () => (theta_else) { theta_else = Identity (theta) } > two_1d = Constant () three_1d = Constant () five_1d = Constant () constant_D_H_W_shape = Slice (size_NCDHW, two_1d, five_1d) # [N, C, D, H, W] => [D, H, W] zeros_D_H_W_ = ConstantOfShape (constant_D_H_W_shape) zeros_D_H_W = CastLike (zeros_D_H_W_, theta) ones_D_H_W = Add (zeros_D_H_W, one_f) D_float = CastLike (D, zero_f) H_float = CastLike (H, zero_f) W_float = CastLike (W, zero_f) start_d, step_d, start_h, step_h, start_w, step_w = If (constant_align_corners_equal_zero) < then_branch = h1 () => (start_d_then, step_d_then, start_h_then, step_h_then, start_w_then, step_w_then) { # => (float, float, float, float, float, float) step_d_then = Div (two_f, D_float) step_h_then = Div (two_f, H_float) step_w_then = Div (two_f, W_float) step_d_half = Div (step_d_then, two_f) start_d_then = Add (minus_one_f, step_d_half) step_h_half = Div (step_h_then, two_f) start_h_then = Add (minus_one_f, step_h_half) step_w_half = Div (step_w_then, two_f) start_w_then = Add (minus_one_f, step_w_half) }, else_branch = h2 () => (start_d_else, step_d_else, start_h_else, step_h_else, start_w_else, step_w_else) { # => (float, float, float, float, float, float) D_float_nimus_one = Sub (D_float, one_f) H_float_nimus_one = Sub (H_float, one_f) W_float_nimus_one = Sub (W_float, one_f) # avoid divide by 0 D_equals_one = Equal (D, one) step_d_else = If (D_equals_one) < then_branch = g5 () => (step_d_else_then) { step_d_else_then = Identity (zero_f) }, else_branch = g6 () => (step_d_else_else) { step_d_else_else = Div (two_f, D_float_nimus_one) } > step_h_else = Div (two_f, H_float_nimus_one) step_w_else = Div (two_f, W_float_nimus_one) start_d_else = Identity (minus_one_f) start_h_else = Identity (minus_one_f) start_w_else = Identity (minus_one_f) } > grid_w_steps_int = Range (zero, W, one) grid_w_steps_float = CastLike (grid_w_steps_int, step_w) grid_w_steps = Mul (grid_w_steps_float, step_w) grid_w_0 = Add (start_w, grid_w_steps) grid_h_steps_int = Range (zero, H, one) grid_h_steps_float = CastLike (grid_h_steps_int, step_h) grid_h_steps = Mul (grid_h_steps_float, step_h) grid_h_0 = Add (start_h, grid_h_steps) grid_d_steps_int = Range (zero, D, one) grid_d_steps_float = CastLike (grid_d_steps_int, step_d) grid_d_steps = Mul (grid_d_steps_float, step_d) grid_d_0 = Add (start_d, grid_d_steps) zeros_H_W_D = Transpose (zeros_D_H_W) grid_d_1 = Add (zeros_H_W_D, grid_d_0) grid_d = Transpose (grid_d_1) zeros_D_W_H = Transpose (zeros_D_H_W) grid_h_1 = Add (zeros_D_W_H, grid_h_0) grid_h = Transpose (grid_h_1) grid_w = Add (grid_w_0, zeros_D_H_W) grid_w_usqzed = Unsqueeze (grid_w, minus_one) grid_h_usqzed = Unsqueeze (grid_h, minus_one) grid_d_usqzed = Unsqueeze (grid_d, minus_one) ones_D_H_W_usqzed = Unsqueeze (ones_D_H_W, minus_one) original_grid = Concat (grid_w_usqzed, grid_h_usqzed, grid_d_usqzed, ones_D_H_W_usqzed) constant_shape_DHW_4 = Constant () original_grid_DHW_4 = Reshape (original_grid, constant_shape_DHW_4) original_grid_4_DHW_ = Transpose (original_grid_DHW_4) original_grid_4_DHW = CastLike (original_grid_4_DHW_, theta_3d) grid_N_3_DHW = MatMul (theta_3d, original_grid_4_DHW) grid_N_DHW_3 = Transpose (grid_N_3_DHW) N_D_H_W_3 = Concat (N, D, H, W, three_1d) grid_3d_else_ = Reshape (grid_N_DHW_3, N_D_H_W_3) grid_3d = CastLike (grid_3d_else_, theta_3d) # grid = Identity (grid_3d) grid = If (condition_is_2d) < then_branch = g1 () => (grid_then) { # [N, D=1, H, W, 3] => [N, H, W, 2] grid_squeezed = Squeeze (grid_3d, one_1d) # [N, H, W, 3] grid_then = Slice (grid_squeezed, zero_1d, two_1d, three_1d) # [N, H, W, 2] }, else_branch = g2 () => (grid_else) { grid_else = Identity (grid_3d) } > } Constrain size's type to int64 tensors.thetaConstrain 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]Indices tensor must have rank >= 1(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.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.Constrain output types to boolean tensors.Constrain input types to float tensors.Constrain to any tensor type.Constrain input to only numeric types.depth 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) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) 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 >= 2Constrain to all tensor types.Constrain input and output types to all tensor types (including bfloat).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.Input and output types can be of any tensor type.updatesWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]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 multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Constrain input and output types to all tensor types up to IRv10.squeezedList 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).Blocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalBlocksize 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).'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.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)Constrain repeat's type to int64 tensors.repeatsFour 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). 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.sizesroiIf 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.Axis is referred to more than once.(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 axisThe 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_indices`shape` only supports `int32_t` or `int64_t` inputsCenterCropPad op must have 2 inputs.Supported modes: `constant`(default), `reflect`, `edge`, `wrap` 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], ] ``` 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 ()Number of elements of input 'shape' () does not match the number of axes (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)pad_amount_left = Div(pad_amount, k2)pad_amount = Sub(padded_sh, x_shape)input_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)sh_diff = Sub (x_shape2, shape)x_shape2 = Shape (padded_input)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.output_dataUnexpected axis value: . Expected range [initial_state_and_scan_inputstensor of bool, which should be a scalar.tensor of int64, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.v_final_and_scan_outputsAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An attribute specifying the number of scan_inputs M. The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.final_state_and_scan_outputsAll Tensor types up to IRv10.seq(tensor(float8e5m2))seq(tensor(float8e4m3fnuz))seq(tensor(float8e4m3fn))optional(tensor(float8e5m2))optional(tensor(float8e4m3fnuz))optional(tensor(float8e4m3fn))optional(tensor(bfloat16))optional(seq(tensor(bfloat16)))seq(tensor(int4))seq(tensor(uint4))seq(tensor(float8e5m2fnuz))optional(tensor(int4))optional(tensor(uint4))optional(tensor(float8e5m2fnuz))Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.condv_initialD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccOnly boolAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.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.(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.output_dtype does not match y_zero_point type D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.ccThe type of the input `y_zero_point` and the output `y`.The type of the input 'x'.(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`Constrain 'x' to float tensor.'x_scale' determines the output type.The type of the inputs 'x_zero_point' and 'x'. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.cc broadcasting: (Wrong op_type name for running propagation: Constrain input and output types to high-precision numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } Constrain input and output types to signed numeric tensors. { 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 SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, 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) } Mish { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Constrain input X and output types to float tensors. { 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) } Coefficient of ELU.Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation. Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) Two = Constant () TwoCast = CastLike (Two, X) SqrtTwo = Sqrt (TwoCast) XSqrt = Div (X, SqrtTwo) ErfXSqrt = Erf(XSqrt) Phi = Sum (OneCast, ErfXSqrt) MultX = Mul (HalfCast, X) Y = Mul (MultX, Phi) Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) TwoOverPi = Constant () TwoOverPiCast = CastLike (TwoOverPi, X) C0 = Constant () C0Cast = CastLike (C0, X) SqrtTwoOverPi = Sqrt (TwoOverPiCast) Three = Constant () ThreeCast = CastLike (Three, X) XCubed = Pow (X, ThreeCast) XCubedC0 = Mul (C0Cast, XCubed) XC0XCubed = Sum (X, XCubedC0) TanhInput = Mul (SqrtTwoOverPi, XC0XCubed) ErfApprox = Tanh (TanhInput) PhiApprox = Sum (OneCast, ErfApprox) MultX = Mul (HalfCast, X) Y = Mul (MultX, PhiApprox) The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) Constrain input X and output types to float/int tensors. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. Constrain input and output types to all tensors.The type of input b and its zeropoint.The type of input a and its zeropoint.Constrain scales.If set to 1 will perform the sums in reverse direction.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Constrain output Y data type as 32-bit integer tensor.The type of the output and its zeropoint.Constrain input and output types to floating-point tensors.axis tensor can be int32 or int64 onlyloss = ReduceMean (loss_Ndd)loss_Ndd = Squeeze (loss_N1dd, axes)loss = Squeeze (loss_N1dd, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) ignore_indexexpanded_target = Unsqueeze (target, axes)const_oneconst_zero { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } slopeConstrain input Y types to float/int tensors.Constrain input and output types to numeric tensors.data_0List of tensors for { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } { 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) } Value of beta.Value of alpha.tmp = Where (input_less_than_min, min, input)output = Where (input_large_than_max, max, input)input_large_than_max = Less (max, input)output = Where (input_less_than_min, min, input)input_less_than_min = Less (input, min)output = Identity (input)sumoutput = Where (output_large_than_max, max, tmp)output_large_than_max = Less (max, tmp) 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, axes) 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) { 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) } { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } Axis has less than the requested k elements.K input must be of type int64.K input must be a one-dimensional tensor of size 1.Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain index tensor to int64IndicesValuesaxis input must be a scalar.%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointaxis_tensor != nullptr%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()operator ()rank == static_cast(new_shape_proto.dim_size())input tensor must have rank >= 2, including the complex dimension.is_onesided and inverse attributes cannot be enabled at the same timeWhether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.If `onesided` is `1` and input is real, only values for `k` 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, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.dft_length input must be a scalar.axis attribute value is invalid for a tensor of rank . Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'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. 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. size input must be greater than 0.size input must be a scalar.Constrain scalar length types to integers.dft_length { 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) } Constrain output types to numeric tensors.Constrain the input size to int64_t.HannA {name} window with length: size. The output has the shape: [size].num_mel_binsThe 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. dft_length input must be scalar.num_mel_bins input must be scalar. { 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) } Blackmanupper_edge_hertzlower_edge_hertzsample_rateIf STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!window input must have rank = 1.frame_length input must be scalar.First input should have at least 2 dimensions in Constrain to any numerical types.Constrain to float tensorsConstrain to integer tensors.frame_lengthframe_stepsignalIf 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. const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) const_ignore_index loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss_Ndd = Mul (loss_unweighted, weight_gather)loss = Mul (loss_unweighted, weight_gather)loss_unweighted = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)loss = ReduceSum (loss_Ndd)squeeze_mask = Squeeze (mask, axes)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)loss_NCdd = Neg (input_gather_element_transform)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)const_zero_casted = Cast (const_zero_float)const_zero_floatinput_gather_element = GatherElements (input, transform_targets)weight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)const_one_casted = Cast (const_one_float)const_one_floattargetWeight rank must be 1.Input and target dimension value mismatch.Target rank must be 1 less than the input rank. loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Number of input tensors does not match the operands in the equation.NegativeLogLikelihoodLossConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.lossConstrain input and output types to all numerical tensor types.InputsEinsum expression string.Rank of input does not match the equation indices.Ellipsis represents incompatible dimensions.output = NegativeLogLikelihoodLoss (X_Log, labels)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)log_prob = Identity (X_Log) X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) Shape3DType 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.SoftmaxCrossEntropyLosslog_problabelsConstrain scalar length types to int64_t.Constrain signal and output to float tensors.'output_shape' must have same number of elements as the shape of input tensor X.'output_shape' must be rank 1 tensor.Attribute kernel_shape must be specified.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).Attribute pooled_shape must be specifiedAttribute pooled_shape has incorrect lengthRoIs tensor must have 2 dimensionsp value of the Lp norm used to pool over the input data.weight and zero_point pair is expected to have same type in input and zero_point pair is expected to have be same type in inputs are expected to have tensor type in Constrain input x and its zero point data type to 8-bit integer tensor.number of groups input channels and output channels are divided into. default is 1.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.The shape of the convolution kernel. If not present, should be inferred from input 'w'.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 pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.The pads attribute cannot be used simultaneously with auto_pad attributeStride along each spatial axis. If not present, the stride defaults to 1 along each axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain output y data type to 32-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Dilation value along each spatial axis of the kernel. Default is 1 along each axis.DeformConvStride along each spatial axis. Default is 1 along each axis.Padding 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.offset_groupNumber of groups of offset. C must be divisible by offset_group. Default is 1.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.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.If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).This number of op outputs should be 1 when Training_mode = False, but it is not.This number of op outputs should be 3 when Training_mode = True, but it is not. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Attribute pads has incorrect sizeAttribute kernel_shape must be specifiedAttribute kernel_shape has incorrect sizeAttribute strides has incorrect sizeAttribute dilations has incorrect sizeInput tensor must have at least 2 dimensionsauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\defs.ccDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain input and output types to float and 8 bit tensors.Second input tensor has wrong dimensionkernel shape should have values in Attribute kernel_shape has incorrect size.Attribute strides has incorrect size.Attribute pads has incorrect size.Input tensor X must have at least 2 dimensions.MaxUnpool op must have either two or three inputs.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. block_shapeimage_shape1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.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.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.Epsilon = Cast (FloatEpsilon)FloatEpsilon%s:%u: %s: Assertion `%s` failed.BuildContextDependentFunctionBodyLayerNormalizationsinceVersion == 17 || sinceVersion == 18Constrain input and output types to all numeric tensor types.The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.Unexpected axis value () rank of first input is Scaled = Mul (NormalizedT, Scale2D)NormalizedT = Cast (Normalized)MeanOfSquare = ReduceMean (Square, Axes_1)Mean2D = ReduceMean (XU, Axes_1)Axes_1 = Constant()XShape = Shape (XU)XU = Cast (X)num_groupsConstrain input types and output Y type to float tensors.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Shape3D = Constant ()XReshaped = Reshape (XU, NewShape)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)InstanceShape = Shape (X)N = Shape (X)GroupSize = Div (C, NumGroups)NumGroupsC = Shape (X)NormalizedU = Div (Deviation, StdDev)Deviation = Sub (X3D, Mean)SquareOfMean = Mul (Mean, Mean)MeanOfSquare = ReduceMean (Square, Axes2)Square = Mul (X3D, X3D)Mean = ReduceMean (X3D, Axes2)Axes2X3D = Reshape (XReshaped, Shape3D)BiasReshaped = Reshape (BiasT, ScaleShape)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasT = Cast (bias)ScaleT = Cast (scale)ScaleShape = Constant ()NormalizedT = Cast (NormalizedNC)NormalizedNC = Reshape (NormalizedOriginalShape, Shape3D)NormalizedOriginalShape = Reshape (NormalizedU, XShape)The floating-point precision used in stage one of the computation.The number of groups of channels. It should be a divisor of the number of channels `C`.Biased = Add (Scaled, BiasReshaped)Scaled = Mul (ScaleReshaped, NormalizedT)running_meaninput_varinput_meanConstrain mean and variance types to float tensors.Constrain scale and bias types to float tensors.running_vartraining_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The axis on which to apply normalization, -1 mean last axis.Invalid value() for attribute 'axis' { 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) } The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.The exponent.Scaling parameter.The number of channels to sum overIndicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain input and output to all tensor types up to IRv10.1-D tensor of floatsInput is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.input must have rank 3.Attribute pads must have an even size { 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) } { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } A list of integers, along which to reduce. The default is to calculate 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.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)GroupNormalizationThe mean of the normal distribution.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Upper boundary of the output values.Lower boundary of the output values.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Constrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64 { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Constrain input types to common numeric type tensors.All inputs to 'Range' op must be of the same typeBernoulliConstrain output types to all numeric tensors and bool tensors.The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.output = Cast (X_greater)X_greater = Greater (X_random, input)X_random = RandomUniformLike (input)Input to 'Range' op should be scalars (Tensor with only one element and shape empty)Attribute expected to have a one-dim tensor in Attribute expected to have tensor or sparse tensor type in Attribute expected to have a one-dim sparse tensor in value_intsThe values for the elements for the 1D, int64, output tensor.value_intThe value for the sole element for the scalar, int64, output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.The value for the elements of the output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.Constrain output types to be numerics or boolean.Constrain input types.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.ccConstrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.Input tensor must be 2-dimensionalEnvironment 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.Outputs are bools and are True where there is a full regex match and False otherwise.Inputs must be UTF-8 stringsRegex pattern to match on. This must be valid RE2 syntax.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\text\defs.ccInputs and outputs must be UTF-8 stringsTensor of substrings.The input must be a UTF-8 string tensorMaximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.List of stop words. If not set, no word would be removed from X.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falsestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0The number of substrings generated.undefinedD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.ccminus infinity (if supported by the datatype) or undefined otherwiseplus infinity (if supported by the datatype) or the maximum value of the data type otherwiseminus infinity (if supported by the datatype) or the minimum value of the data type otherwise { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } log sum exponent { 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) } log sum { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } productsum square { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Keep the reduced dimension or not, default 1 means keep reduced dimension.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).L2 norm { 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) } L1 normencoded_streampixel_formatPixel format. Can be one of "RGB", "BGR", or "Grayscale".RGBInput tensor must be 1-dimensionalD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\image\defs.ccImageDecoderConstrain output types to 8-bit unsigned integer tensor.Constrain input types to 8-bit unsigned integer tensor.imageD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\old.ccConstrain output types to be numerics.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).output = Cast (input)D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccThe input is not evenly splittableInvalid Target shape product of 0Invalid position of 0Target shape may not have multiple -1 dimensionswidth_scaleThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.tilesRanks inferred () is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.Attribute axes has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Ending indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.'axis' must be in [-rank(indices)-1, rank(indices)]Constrain input and output types to all tensor and sequence types.One float, indicates the value to be filled.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Attribute pads has incorrect lengthAttribute value for pads is required 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], ] ``` A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.'step' cannot be 0Input axes has invalid datalength of each output. Values should be >= 0.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. 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)` .values in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicates 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).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".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). 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).Supported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Which axis to concat on. Default value is 1.Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.Constrain input and output to only numeric types.constant_valueList 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`.length of each outputWhich axis to split onoutputs...rank must be greater than axisconsumed_inputslegacy optimization attribute.New shapeOne float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgepaddingsAn 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 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 Graph attribute inferencing returned type information for 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 IRv9.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\old.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.All Tensor types up to IRv9.All Tensor types up to IRv4. was not a tensor.Scan input outputs. Expected (Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.cc(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.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain input to integer tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to integer tensors.Constrain input to boolean tensor.Constrain input types to all (non-complex) tensors.Constrain input/output to boolean 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).Constrain output to boolean tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccConstrain input types to all numeric tensors.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. varThe 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. If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.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.Scaled = Mul (ScaleReshaped, Normalized)If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.XReshaped = Reshape (X, NewShape)X3D = Reshape(XReshaped, Shape3D)D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccConstrain input and output to all tensor types.STDX_varianceProcessed_STDStride along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.EX_squaredX_squaredE_XsquaredThe 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.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.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.dilation value along each spatial axis of the filter.Constrain input and output types to high-precision and 8 bit numeric tensors.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\old.cc{empty_value}The axis in which to compute the arg indices. 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 input tensor must not be empty. The type of the output tensor is integer.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).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.D:\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.Optional is expected to have an output.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.Constrain output type to all tensor or sequence types.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.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.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\old.ccDimension on which to do the sort.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, 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. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Invalid value for attribute kNumber of top elements to retrieveConstrain 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.loss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64'shape' input must be 1D tensorexpanded_targetinput_gather_elementloss_NCddX_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_LogDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size 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. transform_targetsconst_zero_castedinput_gather_element_transformsqueeze_maskconst_one_castedweight_gather_tempweight_gather_temp_1Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Value of alpha default to 0.2Pass 1 to enable broadcastingbroadcastIf set, defines the broadcast dimensions. See doc for details.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxValue of beta default to 0.5Constrain 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.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_sequencepositionConstrain 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].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 1Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Graph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_body], 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. expected to have type info expected to be a sequence typeThe 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_inputsout_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMap_itercount_cond_in_cond_out_inSequenceMap__seqlen_cond_seqempty is null. Type info is expected.Input type for input at index Element type of inputs are expected to be the same.output_sequenceConstrain input types to any tensor type.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'.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.ccSequenceConstruct is expected to have at least 1 input.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.max_output_boxes_per_classselected_indicesdefault 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\defs.ccInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccInput must be an optional-type value containing an element with type information.OptionalHasElement is expected to have 1 input.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.ccA 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.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.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].Enable broadcastingIf set, defines the broadcast dimensions.Constrain input to float tensors.Constrain input to integral tensors.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.ccD:\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 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 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) fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.Input type was null expected to have tensor or sparse tensor type. Got: unknownElement type of input expected to have sequence typeElement type of sequence input expected to have optional typeMismatched type:Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type.target sequence type missing element type.source optional type missing element type.target optional type missing element type.source map type missing key type.Element type of optional input expected to have map typeKey type of map input Value type of map input expected to have type but instead is null declared=Mismatch between number of inferred and declared dimensions. inferred= inferred=Output was expected to have tensor type. Got Input was expected to have sequence type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Element type of optional input was unknownInput was expected to have map type. Got Key type of map input was unknownValue type of map input was unknowntarget map type missing key type.Mismatched map tensor key type:source map type missing value type.target map type missing value type.Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Input element type of Input was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensorD expected to be a 1D tensor but was One and only one attribute must be set out of is undefined so it cannot be parsed.The type of tensor: Actual: content is required to be stored in repeated bytes string_data field. data type is string. string. Expected:ParseData type mismatch for tensor: load external data into raw data for tensor: Cannot parse data from external tensors. Please does not match the actual size expected size Data size mismatch. Tensor: raw_data type cannot be string.' is required to be non-empty.value_info' of 'nameutf8str_to_wstring: string is too long for converting to wstring.wstring_to_utf8str: string is too long for converting to UTF-8.domaingraph ==> Context: is deprecated in domain_version of Op registered for OpType: Bad node spec for node. Name: ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'initTensor initializers must have a non-empty nameWarning: Model contains experimental ops:op_type) has zero input and zero output., type: NodeProto (name: No opset import for domain ' with domain_version of No Op registered for ' has been used as output names multiple times. initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers is not output of any previous nodes.name: ' of node: Nodes in a graph must be topologically sorted, however input ') should be a relative path, but it is an absolute path: Location of external TensorProto ( tensor name: ' points outside the directory, but the ') should be file inside the , but it doesn't exist or is not accessible.) should be stored in ConstantFill) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_datadouble_datauint64_dataField '' is required but missing.elem_typekey_typevalue_type): Unrecognized type value case (value_info name: data_typeSTRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: ) is stored externally and should not have data field.Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!TensorProto (tensor name: ) should contain one and only one value field.) should not be stored in raw_data field values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (] not in sorted order.) first dimension size does not equal NNZ.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field.Attribute (name: ) should refer to attribute in parent node.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) dimensions are not positive.floatstype_protostringsgraphstensorstype_protossparse_tensorsint(line: column: Integer value expected, but not found.Expected character String value expected, but not found.Unexpected literal type.Encountered invalid float literal!Identifier expected but not found.opset_importir_versionproducer_versionproducer_namedoc_stringmodel_versionmetadata_propsUnhandled type: %dError parsing TensorProto shape (expected numeric dimension).Mismatch between expected type and specified value's typeEmpty list attribute value requires type annotation.Unexpected attribute type.Singleton attribute value cannot be specified as a list.Value expected but not found.Incomplete string literal.Expecting primitive type as map key type.Unexpected type.Unexpected type in sparse-tensor element type.Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).stol argument out of rangeinvalid stol argument[ParseError at position Error context: input and zero_point pair is expected to have same type.unk__Graph has inputs but The number of graph input cannot be smaller than the number of node inputCannot use the same name as both a subgraph initializer and subgraph input: Graph initializer names must appear after the actual inputs: were provided.Cannot find missing input: in initializers. Cannot infer type and shape for node name . No opset import for domain Inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set. were provided(op_type:, node name: Inferred elem type differs from existing elem type: (: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension Data for input already exists.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.node does not contain a graph.sparse_tensor_typeopaque_typetype case mismatch. existing=NOT_SETtype case unsupported. existing=key type mismatch from MapProto. existing= optype type case unsupported for symbolic shape inference. inferred=optional_typeThe input tensor and grid tensor must have the same rank for GridSample. Got input tensor rank: The input tensor and grid tensor ranks must be >= 3. Got input tensor and grid tensor ranks: The last dimension of the grid tensor must be the rank of the grid tensor - 2. Got the last dimension of the grid tensor: Dimension value inferred () is not equal to the existing dim value (Got grid tensor rank: values.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. Got 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' (Input 'scales' must have float element type.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 One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_float' expect a float.Attribute 'value_int' expect an integer.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_string' expect a string. Computes the {name} of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. axes as an input and attribute cannot be specified at the same time. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. If the input data type is Boolean, the comparison should consider `False < True`.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.Constrain input and output types to numeric and Boolean tensors.      FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDBFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16FLOAT8E4M3FNFLOAT8E4M3FNUZFLOAT8E5M2FLOAT8E5M2FNUZINT16INT32INT4INT64INT8STRINGUINT16UINT32UINT4UINT64UINT8UNDEFINEDonnx.StringStringEntryProtoonnx.ModelProtoonnx.GraphProtoonnx.TensorAnnotationonnx.TensorProtoonnx.TensorProto.Segmentonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.ValueInfoProtoonnx.AttributeProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.FunctionProtoonnx.OperatorSetIdProtoonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.SparseTensoronnx.TypeProto.Optionalonnx.TypeProtoonnx.TypeProto.OpaqueUnknown encoding D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.ccError compiling 'Error parsing 'Error reverse compiling 'pattern too large - compile failedprogram size pattern length bytemap range list count SearchOnePass inconsistencySearchDFA inconsistencySearchNFA inconsistencySearchBitState inconsistencyInvalid RE2: startpos: RE2: invalid startpos, endpos pair. [text size: endpos: DFA out of memory: Unexpected re_anchor value: {e{eNumCapturesWalker::ShortVisit calledinvalid named capture groupWalk NULLStack not empty.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hRegexp not destroyed.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccunexpected )missing )no argument for repetition operatortrailing \bad repetition operatorinvalid repetition sizeinvalid UTF-8invalid perl operatorUnexpected op in Regexp::Equal: Bad reference count unexpected errorno errorinvalid character classinvalid escape sequencemissing ]invalid character class range ++H+0+p+`+(**`*8**p***8)should never happenD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccNo ranges in char classCompiler::Copy called!Missing case in Compiler: LoLmMnNlNdMcBad call to ParseState::ParsePerlFlagsPcunknown round: Concat of Bad hex digit RE2: unexpected op: LuAnyLtLlD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccRepetitionWalker::ShortVisit calledAddFoldedRange recurses too much.Bad args: nsubmatch=context does not contain textUnexpected opcode in short circuit: in AddToThreadqUnhandled in stepD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.ccunhandled D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.cc|NULL state in RunStateOnByteDeadState in RunStateOnByteStateSaver failed to restore state.Unexpected special state in RunStateOnByteFailed to analyze start state.unhandled opcode: RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetGrowStack() failed: njob_ = D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccjob_.size() = Unexpected opcode: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccCannot use SearchOnePass for unanchored matches.Unexpected opcode in IsMatch: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.cc max Malformed repeat of min Case not handled in ComputeSimple: CoalesceWalker::ShortVisit calledD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccSimplifyWalker::ShortVisit calledSimplify case not handled: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is `9;@9;P9p;9p;9;p9;9:`::p::@::P:;:;:;:;:;9;9:9:9; :;0:x;:x;:L;0;L;@;:;: ;P;`;P;9;9;9:9:9:\9:[:^alnum:][:alpha:]\W[:alnum:][:^ascii:][:blank:][:^alpha:][:ascii:]\d\S\w\D\s[:^print:][:punct:][:^lower:][:print:][:^space:][:upper:][:^punct:][:space:][:^cntrl:][:digit:][:^blank:][:cntrl:][:^graph:][:lower:][:^digit:][:graph:]09AZ__az ~ 09AZ__az!~[:^word:][:xdigit:][:^upper:][:word:]AZ09AFaf[:^xdigit:]AZaz !/:@[`{~09AZazaz0909$dxDdhPd,d`s$8d(dўd@؞dH؞`de e،dƞe|d@Ԟd`ԞhdPtdXƚ dysdhdud@eўeڞeߞeƞeperepx[0Re@eHep(e0e՞xee蛞XeߞhepԞxaa(מXagpaXԞaPמa0 aȼ auahv a@ٞaf!ra ڞ8aȒHaШ(a@  ,asHbصXb쎞4bAACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! ! !!!!!!!$!$!&!&!(!(!*!-!0!3!>!?!E!E!!!,/,`,`,b,d,g,g,i,i,k,k,m,p,r,r,u,u,~,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjll""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~§§ħǧɧɧЧЧ֧֧اا!:[]a3@x #AAJJX_y        @ H } ~     X _ x      0 9 `~&QTRo6?PYPY0;PYPlPYPY$n$`jijjjPkYk[kaknn`x@IPYq-/= @E.0/011122`2~2`|װנZpZs-0>      ( * 0 2 3 5 9 < D G H K M U W \ ] _ c f w P    `!!!!00!0)080:022PRPv`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~,_,:@[    : : .... . . . ...!.!.@kCkoooooo0m7=KK0coo__? @ T T 34MO??55OOoo@j^j`jijnjoj  0J'pz|  @n_n4Mh  89;>@DFFJPl!<Up4Vn!A0000`l$$   ?? 88ii@[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++.]./0000 00070<0?0000000111111 2_22222X33MM!09..ϩϩ[[jk>?0RTfhk ;@[eppª۪ߪooWW  ? ? 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P X     9 ?   UYGM@Ctu8=KOZ[]]AC`l<>;;DF?F AEpqCOp$t$//njojjj7k;kDkDknnoo^_0 9 fo6?PYPY09PYPYPYPY`jijjjPkYk@IPY  #-/!#$&* ' 0 9 oop@UW_1VY` "UUdg,oK_pp0J#%')-Y[ : : < < A H M M Q W b c   < < A B G H K M Q Q p q u u   < < ? ? A D M M U V b c   < < > @ F H J M U V b c  ; < A D M M b c 114:GN557799q~-0279:=>XY^`qt]_23RSrs   "'(229;VVX^``bbels|446:<<BBks,367 ,,----*0-000oot} %&,,ĨŨ&-GQ).1256CCLL|| /  7?ywx  ??@DFFJPR46NPnp%*0m,7=NNCKK!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~ߦ9@ ]JP#7;IMOp 68EPl09`if o f o f o r w f o x ~ X ^ f x PY 3@Ii|FOPY@IPYp p t y P!!!!`$$$$v'',,00!0)080:011 2)2H2O2Q2_22222 )05Ш٨ Щ٩PYaz      !!##%%''))++--//11335578::<<>>@@BBDDFFHIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwzz||~      !!##%%''))++--//1139<<?@BBGGIIKKMMOqqssww{}0_aacceeggiikkmmooqqssuuwwyy{{}}      !!##%%''))++--//`+kwy      !!##%%''))++--//1133557799;;==??AACCEEGGIIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwyy{{}} '07@EPW`gp} ! !!!!!/!/!4!4!9!9!?ADGHKMWWbc57@AEE02;<>> !&&,.880578==@@BB99WX//>>45>?AAQooooefmrMRu'((@[^^   578;FPYoKUppQ T  *0-000 -//!#%'**,,./:;?@\\~~Z_    jm 0>^^d e p p v v w w OOZ[JO`hnn56 DEZ`}~;?~  ' 0 8 ; > A C G Q S S U ^ ,,,,p-p-.... . .........*...0.9.<.?.A.A.C.O.R.T.00=0=000 ss~~twΨϨ./__ͩީߩ\_ުߪ00EFILPRTW_ahhjk  <>@@ x  6::<=?GPY ^`|LP~ttzzYY@@$$((q q FFCCx},jxxq q   |,},o-o-/./.001050;0;00000 ppϩϩppݪݪ\_iipp (*02359<DGHKMPPWW]cflpt-//         5 8 : ? H P X ŨΨ٨ pr p r w x ~ X ^ p x *3i|p p t y P!_!!!`$$$$v'',,11 2)2H2O2Q2_222220546G ?AJnoq/M@X`jp 9 = = P P X a r     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ] ] ` a     : = = N N T V _ a z  023@E@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo1@Q`lnp BDxPmpt T3EL#MOZw5!8!0-g-------------------00<0<0A000000001/111111114MNФ *+nn妏 "@s %0F`|(@BDK`oqvzz~ªª۪ܪ  &(.װmp(*68<>>@ACDF=Pptvfoq`   `lnprs0-g-o-p---6@MPY\_  ; ; > @ I L N O   > @   > > @ @ G H K L W W   A D   > @ F H J L W W >?+,1188;<VWbdgm44#&)+0138UUWWaacdmr55;;=ACD$+45.0/0#$''èRS/034MM{{}}?{%d,}(?:)Bad final char: \C^{%d}{%d,%d}D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\tostring.cckRegexpCapture cap() == 0 [truncated](?:(){}[]*+?|.^$\[^\x00-\x{10ffff}]?P<>\f\x{%x}(?-m:$)\z$(?-m:^)(?HaveMatch:%d)[]^-\\b\BAZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&C&*&'%EETpsvw{}t&%@?C  3@60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}8`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,/,00,_,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~Z0ZZZZZZZZçħħŧŧZƧƧuǧʧЧѧ֧٧SS`p0h!: AZ'((O(pz'|'''  @   @n_n `nn!""C*** Check failure stack trace: *** (stacktrace: [%c0000 00:00:%02d.%06d %7d RAW: Check %s failed: %sReaderUnlock Wait on ReaderLock returning Unlock SignalAll on D:\a\_work\_temp\abseil_cpp\abseil-cpp-20240722.0\absl\synchronization\mutex.ccWait unblocked Signal on TryLock failed ReaderTryLock succeeded TryLock succeeded Lock returning ReaderLock blocking ReaderTryLock failed Lock blocking detected illegal recursion in Mutex codes->waitp != nullptr || s->suppress_fatal_errorsbad skip in DequeueAllWakeablepw->skip == nullptr %p%s%p %s %s @detected illegal recursion into Mutex codes->waitp == nullptr || s->waitp == waitp || s->suppress_fatal_errorscondition untrue on return from LockSlowthis->LockSlowWithDeadline(how, cond, KernelTimeout::Never(), flags)waitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorsLockCheck (v & (kMuWriter | kMuReader)) != (kMuWriter | kMuReader) failed: %s: Mutex corrupt: both reader and writer lock held: %pCheck (v & (kMuWait | kMuWrWait)) != kMuWrWait failed: %s: Mutex corrupt: waiting writer with no waiters: %p%sillegal skip from headold_h->skip == nullptrMutex queue changed beneath usold_h == nullptr || h->maybe_unlockingpw->next == wunexpected empty wake listh->skip == nullptrpw not w's predecessorEnqueue to list failedUnlockEnqueue to empty list failednew_h != nullptrwaitp != nullptrwaiters disappeared during Enqueue()!waitp == nullptr || waitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorsUnlockSlow is confusedwaitp->thread->waitp == nullptrthread should hold at least a read lock on Mutex %p %swaiting when shouldn't bewake_list != kPerThreadSynchNullslow release  hp  P`SleepConditionVariableSRW failed: %luD:\a\_work\_temp\abseil_cpp\abseil-cpp-20240722.0\absl\synchronization\internal\win32_waiter.cc0123456789ABCDEF%   *.*NAN0123456789ABCDEF0123456789abcdef#-00@@@@@@@@@hHHHH@@@@@@@@@@@@@@@@@@(@000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f707172737475767778797a7b7c7d7e7f808182838485868788898a8b8c8d8e8f909192939495969798999a9b9c9d9e9fa0a1a2a3a4a5a6a7a8a9aaabacadaeafb0b1b2b3b4b5b6b7b8b9babbbcbdbebfc0c1c2c3c4c5c6c7c8c9cacbcccdcecfd0d1d2d3d4d5d6d7d8d9dadbdcdddedfe0e1e2e3e4e5e6e7e8e9eaebecedeeeff0f1f2f3f4f5f6f7f8f9fafbfcfdfeffD:\a\_work\_temp\abseil_cpp\abseil-cpp-20240722.0\absl\base\internal\low_level_alloc.cce == foundelement not in freelistlevel >= 1block not big enough for even one levelsum >= aLowLevelAlloc arithmetic overflowleft_haven't left Arena regionf->header.magic == Magic(kMagicAllocated, &f->header)bad magic number in AddToFreelist()reinterpret_cast(prev) + prev->header.size < reinterpret_cast(next)malformed freelistarena->allocation_count > 0nothing in arena to freef->header.arena == arenabad arena pointer in AddToFreelist()next->header.magic == Magic(kMagicUnallocated, &next->header)bad magic number in Next()i < prev->levelstoo few levels in Next()prev < nextunordered freelistnext->header.arena == arenabad arena pointer in Next()s->header.arena == arenanew_pages != nullptrVirtualAlloc failedntdll.dllRtlCaptureStackBackTrace[%s : %d] RAW: %.*s ... 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