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? ???p?p?`?P? ? ???@@@@@@@@@@p@@@@p@`@@@@@@@p@`@@@Ѐ@@@@0@ @~@~@~@p~@@@@@@@@@0@0@ @@@@~@~@І@І@p@@`~@`~@P~@@~@P@P@t;@@P@P@@@0@@@@@~@~@}@}@@@@@0~@0~@ ~@~@@@t;@@@`u;@AAPA@AAAAAAAAAAAApAAA AA0A0A AAAAPAAAAAAAAAAAAAA@A@A0A AAAAAAAAApApA`APA@A@A0A AAAAAAAAApApA`APAAAAAAAAA@A@AAAAAAA`A`APA@AAAAA0A0A AAAAAAAAAAAAAApApA`APAAAAAAAAA`A`APA@A0A0A AAAAAAЃBЃBABBBABBBBBBBB:BЈBЈBBBBBBBBB@B0BBBЂBB B B;BBBBB@B@B0B BBBBBЍBЍBBBBB;B@B@B0B BpBpBBBpBpB`BPBBBABBBpB`BBB`BPBBBBB B BA:BBB0B BPBPB@B0B C Cp@CCCCCCC@C@C@CCC0C0C CCCCCCCCp@C`C`CPCPC0C0Cp@ CCC @pCpCpCCCpCpCp@`CpCpCP~@`CCCCC`C`CPC@CCCp@pCCCp@CPCPC}@@CCCCC C CCCPCPC@C0CCCp@C0C0CCCCC`CPCCCCCPCPC CCCCCCCCp@CSDSDSDpSDODODNDNDQDQDPDPDQDQDPDQD0OD0OD ODODPDPDPODPDpPDpPDPOD@ODSDSDSDSD`SD`SDQDQDdDdDcDpcDeDeDSDeDeDeDdDdD`cD`cDPcD@cDDDDDDDDD0D0DA DDD`BDDDBDDDDDpDpD`DPDDDPD@DDDDDDDDDDDDDDDDDDDDDDDDD@D@D0D DpDpD0D DDDADDD0BDpEpE A`EEEEEEEAE0E0E EEEEEpEEE`AE0E0EA E E EAEEEPAEPEPE@E0EEEAEEE0AEEE`AEPEPE0A@EEEAE`E`EPE@EEEAEEEAEEEEEEE EE0E0EA EEEAE E EAEEE AEEEpE`E E EEE@E@EA0EEE`AEEEAEEEpE`EEEEEPEPE@E0EPEPEA@E`E`E APEEEEEEEEEEEAEEEEEpEpE`A`E` F` F AP F@ F@ F0 F F@F@F Fp F F F F F@ F@ F A0 F F F F F F F F FFFpF`Fp Fp F` FP F F F F F`F`FFFFFFpFFFFFPFPF@F0FFFFFFFОFFFFpF`FFFFFFFFF F FFFpFpF C`FPFPFPF@FFF CFFFFpF0F0FFFFF@F0FFF`u;FFFt;FFFFFFFFFFFFFqGrG0rGrGsGsGtGvG@GGH0LHGG0@pHHP6KHKHGHH JH0jH0sHGG0@lHP6qHpHG0mHPoH0HЗHGG0@(HP6,Hp+HG)H*H00 HHGG0@ HP6HPHGp HH0&H -HPG@G0@(HP6,Hp+HG)H*H00HЕHGG0@HP6`HHG@HЅH00HHGG0@HP6`HЧHG@HH0 .H3HpG`G0@.HP62H2HGP/H1H0HPHGG0@0HP6HHGHH0@HHpG`G0@HP6@HHGЊH H00G0 HGG0@0HP6PHHGHH0PHPHPG@G0@HP6`HHG@HЅH0G GGG0@GP6 G`GGG`G0P}H`H0G G0@p~HP6HHG~H H0GGGG0@PGP6GpGGG G0HHGG0@HP6PHHGHH0tH`|HGG0@`vHP6pzHyHGvHxH0MHiHGG0@RHP6cH_HGSHZH0H &H0G G0@HP6"H HGHH05HGHGG0@7HP60CH?HG8H= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %I64uLvQXvQ`vQhvQD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xty10invalid string position...\\?\\/INVALID_ARGUMENT:D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t., D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): map/set too longunordered_map/set too longinvalid hash bucket count/\ was false. Stacktrace: Cpuai.onnx.mlai.onnx.trainingai.onnx.preview.trainingD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetD:\a\_work\1\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()VIWEFCPUExecutionProviderORT_LOAD_CONFIG_FROM_MODELInteger overflowSafeIntExceptionHandler::SafeIntOnOverflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Gettried creating tensor with negative value in shapesize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryonnxruntime_profile_input name cannot be emptyoutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.input array is too shortUnsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.ccSequences must have tensors of the same data type. There was at least one tensor in the input that was different.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValuethis API does not support stringslocation dimensions do not match shape sizeinvalid location rangeCUDA execution provider is not enabled.The given version [%u] is not supported, only version 1 to %u is supported in this build. 1.7.0T Tensor type mismatch. utils::IsPrimitiveDataType(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscript != onnxruntime::DataTypeImpl::GetType() == type_len >= 0 && static_cast(len) < std::numeric_limits::max()OrtCreateMapMLValueEncountered nullptr.not implementedexecution_mode is not validgraph_optimization_level is not valid is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'custom op registered at runtimeD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInputTThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.all typesonnxruntime::CreateCustomRegistrytype_id_counter == 1OutputT0No requested allocator availableEnv is nullOrtMemoryInfo is nullD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccenv_ptr == p_instance_OrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputp_providerbad castD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::DefaultLogger]CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProvider has already been registered.D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.hTrying to get a SparseTensor, but got: Provider onnxruntime::ExecutionProviders::AddIsSparseTensor()Compute_Create_State_onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hRelease_State_MemcpyTransformer[json.exception.ORTM] other_errorsession.use_env_allocatorssession.set_denormal_as_zerosession.load_model_formatsession.save_model_format31.4.0%Y-%m-%d_%H-%M-%S are '0' and '1'. The only supported values for the environment variable The environment variable contained the value: D:\a\_work\1\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itReading the provided model for the ORT configonnxruntime::FinalizeSessionOptionsoffFlush-to-zero and denormal-as-zero are is_model_proto_parsedonstatus.IsOK()onnxruntime::InferenceSession::ConstructorCommononnxruntime::InferenceSession::ConstructorCommon::::operator ()Could not finalize session options while constructing the inference session. Error Message: intra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALgraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truesession_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.Could not parse model successfully while constructing the inference sessionst.IsOK()Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessiononnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()resultError during EndProfiling(): Execution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProvideronnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec providerHaving memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized.So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the CUDA Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::SaveToOrtFormatonnxruntime::InferenceSession::LoadUnknown exception in Load()Exception during loading: This session already contains a loaded model.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Failed to load model because protobuf parsing failed.Encountered unknown exception in Load()ORTModelProto 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_arrayCould not find an implementation for the node (model_loading_from_saved_protoonnxruntime::InferenceSession::TransformGraph].All nodes have been placed on [ (Node placementsonnxruntime::InferenceSession::PartitionOrtFormatModel: [ Provider: [This session has already been initialized.ORT model verification failed.onnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModel] is not supported this build The ORT format model version [InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()Missing Model. Invalid ORT format model.SessionState is null. Invalid ORT format model.onnxruntime::InferenceSession::InitializeModel was not loadedonnxruntime::InferenceSession::Initialize::::operator ()Initializing session.Adding default CPU execution provider.This session will use the allocator registered with the environment.Model was not loaded.Session has already been initialized.Session successfully initialized.Encountered unknown exception in Initialize()Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Expected: Got: session_initialization Please fix either the inputs or the model.Got invalid dimensions for input: index: Invalid rank for input: for the following indices )) elements.Unexpected input data type. Actual: ()) , expected: (Invalid Feed Input Name: is not expected to be of type tensor.elements, but feeds has Size mismatch: feed_names has onnxruntime::InferenceSession::ValidateInputs is not expected to be of type sparse tensor.Input with name: tensorseqOutput vector pointer is NULLsparse_tensor is not expected to be of type tensor sequence.Output vector incorrectly sized: output_names.size(): Invalid Output Name:At least one output should be requested.p_fetches->size(): Session not initialized.Running with tag: Session was not initializedonnxruntime::InferenceSession::Runonnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::GetModelInputsEncountered unknown exception in Run()model_runonnxruntime::InferenceSession::NewIOBindingProfiler is disabled.onnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::CreateLoggerForRunInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)graph_optimization_level <= TransformerLevel::MaxLevelonnxruntime::InferenceSession::AddPredefinedTransformersonnxruntime::InferenceSession::InitLoggerExceeded max transformer level. Current level is set to /961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1model_loading_uri failed:.json_ bytes were able to be read. failed. Only Load model from onnxruntime::LoadOrtModelByteslist too longonnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0com.microsoftcom.microsoft.mlfeaturizerscom.microsoft.nchwccom.microsoft.experimental[TypeInferenceError] expected to have tensor typeOutput Element type of input unknownInput expected to have type but instead is null expected to have sequence type[ShapeInferenceError] tensor(int32)tensor(uint64)tensor(uint32)unknowntensor(double)tensor(float)tensor(float16)tensor(int64)tensor(int8)tensor(uint16)tensor(uint8)tensor(bfloat16)tensor(int16)tensor(complex128)tensor(complex64)tensor(bool)tensor(string) version: ) from file , but it is already registered from file line , but it its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: , but it its version is not in the inclusive range [Only CPU devices are supported for now.Allocator with this OrtMemoryInfo is already registered.D:\a\_work\1\s\onnxruntime\core\session\environment.ccinter-opException caught: Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.outputXinputMemcpyFromHostMemcpyToHostConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Y: value'parse error at line , column parse_errorort_configout_of_rangetype_errorinvalid_iteratorD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: session_optionsSetting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting intra_op_num_threads to Parallel modeSequential modeonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: Setting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to Unsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to onnxruntime::SetEnableProfilingORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.intra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.execution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsintra_op_num_threads option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelexecution_mode option in the model file must be an integer' not foundIgnoring unsupported session option in ORT config: enable_profiling option in the model file must be an integercannot use at() with key 'nullinvalid map keycannot use key() for non-object iteratorsbooleanstringarrayobjectnumberdiscardedcannot get valuecannot compare iterators of different containers- while parsing syntax error ; expected unexpected ; last read: 'true literalinvalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if givennumber literalstring literalnull literalfalse literal'}'']''{''['end of input','':'invalid number; expected digit after '.'invalid number; expected digit after '-'unknown token'[', '{', or a literalinvalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dvector too longobject separatorobject keynumber overflow parsing 'iterator does not fit current valuetype must be number, but is cannot use erase() with iterator out of rangeD:\a\_work\1\s\include\onnxruntime\core/graph/graph.hnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:onnxruntime::Graph::NodeAtIndexImplInserted_CastD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.honnxruntime::GraphTransformer::RecurseRemoveDuplicateCastTransformertoCastcast node to cast from float16 to float32 on cpudtypeonnxruntime::InsertCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::RemoveDuplicateCastTransformer::ApplyImpldtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.ArmNNExecutionProviderACLExecutionProviderAttentionFusionCommonSubexpressionEliminationCastEliminationBiasSoftmaxFusionBiasGeluFusionConvActivationFusionConvMulFusionConvBNFusionConvConvAddFusionEmbedLayerNormFusionDynamicQuantizeMatMulFusionDropoutEliminateDropoutGeluApproximationFastGeluFusionExpandExpandEliminationIdentityEliminateIdentityGemmActivationFusionGeluFusionMatMulIntegerToFloatFusionMatMulAddFusionSimplifiedLayerNormFusionLayerNormFusionFuseReluClipNhwcTransformerNchwcTransformerMatMulScaleFusionShapeShapeToInitializerReshapeFusionReluUnsqueezeEliminationSliceEliminateSliceSkipLayerNormFusion_RuleBasedTransformersession.enable_quant_qdqUnsqueezeD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelLevelonnxruntime::optimizer_utils::GenerateTransformersUnsupported level VitisAIExecutionProvideronnxruntime::Node::ForEachWithIndexD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImplMemcpyExecution type '' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessDefsonnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()dup_replacements.find(&arg) == dup_replacements.end()Copy from/to host memoryonnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: Could not find OrtValue with name 'onnxruntime::ConstantFolding::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold ConstantFoldingDequantizeLinearCould not find a CPU kernel and hence fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of Unsupported data type: shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccDivConstant initializer NodeArg shape should not be null. NodeArg: input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesdiv_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeMulmul_inputs.size() == 2Fused MatMul and Scale_FusedMatMulAndScaleonnxruntime::MatMulScaleFusion::ApplyImplMatMulFusedMatMulTransposeMatMulalphaonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()D:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsSlice ends is less than INT_MAXExpected value:input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersGemmConcatSqueezeFaild to match gemm pathSlice parameter is not expected. Input index:Start MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsInput 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 is not constantOutput edge count not expected for nodes in gemm gather pathaxesunsqueeze_after_gather axes value not expectedaxisconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueGatherFaild to match gemm gather pathonnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializergather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphStart ValidateGemmInitializerunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatGemm weight shape is not expectedPass ValidateGemmInitializerunidir mask is not constantonnxruntime::AttentionFusionHelper::ValidateUnidirMaskSubFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphWhereFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphSoftmaxFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Output 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: 2Failed to find Softmax nodeOutput edge count not expected for SoftmaxAddFailed to find path for maskEqualFailed to find mask pathwhere const not matched.Failed to find shape pathmask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertPass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphTransposeequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.permpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVpresent_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 inputhidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedonnxruntime::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, 3q_transpose perm attribute not matchedPass CheckNodesInPathQStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.Cast mask from int64 to int32MaskCastStart FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32MatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeFaild to find path to qkv_matmulSplitFaild to find path v to SplitCheckNodesInPathV return falseMatchPastSubgraph returns falseFused Attention subgraphs Attentionnum_headsCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMask 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 float32unidirectionalFused an attention node for GPT.qkv_weightsqkv_biasonnxruntime::FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normonnxruntime::AttentionFusion::ApplyImplLayerNormalizationshape of layer norm bias tensor not expectedTotal fused Attention node count: onnxruntime::FuseSubGraphQKFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFaild to find path vk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32onnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphbn_scale_tensor_protoepsilonD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::ConvBNFusion::Applyonnxruntime::Initializer::ToProtodata type is not supportedConvBnFusion_W_conv_B_tensor_protoBatchNormalizationConvBnFusion_BN_B_bn_mean_tensor_protobn_B_tensor_protoconv_W_tensor_protobn_var_tensor_protominCliponnxruntime::ConvActivationFusion::ApplyImplmaxUnexpected data type for Clip input of D:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()FusedConvfused Conv activationfused TanhSigmoidwith activation LeakyReluactivation_paramsonnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccUnsqueezeElimination_onnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of _min_zero_constantD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.ccdata type is not supported.FuseReluClip_BiasGeluonnxruntime::GeluApproximation::ApplyImplGeluGelu approximationD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: FastGeluhQXQQendsstartsonnxruntime::ReshapeFusion::Fuse_SubgraphCannot replace concat node with initializer:D:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::ApplyImplTotal fused reshape node count: Fused reshape node: _Int32_CastCast Input from int64 to int32onnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Output edge count not expected for nodes in path 1 of position shape.onnxruntime::CheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64D:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cctwo paths share the same shapeThe parent of two shape nodes are expected to be input_ids.ConstantOfShapeNonZeroOutput edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape.Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.Second input of Gather should be a constant with value 1. The third input of Range should be a constant with value 1.The parent of shape nodes are expected to be input_ids.Failed to match Shape node. Output edge count not expected for nodes in path1.RangeThe first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherfused EmbedLayerNorm subgraphs mask_indexWord embedding shape not expected.EmbedLayerNormalizationOptional position subgraph nodes Where node is expected to be the parent of Reshape.Optional position subgraph nodes number of outputs unexpected.position_embeddingsFailed to match position subgraph.Failed to match position embedding subgraph.Position embedding data type shall be float or float16.Position embedding shape is not expected.Failed to get position embedding weights.Input is expected to have dim value in all dimensions.onnxruntime::FuseSubGraphPosition embedding shape not matched.Failed to get initializer tensor.onnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). onnxruntime::EmbedLayerNormFusion::ApplyImplSegment id is not valid. Input id is not valid. Gamma should be of shape (hidden_size). Input_ids and segment id should have the same shape. ̼+onnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeMatMulFreeDimensionOverrideTransformerInvalid free dimension override.The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplwith a fixed dimension size ' Conflicting free dimension overrides.which does not equal the specified override of onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add onnxruntime::MatMulAddFusion::ApplyImplEluHardSigmoidonnxruntime::GemmActivationFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccParametricSoftplusThresholdedReluSoftsignScaledTanhSeluSoftplusactivation_fused Gemm FusedGemmonnxruntime::ConvMulFusion::ApplyConvMulFusion_W_ConvMulFusion_Mul_B_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protofused LayerNorm subgraphs onnxruntime::SimplifiedLayerNormFusion::ApplyImplSqrtPowD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccReduceMeanonnxruntime::LayerNormFusion::ApplyImplSimplifiedLayerNormalizationinvalid unordered_map key'7SkipLayerNormalizationD:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs onnxruntime::SkipLayerNormFusion::ApplyImpl̼+NOTSETauto_padkernel_shapedilationsstridespadsReorderInputreorderspatial_nchwcgroupVALIDSAME_LOWERSAME_UPPERasymmetriccoordinate_transformation_modenearestmodechannels_lastchannelsReorderOutput_bn_nchwcResizeSumAveragePoolMaxPoolscalesUpsamplefloornearest_modeD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImplGlobalAveragePoolGlobalMaxPooldeque too longROCMExecutionProvider and D:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-D:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImplbroadcast_axissoftmax_axisBiasSoftmax into softmax(input + bias)Not eliminating output of node [] because it's the graph's output.representative.output_index != kInvalidOutputIndexD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::CommonSubexpressionElimination::ApplyImplnhwc_permutated_padsstorage_orderNhwcMaxPoolQLinearConv_nhwcD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplPadQLinearGlobalAveragePoolQLinearSigmoidQLinearLeakyReluQLinearMulQLinearAddonnxruntime::MatMulIntegerToFloatFusion::ApplyImplMatMulIntegerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.ccfused Gelu subgraphs ErfD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::GeluFusion::ApplyImplonnxruntime::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 onnxruntime::ConvAddFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_External data type must not be UNDEFINED or STRING.onnxruntime::Initializer::ReadExternalRawDataD:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccmodel_path must not be empty. Ensure that a path is provided when the model is created or loaded., external_data.length: Computed size: TensorProto external data size mismatch. RandomUniformLikeRandomNormalLikeMultinomialRandomUniformRandomNormalonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=DnnlExecutionProviderOpenVINOExecutionProviderNupharExecutionProviderNnapiExecutionProviderRknpuExecutionProviderMIGraphXExecutionProviderXQ@QX1R%R0R0R1RQ(1RQQxQ@1R|Qonnxruntime::RegisterOnnxOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel.hLogSoftmaxonnxruntime::OpKernelContext::InputD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13Missing Input: onnxruntime::Softmax::ComputeImplOpset13, D=D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.ccHardmax inputs N, D and N * D must be < . N=Hardmaxonnxruntime::Hardmax::ComputeVBonnxruntime::If::Ifinfo.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()else_branchinfo == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.CreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::If::SetupSubgraphExecutionInfo outputs.If'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)then_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::IfImpl::ExecuteFailed to create output tensor for If output onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.onnxruntime::IfImpl::Initializesession_stateOnly tensors or sequence of tensors are suppportedFailed to create output tensor for onnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size() but has D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cc'Loop' node has outputs so the subgraph requires Got:num_subgraph_outputs - 1 == num_outputsLoopIGraph in 'body' attribute of Loop should have inputs. Found:feeds_fetches_manager_onnxruntime::Loop::Compute'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of last_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsonnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsInconsistent shape in loop output for output. Expected:info.GetAttr("body", &proto).IsOK()bodyinfo_ == nullptronnxruntime::Loop::LoopSubgraph SessionState was not found for 'body' attribute.onnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::LoopImpl::Execute::::operator ()Unable to get an allocator was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::ExecuteLoop had zero iterations and the shape of subgraph output extrapolation_valueinfo.GetAttrs("scales", scales_).IsOK() model uses the deprecated attributehalf_pixel`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset cubic_coeff_around_prefer_floorlinearinfo.GetAttr("mode", &mode).IsOK()cubicD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBasealign_cornerstf_half_pixel_for_nn] is not supportted!tf_crop_and_resizeonnxruntime::UpsampleBase::StringToCoordinateTransformationModecoordinate_transform_mode:[ceilround_prefer_ceilexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to exclude_outside(default) or or mode attribute is . It can only be pytorch_half_pixelonnxruntime::UpsampleBase::StringToUpsampleModeUpsample operatorResize operatorscales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales_size > 0scales size should be greater than 0.nearest_mode:[] is not supported!Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModeonnxruntime::UpsampleBase::ScalesValidationscale >= 1scale > 0Scale value should be greater than 0.T1onnxruntime::UpsampleBase::ParseScalesDataArgMaxReduceSumSquareInput to be reduced is nullArgMinonnxruntime::SetupForReduceinput_tensor_ptr != nullptrThe input shape override's size does not match the input tensor's shape sizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccReduceL2ReduceL1ReduceLogSumExpReduceLogSumReduceMinReduceMaxReduceSumReduceProdinput_tensor_ptr->Shape().Size() == input_shape_override->Size()keep_dimsCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceSum::Implonnxruntime::ReduceSum::Implinfo.GetAttr("keepdims", &keepdims).IsOK()keepdimsD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<0>::ReduceKernelBaseselect_last_indexnoop_with_empty_axesonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseAn axes tensor must be a vector tensor.onnxruntime::CommonReducekeepdims_axes_tensor->Shape().NumDimensions() == 1count == 1Reduction on all axes, output size should be 1.last_results.last_loop_red_size > 0onnxruntime::NoTransposeReduceonnxruntime::ReduceSum::Implonnxruntime::ReduceSum<__int64>::Implaxes_tensor != nullptrAxes input is nulllast_results.projected_index.size() > 0last_results.last_loop_size > 0constantInvalid 'mode' attribute valueonnxruntime::PadBase::PadBasereflectedgeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueonnxruntime::SliceSkips::SliceSkipsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()pads_tensor.IsDataType()onnxruntime::Pad::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorPads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankPads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::InitUnsupported input data type of onnxruntime::PadValueFromFloatCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1data_rank * 2 == pads.size()onnxruntime::PadImpl'pads' has wrong number of valuesonnxruntime::PadInputWithDimValueOfZeroInput tensor has no dimensionsdata_rank > 0batch_dimsGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrTindGatherNDlast dimension of indices must not be larger than rank of input tensorindices tensor data type not supportedonnxruntime::GatherND::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor must has rank larger than 0invalid index found, index = X input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!onnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeT2IsNaNNull input ptrTensor type mismatch.onnxruntime::Tensor::DataRawtype == dtype_onnxruntime::Tensor::MutableDataRawX != nullptronnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<1>::Compute, indices shape: input shape: ScatterNDupdates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. input tensor and indices tensor must has rank larger than 0. , data shape: invalid indice found, indice = onnxruntime::ScatterND::Computeonnxruntime::ScatterNDBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cc. Input rank=Indices must have the same rank as Input. Indices rank= vs Indices vs updates dimensions differs at position=Indices dim=Unknown tensor type of is greater than input dim= at pos=onnxruntime::Scatter::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccMissing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK()data type is different from updates typeIndices and updates must have the same rankScatterScatterElementsonnxruntime::Scatter::ComputeExpecting indices to be either int32_t or int64_t must be within the inclusive range [indices element out of data bounds, idx=bad allocationAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()blocksizeonnxruntime::SpaceDepthBase::SpaceDepthBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hinput_height % this->blocksize_ == 0input_width % this->blocksize_ == 0onnxruntime::SpaceToDepth::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccinput_depth % (blocksize_ * blocksize_) == 0CRDinput.Shape().NumDimensions() == 4onnxruntime::DepthToSpace::Computeinput count mismatchinput_shape.NumDimensions() == 4SpaceToDepthDepthToSpaceonnxruntime::DepthToSpace::DepthToSpaceDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedGatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElements]. Actual value is D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_impl::::operator ()GatherElements op: Value in indices must be within bounds [ , onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()Gather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBaseTileTile doesn't have an implementation yet for the type: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cc'repeat' input tensor must be 1 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyonnxruntime::Tile::Compute!input_tensor.IsDataType()Tile doesn't support string type yet'repeat' input tensor must have the same length as the 'input' tensorAttribute perm of Transpose has an invalid value. Value is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBaseperm: does not align with rank of input data: [ is repeated.v >= 0 && static_cast(v) <= std::numeric_limits::max()naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetuponnxruntime::Transpose::ComputeTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0Transpose not implemented for empty tensors.Mismatched data types between input and output Tensors. (local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseonnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)input_shape[i] == 1Dimension of input must be 1 instead of . shape=onnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeinfo.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBasenullptr != p.output_tensor'axes' has a duplicate axis'axes' has an out of range axisaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1onnxruntime::Unsqueeze::ComputeAn axes tensor must be a scalar or a 1-D tensor.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareComputeD:\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 > 0split Axis= NumOutputs= 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=onnxruntime::Split::ComputeSplit operator does not support yetCannot split using values in 'split' attribute. Axis=D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::ComputeImplsplit_tensor->Shape().NumDimensions() == 1An split tensor must be a vector tensor.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBase'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0Unexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepCannot slice scalarsonnxruntime::SliceBase::ComputeStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got Starts must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arrayoutput == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOcalled_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncePlease fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Outputoffset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:Invalid start/ending offset [Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!onnxruntime::BroadcastIterator::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid. by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largeststart_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocatorstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Broadcast Output range [AbsNegFloorOrXorUnsupported X type: Must have 1 or more inputsMeanBitShiftNotAndMaxMinLessGreaterCeilReciprocalExpLogAcosAtanSinhCoshSinCosTanAsindata_n.Shape() == shapeonnxruntime::Max_6::Computeonnxruntime::Mean_6::Computedata_1.Shape() == shapeinputCount >= 1onnxruntime::Min_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shape(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModModfmod attribute must be true for float, float16 and double typesshape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandfmodfmod must have value either 0 or 1ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.AsinhAcoshAtanhPReluonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelfmod_onnxruntime::Mod::Computeinput_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::Sum_6::Computedirectiononnxruntime::BitShift::BitShiftLEFTinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: Invalid usage. Input 1 is a shape with no data.onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()RIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Compute'is defined.No attribute with name:'onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::Selu::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::functors::HardSigmoid::Initunknown kernel typeonnxruntime::functors::ScaledTanh::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ParametricSoftplus::InitbetaCannot concatenate scalars got: Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rankonnxruntime::ConcatBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccnew_axisMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/concat.h has mismatched dimensions of Non concat axis dimensions must match: Axis Data type mismatch, requested shape:i < input_shape.NumDimensions()size != 0 && (input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:At most one dimension can be -1.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hThe dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1A dimension cannot be less than -1, got onnxruntime::ReshapeHelper::ReshapeHelperrequested_shape[i] >= -1gsl::narrow_cast(input_shape.Size()) == sizeAttribute shape is not set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1A shape tensor must be a vector tensor.onnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1seedonnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.hX and mask should have the same shapeonnxruntime::Tensor::MutableDataAsSpanonnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)ratio input should have a single value.onnxruntime::Tensor::DataAsSpanonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceX->Shape().NumDimensions() == 4biasbeta_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()size_ % 2 == 1info.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0fonnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputesizeLRND:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flattengsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisFlattenonnxruntime::Flatten::Computezero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size QuantizeLinearD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.cconnxruntime::PrepareForQDQscale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_zero_point must be null or a scalar or 1D tensor or size 1.DequantizeLinear with type int32 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })onnxruntime::DequantizeLinear::ComputeSizeSigninfo.GetAttr("transA", &temp).IsOK()onnxruntime::Gemm::GemmM_ >= 0 && K_ > 0 && N_ >= 0transAGEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcast N: K: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperinfo.GetAttr("transB", &temp).IsOK()onnxruntime::Gemm::GemmD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm.htransBonnxruntime::GemmBroadcastBiasD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm.ccc_shape is required if c_data is providedc_shape != nullptrleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMulComputeHelper::Computenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falseleft operand cannot broadcast on dim right operand cannot broadcast on dim D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.hmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<__int64>::operator ()stoull argument out of rangestoll argument out of rangeinvalid stoull argumentinvalid stoll argumentstod argument out of rangeinvalid stod argumentD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.onnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::Castsnprintf_result > 0onnxruntime::`anonymous-namespace'::CastToString%.8gsnprintf() failed with return value: -INFINFNaNFailed to write value with snprintf().snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1invalid expand shapeEinsum op: An implementation for the input type is not supported yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cconnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one inputEinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsuminfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeequation->D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.GlobalLpPoolonnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringonnxruntime::PoolAttributes::InferOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]Pad should be smaller than kernel.kernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()LpPoolkernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()ponnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.onnxruntime::MaxPoolV8::ComputeImplUnsupported pooling size : onnxruntime::Pool::ComputeT3Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar.OneHotonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::ComputeDepth is negative.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computesortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestonnxruntime::TopkOpset11ConstructorCommononnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()kTopKop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [value of k must not be negativek tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueinput count mismatch, expected 1 input - the tensor to be processedoutput count mismatch, expected 2 outputs to be present for TopK operatorUnsupported tensor type of Uniquedelta in Range operator can not be zero!delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:onnxruntime::Tensor::Reshapeshape_.Size() == new_shape.Size()Tensor size () != new size (SSequenceEmpty: Got nullptr for output sequenceSequenceEmptySequenceAt: Got nullptr for output tensorInvalid sequence index () specified for sequence of size (I != nullptrGot nullptr input for index tensoronnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxSequenceAtY != nullptrSequenceLength: Got nullptr for output tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceLength::ComputeGot nullptr for sequence input.SequenceLengthonnxruntime::SequenceConstruct::Computenum_inputs >= 1SequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceErase::ComputeSequenceEraseSequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (Got nullptr for input tensor.onnxruntime::SequenceInsert::ComputeS != nullptronnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertUnsupported 'dtype' value: onnxruntime::SequenceEmpty::Computeonnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputInvalid data type for split tensor split_size_sum () != split_dim_size (onnxruntime::SplitToSequence::ComputeSplitToSequence operator does not support SplitToSequenceViolation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequencesplit_scalar > 0Split should be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplConcatFromSequenceonnxruntime::ConcatFromSequence::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSETInvalid input shape: output_paddingoutput_shapegroup count is <= 0 num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapeonnxruntime::ConvAttributes::ConvAttributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. X: X num_dims does not match W num_dims. group: *out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeConvTransposeonnxruntime::ConvTranspose::DoConvTransposeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccNot enough elements in dilations. Expected: Not enough elements in pads. Expected: onnxruntime::ConvAttributes::InferOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShape kernel channels: C: Input channels C is not equal to kernel channels * group. M: Output channels M is not divisible by group.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: onnxruntime::Conv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.ccinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()MaxUnpoolonnxruntime::MaxUnpool::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shape inferred output shape:output_shape is smaller than minimum required. output_shape:The most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsonnxruntime::NonMaxSuppression::Computeoutput != nullptrcenter_point_boxcenter_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.hNonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.onnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedDetCompressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()onnxruntime::Compress::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccAttempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputonnxruntime::Scan<9>::ScanD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hInvalid scan input: has length of ' dimension but input 'Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputs outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariablesOutput OrtValue has not been created for loop state variable output output_mlvalueinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsonnxruntime::Scan<9>::SetupSubgraphExecutionInfofeeds_fetches_manager_ && info_onnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initialize dimensions or more but input had shape of Expected onnxruntime::ScanImpl::Execute. Output tensor rank was Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputScanonnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccDynamicQuantizeLinearRoundAxis tensor should be 0D or 1DAxis tensor must be provided to the CumSum opCumSumAxis tensor should be of type `int32_t` or `int64_t`onnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::Computestarts.size()=dims.size() == starts.size()dims.size()=exclusivereverseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.ccextents.size()=onnxruntime::WritableSliceIterator<__int64>::Initsteps.size()=dims.size() == extents_.size()onnxruntime::WritableSliceIterator::Initdims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initinfo.GetAttr("batch_axis", &batch_axis).IsOK()batch_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("time_axis", &time_axis).IsOK()time_axis. batch_size=ReverseSequenceonnxruntime::ReverseSequenceOp::Computesequence_lens shape must be {batch_size}. Got:Invalid batch_axis of . Must be 0 or 1Invalid time_axis of batch_axis < 2time_axis and batch_axis must have different values but both are time_axis < 2batch_axis != time_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccsampling_ratio_ >= 0Sampling ratio should be >=0, but it was spatial_scaleNull input X ptrRoiAlign specified. It should be either avg or maxavgonnxruntime::RoiAlignBase::RoiAlignBaseInvalid mode of value output_heightD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.hsampling_ratiooutput_widthNull batch_indices_ptrNull rois_ptrNumber of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1First dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly detect_positiveIsInfonnxruntime::IsInf::IsInfFailed to obtain detect_positivedetect_negativeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccFailed to obtain detect_negativeQLinearConv : input zero point must be a scalar or 1D tensor of size 1T4onnxruntime::QLinearConv::ComputeIsScalarOr1ElementVector(X_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\qlinearconv.ccQLinearConv : filter zero point must be constantIsScalarOr1ElementVector(Y_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : filter zero point shape invalidQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(W_Zero_Point)D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.onnxruntime::MatMulInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)Input B should not be null.D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1QLinearMatMulonnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(b_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)IsScalarOr1ElementVector(b_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1*attribute case_change_action has invalid valuelocaleUPPERNONE!sw.empty()Duplicate stopwords not allowedstopwordsEmpty stopwords not allowedwstr != wconv_errorSingle dimension value must be greater than 0p.secondStopword contains invalid utf8 charsInput dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: StringNormalizerConversion ErrorFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleConversion Error:Please, install necessary language-pack-XX and configure localesis_case_sensitiveattribute is_case_sensitive is not setD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USattribute case_change_action is not setLOWERonnxruntime::StringNormalizer::StringNormalizercase_change_actionbad conversion Input dim value: Dimension: Output dim value: output_dims[i] == 0onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeCannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. Either scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Only one of scales or sizes must be provided as input.sizes == nullptronnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeResize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.onnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.Invalid roi input index.roi_input_idx_ > 0onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccResize: unexpected modeUpsample: unexpected modeUpsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Resize: input/output value is nullptronnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input shape needs to be at least a single dimension.Invalid input X: The rank of input X must be atleast 2. Got rank: dimension != Invalid input scale: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input mean: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNormInvalid input var: onnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormTfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countssize_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: <= min_gram_length must be inbounds of ngram_counts: Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesweights must be of equal sizeNegative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFmin_gram_length is requiredRequired min_gram_length must be positive: impl_->weighting_criteria_ != kNonemin_gram_length >= min_gram_length >= max_gram_length required: impl_->min_gram_length_ > 0max_gram_lengthmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countimpl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings but ngram_indexes size: Got weights of size: pool_int64snon-empty pool_int64s is required if pool_strings not providedpool_strings must not be empty if specified!pool_strings.empty()n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsstatus.IsOK() && !pool_int64s.empty()-gramsInput shape must have either [C] or [B,C] dimensions with B > 0.Number of items must compose whole (items % ngram_size == 0) id: onnxruntime::ngram_details::PopulateGramsDuplicate ngram detected, size: p.first->second->id_ == 0lambdD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()ShrinkD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\eye_like.cconnxruntime::EyeLike::ComputeT1 != nullptrEyeLikeEyeLike : Input tensor dimension is not 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.Unsupported output datatype with size: onnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: utils::HasDataType(t_proto)!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 >::SetValueFromTensorProtoMust have a single dimension of 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto.dims_size() == 1Must have a single dimensiont_proto.dims()[0] == 1Unsupported value attribute datatype: onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Computeonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsonnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was but has batch size of onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was sequence_lens length of did not match batch size of position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::ScanScan<8> spec does not support transpose of output. This should never be called.D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *bidirectionalforwardRNN op: Invalid activation attribute - activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizeallowed_activations.find(activations_[direction]) != allowed_activations.end()RNNclipAffineD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::Computeallowed_directions.find(direction_) != allowed_directions.end()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()info.GetAttrs("activations", activations_).IsOK()activationsactivation_betaactivation_alphaonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseinfo.GetAttr("direction", &direction).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hsigmoidinput_forgetclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0LSTM operator does not support double yetLSTMactivation_func_names.size() == static_cast(num_directions_) * 3tanhD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of linear_before_resetD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpGRU operator does not support double yetGRUactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of A + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::SafeRawConstPointercur + size <= endC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())onnxruntime::DeepCpuGruOp::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeMaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::Computepooled_height_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0info.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2LpNormalizationop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormInstanceNormalizationD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: vs. Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count onnxruntime::RandomNormal::RandomNormalD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hscaleinfo.GetAttr("scale", &scale_).IsOK()meaninfo.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLikehighinfo.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniforminfo.GetAttr("dtype", &dtype).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()onnxruntime::RandomNormalLike::Computebatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDlowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikesample_sizeonnxruntime::MultinomialComputeFLOAT16 is not supportedonnxruntime::RandomNormalComputeonnxruntime::RandomUniformComputeonnxruntime::contrib::RegisterNchwcKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_value_field_name, values).IsOK()values is and the number of default_floatkeys_int64svalues_int64sinfo.GetAttrs(_key_field_name, keys).IsOK()The num_keys == num_valuesonnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2However, the number of key is ) must have the same length. (name: attribtues in LabelEncoder default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()classes_stringsinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hvalues_strings_Unusedkeys_stringsvalues_floatsLabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsonnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::ZipMapOp::ZipMapOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsclasslabels_int64sclasslabels_stringsMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()] != number of classlabels[Input features_per_batch[ZipMapMINSOFTMAXSOFTMAX_ZEROAVERAGESUMBRANCH_GTEBRANCH_GTBRANCH_EQLOGISTICBRANCH_LEQLEAFBRANCH_LTtarget_weightstarget_treeidsTreeEnsembleRegressorn_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() is already there.nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_truenodeidsnodes_treeidsnodes_nodeidsnodes_modestarget_nodeidstarget_idspost_transformnodes_valuesn_targetsbase_valuesaggregate_functionInput shape needs to be at least a single dimension.nodes_missing_value_tracks_truenodes_hitratesnodes_featureidsnodes_falsenodeidsonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionUnable to find node One falsenode is pointing either to itself, either to another tree. (falsenode). (weights). in tree Node (truenode).-predictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMin::MergePredictionUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::computeTreeEnsembleClassifierX dims is empty.class_treeidsclass_weightsclass_idsclass_nodeidsonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommonClassifier::computepredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binarykernel_typeinfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramsD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonSVMRegressorLINEARRBFPOLYcoefficientsD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cc!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()rhosupport_vectorsonnxruntime::ml::SVMRegressor::SVMRegressorinfo.GetAttrs("rho", rho_).IsOK()num_features == feature_count_one_classUnexpected mode:onnxruntime::ml::SVMRegressor::Computen_supportsonnxruntime::ml::SVMClassifier::SVMClassifierprob_bproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccSVMClassifierprob_avectors_per_classD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hUnexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Unsupported data type of onnxruntime::ml::SVMClassifier::Computeinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intscoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0ScalerD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOpScale size: () != (offset!scale_.empty()Empty scale in attributesonnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOpInvalid argument: input has empty dimensions.scale_.size() == offset_.size()Either both scale and offset can be of feature size () or 1zeroscats_stringscats_int64sOneHotEncoderUnknown Category and zeros = 0.onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOponnxruntime::ml::MakeNormalizeInvalid normalize value of info.GetAttr("norm", &norm).IsOK()normMAXL2L1D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::NormalizerNormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorInput shape had more than 2 dimension. Dims=interceptsLinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()targetsScores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:multi_classonnxruntime::ml::LinearClassifier::LinearClassifierLinearClassifiershape_size == out.length()onnxruntime::ml::CastInputToFloatonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of Imputerreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedimputed_value_floatsimputed_value_int64sEmpty value of imputed values.Empty input dimensions.onnxruntime::ml::ImputerOp::ComputeInvalid typeMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()inputdimensions) does not match number of inputdimensions values (Number of inputs (FeatureVectorizer).onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::Computeonnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.hint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()DictVectorizerstring_vocabularyonnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOpInput of string must have output of int64Input of int64 must have output of string num_entries == int_categories.size()CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()info.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapper%finvalid stof argumentstof argument out of rangecast_toinfo.GetAttr("cast_to", &attr).IsOK()Invalid PACK_MAP value of onnxruntime::ml::MakePackSPARSE Expected DENSE or SPARSEonnxruntime::ml::MakeCastDENSEmax_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_forminfo.GetAttr("map_form", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.h Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of TO_STRINGTO_INT64TO_FLOATD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.cccur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplNegative index values are not permitted. First entry in map has index value of Unexpected CAST_TO value of Invalid input type of value: 0.fCastMap Expected std::map or std::map is NaNInput data with index: thresholdBinarizerInvalid Y argument: num_indices = 0) >=Invalid argument: X input has empty dimensions.] (Invalid Y argument: index is out of range: Y[ArrayFeatureExtractorSoftmaxCPU inputs N, D and N * D must be < Left shape: Left shape override: Right shape: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cconnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessleft.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. onnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputcandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputEinsum op: Input shapes do not alignleft_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_rank == right_rankRanks of pair-wise operands must be equal. onnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: candidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: . The shape is: Another operand has a dim value of in the same dimensionThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Found '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsEinsum subscripts string contains too many subscript labels when compared to the rank of the inputFound a '.' not part of an ellipsis in input: dim_iter == rankThe broadcasted dimensions of the inputs are incompatibleonnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsnum_broadcasted_indices < num_of_ellipsis_dims_Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Number of subscripts in the input equation does not match number of input tensorsD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.cconnxruntime::EinsumComputePreprocessor::RunRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains letters not seen in the inputsOutput subscript contains repeated lettersFound '.' not part of an ellipsis in the output subscript providedFound a '.' not part of an ellipsis in the output subscript providedInputs have ellipses in them but the provided output subscript does not contain an ellipsis...onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalrank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape Einsum op: Unsupported data type for Diagonal onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsinput_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Transpose failed: onnxruntime::EinsumOp::TransposeLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::IsTransposeRequiredinput_rank == permutation.size()The rank of the input must match permutation size for TransposeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyoutput.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapeBatch dimension should match for MatMul;input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Only 1 batch dimension is allowed for MatMulonnxruntime::EinsumOp::MatMulinput_1.DataType() == input_2.DataType()Data types of the inputs must match for MatMulEinsum op: Exception during MatMul operation: input_shape_1_override[2] == input_shape_2_override[1]Incompatible matrix dimensions for matMulinput_shape_1_override[0] == input_shape_2_override[0]num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given Invalid values in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesNumber of entries in '' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::Infoonnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputiteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::CreateFeedsFetchesManagerSubgraph must have the shape set for all outputs but did not.valid!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first output is not compatible with onnxruntime::scan::detail::LoopStateVariable::NextInput X must have 3 dimensions only. Actual:affineInput initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {Input B must have shape {Input R must have shape {Input W must have shape {}. Actual:onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid activation function of softplussoftsigneluhardsigmoidscaledtanhthresholdedreluleakyrelurelualpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fweights.quant_para_A + (M * K) <= A_endD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got Input P must have shape {Input initial_c must have shape {D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.cconnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\trilu.cconnxruntime::contrib::Trilu::ComputeIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.TriluD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\trilu.honnxruntime::contrib::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()upperInverseskip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccepsilon_ >= 0input is expected to have 3 dimensions, got onnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNormD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::Affine::Affineinfo.GetAttr("beta", &beta_).IsOK()Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale::ScaleD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler::ImageScalerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()DynamicSliceonnxruntime::contrib::Crop::ComputeCropD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) needs to be greater than or equal to the topBorder () + bottomBorder () + rightBorder (Input's height (Input's width () needs to be greater than or equal to the leftBorder () + scale_[1] () + scale_[0] (border input dimensions insteadAttribute border needs to be specified with four border elements, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::Computeuse_approximationsqeuclideanD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDisteuclideanmetricCDistonnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: Input shape dimensions mismatch:D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hbilinearCropAndResizeonnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskonnxruntime::contrib::MaxpoolWithMask::Computeinput_num_bytes % 4 == 0Invalid assumption of output element sizesizeof(uint32_t) == output_element_bytespositiveonnxruntime::contrib::MurmurHash3::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccMurmurHash3keys conv filter size: embedding_size attribute: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute .Conv filter size does not match embedding_size attribute. conv kernal size 1: char_embedding_sizeembedding_sizeconv_window_sizeChar embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Char embedding size: char_embedding_size attribute: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbeddingChar embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeInput string contains invalid utf8 chars: Match contains invalid utf8 chars: Can not digest separators: Can not digest tokenexp: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedExpecting a non-empty tokenexp!tokenexp.empty()tokenexpEither one of the separators OR tokenexp attributes required but none is setmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2separators must not be empty!separators.empty()attribute pad_value is not setmincharnumD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valuemincharnum_ > 0separatorsattribute mincharnum is not setattribute mincharnum must have a positive valueattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerTokenizermarkAttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpAttention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention v weight shape error! Expected:{, aw_attn_size}. Got:Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got: found!], while Attention query layer weight shape error! Expected:{}. Got: Attention memory layer weight shape error! Expected:{}, Got: onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplonnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccaxis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeAn axis tensor must be a scalar tensor.Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is ExpandDimsinput index out of rangeonnxruntime::contrib::EmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNormInput 'past' is expected to have 5 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'bias' is expected to have 1 dimension, got Input 'weights' dimension 1 should be 3 times of dimension 0Inputs 'past' dimension 2 shall have length of Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 0 shall have length of 2D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'weights' is expected to have 2 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'input' is expected to have 3 dimensions, got onnxruntime::contrib::Attention::Computeonnxruntime::contrib::AttentionCPUBase::ApplyAttentionInput 'mask_index' is expected to have 1, 2 or 3 dimensions, got Inputs 'mask_index' of 3d shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeonnxruntime::contrib::AttentionBase::GetPresentExpect to have present state output when past state input is givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.hSampleOponnxruntime::contrib::NhwcMaxPool::Computeinput_shape.Size() > 0 || N == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccDynamicQuantizeLSTMR_scaleW_scaleR_zero_pointW_zero_pointRecurrentWeight point must be constantDynamicQuantizeLSTM : Weight zero point must be zero must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)IsScalarOr1ElementVector(b_scale_tensor)DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonMatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::DynamicQuantizeMatMul::Computeinput zero point must be a scalar or 1D tensor of size 1.weight must be a scalar or 1D tensor of size 1input scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccweight zero point must be a scalar or 1D tensor of size 1.onnxruntime::contrib::QAttention::ComputeQAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : 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_x_scale)onnxruntime::contrib::QlinearBuildLookupTableD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.ccQLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)Input x_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_global_average_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if givenA != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMatMulInteger16X_rank == 4onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0info.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseonnxruntime::contrib::ReorderOutput::ReorderOutputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinvalid channel countchannels_ > 0info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::NchwcUpsample::Compute(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchonnxruntime::contrib::NchwcPoolBase::NchwcPool.. rows[Input 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got row[ [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps onnxruntime::contrib::BahdanauAttention::PrepareMemoryonnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_ is not in (0, D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccnot support normalize yet.unimplemented activation: activation_params count mismatchgamma is expected to have size of beta is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got beta is expected to have size of position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got Input 0 and 1 shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1D:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPositiondims[d_i] < d_maxTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType. is not supportedDevice:[DeviceType: OrtMemType: OrtAllocatorType: MemoryType: DeviceId:CudaPinnedname: id:CudaOrtMemoryInfo:[Specified device is not supported.D:\a\_work\1\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Argument is not a tensorOrtApis::GetTensorTypeAndShapeD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtValue is not a Tensortype != nullptrNot implementedtype_proto is not of type sequence!onnxruntime_providers_shared.dllSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllGetProvideronnxruntime_providers_dnnl.dllProvider_SetHostonnxruntime::ProviderLibrary::Getonnxruntime::ProviderSharedLibrary::EnsureD:\a\_work\1\s\onnxruntime\core\framework\provider_bridge_ort.cctype_proto is not of type map!Tensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Inittensor failed memory size calculationtensor size overflowD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0p_type != nullptronnxruntime::Tensor::Tensoronnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeint16uint16int8uint8booldouble(null)floatonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())utils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())We do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredonnxruntime::data_types_internal::IsCompatibleonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.hexpected a registered ONNX typevalue_proto != nullptronnxruntime::utils::ContainerChecker::ContainerCheckerMLDataType for: Invalid DataTypeImpl TypeProto definition is not currently registered or supported(unknown type)tensor type float16bfloat16int64uint64int32uint32onnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Set dimensions. for SizeFromDimension. Tensor has {}dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Sliceonnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.onnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dimsBuffer containing the initializer must be owned by the user.An OrtValue for this name has already been added.Received nullptr for OrtValue.Received OrtValue is not a tensor. Only tensors are supported.D:\a\_work\1\s\onnxruntime\core\framework\session_options.ccReceived nullptr for name.Session Config with key [onnxruntime::SessionOptions::AddConfigEntry]. It will be overwritten] already exists with value [Config key is empty or longer than maximum length 128Config value is longer than maximum length 1024InternalTestingExecutionProviderCoreMLExecutionProvideronnxruntime::utils::ExecuteGraphOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedD:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueallocator != nullptronnxruntime::utils::BatchOrCopyMLValueinvalid allocatorFailed to find allocator for device Attribute: expected to be of type: but is of type: No attribute with name: is defined.No attribute with this name is defined.Attibute name and type don't matchRequested attribute: is expected to have type: D:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocatorD:\a\_work\1\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Invalid arg_num of . Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was nullCould not find OrtValue with idx 'entry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.onnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingD:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::SetupAllocatorsAllocator already registered for . Ignoring allocator from session.disable_prepacking. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.onnxruntime::SessionState::LoadFromOrtFormatKernel create info is null. Invalid ORT format model.onnxruntime::SessionState::LoadFromOrtFormat::::operator ()onnxruntime::SessionState::CreateSubgraphSessionStatesubgraphMain Graph instance should have populated all subgraphs when being resolved.onnxruntime::SessionState::SaveToOrtFormatonnxruntime::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::SessionState::GetNodeIndexInfonode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node onnxruntime::SessionState::PrepackConstantInitializedTensorsduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::GetNodeKernelCreateInfop_op_kernelentry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStateSubgraph SessionState for is null. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Unable to find compiled kernel hash for node ''.Can't find node with index Failed to find kernel for The node is not placed on any Execution Provider. (node ). found duplicated provider in KernelRegistryManagerSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroonnxruntime::utils::ConstantNodeProtoToTensorProtoInitialized tensor with unexpected type: initialize preallocated buffer failedThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsstring tensor can not have raw dataonnxruntime::utils::TensorProtoToMLValueonnxruntime::utils::GetFileContentdata overflowUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size() does not match the data size() in protoonnxruntime::utils::UnpackTensorWithExternalDataImplnullptr == p_data`anonymous-namespace'::ReadExternalDataForTensorTensorProto external data size mismatch. Computed size: External data type cannot be UNDEFINED or STRING.D:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc`anonymous-namespace'::GetExternalDataInfoTensor does not have external data to read from.UnpackTensor: the pre-allocated size does not match the raw data size, expected Indicies expected to be INT64Invalid TensorProtoUnsupported type: onnxruntime::utils::UnpackInitializerDataonnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)Must have a valid data typeBUG! Report to onnxruntime team.onnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Unsupported attribute value type of in 'Constant' node 'Invalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend()rank == dims.size() && rank > 0Invalid SparseTensor indices. Should either have raw or int64 dataSparse Indicies raw data size does not match expected.onnxruntime::utils::CopySparseDatanullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccduplicated allocatoronnxruntime::IExecutionProvider::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccduplicated allocator: onnxruntime::IExecutionProvider::TryInsertAllocatorIExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_onnxruntime::IExecutionProvider::GenerateMetaDefIdonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: Error mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::PartitionOrtFormatModelImpl has Compile error: EP: single_node_compute_func should have 1 elements. Execution Provider must generate unique names across the entire model.Existing entry in compiled kernel hashes for onnxruntime::GraphPartitioner::PartitionOrtFormatModelNo provider specified.onnxruntime::GraphPartitioner::PartitionCompiled kernel hashes must be providedcompiled_kernel_hashes != nullptronnxruntime::FunctionKernel::FunctionKernelD:\a\_work\1\s\onnxruntime\core/framework/func_kernel.hcompute_info_->create_state_func(&context, &func_state_) == 01 == capability.nodes.size()onnxruntime::PlaceNodeD:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PartitionOnnxFormatModelImplMust use Function based fusion when exporting compiled nodes to dll.fusion_style == IExecutionProvider::FusionStyle::Function did not return correct number of compiled functionsonnxruntime::InlineNodesonnxruntime::GraphPartitioner::PartitionOnnxFormatModelReceived invalid value of arena_extend_strategy onnxruntime::CreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cconnxruntime::AllocatorManager::InsertAllocatordata_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.ccA dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while loading custom ops with message: has already been loaded.offset >= 0 && static_cast(offset) < node_values_size_node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cc Requested shape:OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains failed. Error:Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame should only appear once.Memory type static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behavior but the actually size is: , block in memory pattern size is: onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueD:\a\_work\1\s\onnxruntime\core/framework/execution_frame.hFor ort_value with index: . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffermlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.model format error! Need a key for the external data infomodel format error! Need a value for the external data infolocation failedparsing lengthchecksummodel format error!model format error! Missing 'location'len <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProto kernel_end_version: kernel start version: node_version: Version mismatch. and type (Op with name ( This op has been implemented only for the following types ( However the types are incompatible. (node_version: in the supported version rangeFound kernel for Op with name (), but the node in the model has the following type (onnxruntime::KernelRegistry::TryCreateKernelD:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cc No matching hash for kernel not found in Encountered following errors: ( kernel is not supported in Kernel not foundkernel def can't be NULL: Conflicting with a registered kernel with op versions.Failed to add kernel for onnxruntime::IDataTransfer::CopyTensorssrc.SizeInBytes() == dst.SizeInBytes()D:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensorD:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()Can't slice a non-tensor OrtValue. Type was onnxruntime::OrtValueTensorSlicer::Createort_value.IsTensor()OrtValue has not been allocated so can't be sliced.D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cc. Shape:ort_value.IsAllocated()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on Invalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::CreateD:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalonnxruntime::ParallelExecutor::ExecuteMultiple errors were found.D:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::Executeonnxruntime::ParallelExecutor::RunNodeAsyncBegin executionGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true._fence_beforeop_name' Status Message: Non-zero status code returned while running node. Name:'_kernel_timeproviderUnknown exception was caught by catch-all handler._fence_afterException running nodes starting at '. does not.entry != nullptrAll implicit inputs should have OrtValue instances by now. onnxruntime::SequentialExecutor::ExecuteD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.ccexec_plan_indexgraph_indexparameter_sizeactivation_sizeoutput_sizeSequentialExecutor::Execute[Memory] ExecutionFrame statically allocates bytes for [Memory] ExecutionFrame dynamically allocates onnxruntime::ReleaseNodeMLValues already exist.Can't use func with null ptrfunc info for node: onnxruntime::FuncManager::GetFuncs not found.D:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.ccGraphonnxruntime::session_state_utils::DeserializeTensorProtostring tensor is not supported for copying between allocators, Got D:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.ccFailed to copy tensor to Internal error. The preallocated buffer is too small. Requires ) because the ORT planned memory location device ) is different from what is supplied (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.ort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.[Memory] SessionStateInitializer statically allocates entry != initialized_tensors_to_allocate.end() failed.Using user supplied initializer with name (Deserialize tensor Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator () is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingSubgraph input with name Failed to get allocator for location: onnxruntime::TensorAllocatorWithMemPattern::FinalizePlanduplicated locationInternal error.D:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hMem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedonnxruntime::TensorAllocatorWithMemPattern::Trace : ) D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Indexonnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseShould not have entry in kernel create info with nullptr for kernel_defCan not find the node onnxruntime::PlannerImpl::ComputeUseCountsp_kernel_defstarts_.size() == ends_.size()Previous entry was not terminated.D:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.honnxruntime::AllocPlanPerValue::ProgramCounter::AddStartstarts_.empty() || start > ends_.back()Invalid 'start'. Value is smaller than previous 'end'.starts_.size() == ends_.size() + 1No matching 'start' entry.Invalid 'end'. Value is larger than 'start'.onnxruntime::AllocPlanPerValue::ProgramCounter::AddEndend >= starts_.back()allocatorCan not find the execution provider onnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::GetLocationForNodeInputInvalid program_counter entries at index onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::VerifyMemoryTimeScheduleentry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::GenerateDeallocationPlanAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::CreatePlansource and destination buffer size mismatchonnxruntime::utils::detail::CopyLittleEndianD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccTotal allocated bytes: Allocated memory at Extended allocation by onnxruntime::BFCArena::Extendreserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reservetried to allocate 0 bytesh != kInvalidChunkHandle. bin_num:Extending BFCArena for onnxruntime::BFCArena::AllocateRawInternal rounded_bytes:!chunk->in_use()Failed to find a free memory block despite calling Extend. rounded_bytes=onnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)c2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::Mergeonnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in bin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.h0 == memory_size % kMinAllocationSizep_int < base_int + memory_size_Could not find Region for p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexFor memory limit: max_dead_bytes_per_chunk: onnxruntime::BFCArena::RegionManager::RegionFor arena_extend_strategy onnxruntime::BFCArena::BFCArenaD:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc with following configs: initial_chunk_size_bytes: Creating BFCArena for BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) bins of max chunk size Creating h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)Incorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator ()cudaMallochipMallocFailed to allocate memory for requested buffer of size bytes. is smaller than requested bytes of Available memory of onnxruntime::BFCArena::FreeAndMaybeCoalesceBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum). onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.h!using_counters_current <= buffer_size_D:\a\_work\1\s\onnxruntime\core\framework\simple_tensor_allocator.cc', location: invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferFailed to get allocator for initializer 'ConstantNoOpCan't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source= Dimension= Target=SparseTensor element type mismatch. ] op_type [Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapedoes not have the graph for key onnxruntime::Node::SaveToOrtFormatNode [Serialization of fused function body is not currently supported, node_arg_name cannot be null], could not find NodeArg fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()fbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceLoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatonnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:Node::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for input edgesoutput edgesinput index: onnxruntime::Node::LoadEdgesFromOrtFormatgraph_proto != nullptronnxruntime::Graph::GraphThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullSparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)' Model is invalid.Duplicate constant node sparse initializer name: 'onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: Output:' source:Error merging shape info for output. '. Falling back to lenient merge. target: Input=Type mismatch. Current=onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccThis is an invalid model. Failed to find NodeArg in all parent graphs. Name=Some nodes are not included in the topological sort, graph have a cycle. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=Graph attribute inferencing failed: in node onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic. inputs and requires inputs but subgraph has No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs.Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesSize mismatch validating subgraph inputs. Got Node:onnxruntime::Graph::UpdateShapeInference) Op (UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()) input arg (This is an invalid model. Node (Node () does not have type information set by parent node.) of operator (' of input parameter () is invalid.) in node () bound to different types (Type Error: Type parameter (This is an invalid model. Type Error: Type ' in node (' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'Duplicate sparse_tensor_initializer: '. Please, fix your model.by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.Initializer Graph state to be loaded into must be empty.This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Graph ctor should have created NodeArg for initializer. Missing:node_arggraph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoThis is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name () does not exist in the graph.This is an invalid model. Graph output (onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Argument mismatch when removing edge.onnxruntime::Graph::BuildConnectionsInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.graph_proto_ is not in sync with name_to_initial_tensor_.!foundsparse_tensor_names_.count(tensor_name) == 0onnxruntime::Graph::RemoveInitializedTensoronnxruntime::Graph::SaveToOrtFormat as it still has output edges._token_onnxruntime::Graph::RemoveNodeCan't remove node node->GetOutputEdgesCount() == 0'was added but does not exist. Outer scope node arg name 'Failed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProtoRemoving initializer 'onnxruntime::Graph::CleanUnusedInitializersonnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNode'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.) output arg () does not match expected type (onnxruntime::Graph::InferAndVerifyTypeMatch) type inference failedType Error: Type () does not have type information.) of node () of output arg (' has element type Type Error: Data in initializer 'This is an invalid model. Model input ( but usage of initializer in graph expects This is an invalid model. Error in Node:onnxruntime::Graph::VerifyNodeAndOpMatch does not match. Type Error: Shape of initializer ) is required but not specified.) attribute ( is not a registered function/opFatal error: onnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsError: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsouter_scope_node_args_consumed.empty() but different TensorProto.onnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::Graph::AddInitializedTensorsparse_tensor_names_ not in sync with name_to_initial_tensor_AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()nullptr != func_meta_defonnxruntime::Graph::CreateFusedSubGraphNodenode.MutableOutputDefs().size() == subgraph.GetOutputs().size()onnxruntime::Graph::InlineFunction's number of inputs is different from function body graph's number of input.'s number of outputs is different from function body graph's number of outputs.onnxruntime::Graph::SetInputsInput to set must exist.input->Exists()onnxruntime::Graph::LoadFromOrtFormat::::operator ()Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.index < data_.size()onnxruntime::ConstPointerContainer > >::atNode index is out of rangeNodeEdge is missing. Invalid ORT format model.D:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.hPTD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSet, but it its version is higherknown by the checker.than the operator set 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 Unknown model file format version. model may run depending upon legacy support of some older opset version operators.No graph was found in the protobuf.onnxruntime::Model::LoadFailed to load model with error: less than 0.Protobuf parsing failed.Protobuf serialization failed.onnxruntime::Model::SaveToOrtFormat is less than 0.onnxruntime::Model::Saveonnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model. is till opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::model_load_utils::ValidateOpsetForDomainD:\a\_work\1\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.onnxruntime::Model::Modelonnxruntime::SaveModelsystem error number failed. File doesn't existLoad model C should specify a shapeAttribute Incompatible dimensionsNegative values are not allowed in a shape specificationweight_scaleinput_scaleweight_zero_pointinput_zero_pointOne sided attention windows length W, or half of total window lengthLongformerAttentionConstrain input and output types to int8 tensors.global_weightmaskwindowglobalGglobal_biasinput_idsThe epsilon value to use to avoid division by zero.Constrain to integer typesword_embeddingsegment_idssegment_embeddingposition_embeddingPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Inputs 0 shall be 3 dimensionsIncompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).Inputs 0 shall be 2 dimensionsInputs 4 shall be 5 dimensionsNumber of attention headsD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccword_embedding should have 2 dimensions and dimension size is known.weightWhether every token can only attend to previous tokens. Default value is 0.MpresentpastConstrain mask index to integer typesConstrain input and output types to float tensors.) needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)) + scale[1] () + scale[0] () needs to be greater than or equal to the left_border () + right_border ( Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. Let's assume "separators" is [" "] and consider an example. If input is ["Hello World", "I love computer science !"] whose shape is [2], then the output would be [["Hello", "World", padvalue, padvalue, padvalue], ["I", "love", "computer", "science", "!"]] whose shape is [2, 5] because you can find at most 5 tokens per input string. Note that the input at most can have two axes, so 3-D and higher dimension are not supported. If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings will be broken part into individual characters. For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). This operator searches for the first match starting from the beginning of the considered string, and then launches another search starting from the first remained character after the first matched token. If no match found, this operator will remove the first character from the remained string and do another search. This procedure will be repeated until reaching the end of the considered string. Let's consider another example to illustrate the effect of setting "mark" to true. If input is ["Hello", "World"], then the corresponding output would be [0x02, "Hello", "World", 0x03]. This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. If tokenizer removes the entire content of [C]-input, it will produce [[]]. I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. If the tokenizer receives empty input of [0] then the output is [0] if empty input of [N, 0] then [N, 0]. Input axis is invalid: Second input does not have rank 2First input does not have rank 2both data and indices tensor need to have rank larger than zero. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html Duplicate of FusedMatMul. Going forward FusedMatMul should be used. This OP will be supported for backward compatibility. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html inputs are expected to have tensor type and output type should not be null.rois input tensor has wrong dimensionfirst input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64last dimension of indices must not be larger and rank of data tensorGaussian Error Linear Unit. A high-performing neural network activation function.The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.stash_typecrop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionThe inner-most 2 dimensions must have the same size (mat_w: != mat_h:Input rank must be >= 2.Bias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. 1D input tensorValue of betaValue of alpha Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal. Constrain input and output integer tensors typesoutput tensorinput tensorConstrain input and output float tensors types.Constrain input and output types to float or half tensors.skipinv_std_varSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationUScale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. Constrain mean and inv_std_var to float tensors.input_as_shape Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided.Input's shape must be 4-D GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. extra_shapeThe new GRU hidden state calculated by this op.The timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.If 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.hiddenFor internal use. Sample echo operator.The scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valuenormalizedsignal_ndimRfftConstrain input0 and output types to float tensorsinput_0ComplexMulIrfftonesidedWComplexMulConjinput_1A The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha. The fused convolution operator schema is the same as Conv besides it includes an attribute activation.Constrain input and output types to float tensorsPadsOutput tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor of shape [N,C,H,W]The scale to apply.Bias applied to each channel, same size as C.1D output tensorResult, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Result, has same shape and type as inputTensor of data to extract slices from.Output tensorInput tensorThreshold value1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`dataGivenTensorFillConstrain indices to integer typesConstrain input and output types to all tensor types.Sliced data tensor.Input data to be scaledvaluesThe filled tensorThe shape of filled tensordrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitOutput data after scalinggatesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevThe previous GRU hidden state.Tensor of rank q >= 1.Tensor of rank r >= 1.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain indice type to int32 or int64Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].indicesInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]] Specify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingThe WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Constrain to tensor(int32).Specify embedding vector of char(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Input tensor.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayxA 1-D input tensor that is to be processed. Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Tensor after padding.A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'Scalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.ExpandDims echo operator.Specified axis to insert a dimensionactivation_gammaScalar multiplier for input tensor C.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorTokenized stringsStrings to tokenizeMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Matrix multiply results from A * BN-dimensional matrix BN-dimensional matrix A Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.Scalar multiplier for the product of the input tensors.Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorThe underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.Matrix multiply resultsWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationConstrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Output tensor of the same type and shape as the input tensor.Input tensor. Every matrix in the batch must be invertible.The bias input data that is a 1D tensor.Y = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.Constrain input and output types to all numeric tensors and bool tensors.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Input tensor of rank 2 or higher.The bias (or mask) as Tensor.broadcast bias across input for dimensions broadcast_axis to softmax_axis-1apply softmax to elements for dimensions softmax_axis or higherThe distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". Finds all the unique values (deduped list) present in the given input tensor. This operator returns 3 outputs. The first output tensor 'uniques' contains all of the unique elements of the input, sorted in the same order that they occur in the input. The second output tensor 'idx' is the same size as the input and it contains the index of each value of the input in 'uniques'. The third output tensor 'counts' contains the count of each element of 'uniques' in the input. Example: input_x = [2, 1, 1, 3, 4, 3] output_uniques = [2, 1, 3, 4] output_idx = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] Input can be of any tensor type.countsConstrains input to only numeric types.A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisConstrain types to int tensors.Constrain types to float tensors.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].crop_sizeInput data tensor from the previous layer.type used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.Constrain input and output types (except mean and inv_std_var) to float tensors.Output data tensor.Bias tensor.Scale tensor.The normal input data.The output.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.̼+̼+. Index: ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::AddInitializernum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node onnxruntime::graph_utils::FindPathFailed since multiple edges matched:onnxruntime::graph_utils::AddNodeInputD:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.Attempting to get index for an input which does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::GetNodeInputIndexFromInputNameitr != node.InputDefs().end()onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputName!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the GraphMismatch between Graph and IndexedSubGraph. Output not found:nodeargMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GetNodesInTopologicalOrderInvalid ExecutionOrdernodeMismatch between Graph and IndexedSubGraph. Node not found: onnxruntime::GraphViewer::GetRootNodesfilter_info_ == nullptrNot supported with filtered graph.TENSORUNDEFINEDD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveInitializerOrtFormatSaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.onnxruntime::experimental::utils::SaveAttributeOrtFormatonnxruntime::experimental::utils::SaveSparseInitializerOrtFormatMissing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::experimental::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::experimental::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dims for sparse initializer: Missing indicies for sparse initializer: Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.GRAPHSTRINGINTFLOATTENSORSSTRINGSINTSFLOATSSPARSE_TENSORSSPARSE_TENSORGRAPHSTxTtTlTTdTTTTTTTTonnxruntime::ViewerFunctionImpl::BodyNot supportedD:\a\_work\1\s\onnxruntime\core/graph/function_impl.houtininput_arg->Type() != nullptrD:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyA node with a function body within a subgraph within another function body is currently not supported in ORT for domain and function is trying to import opset version for the same domainonnxruntime::CreateSchemafused_function_subgraphonnxruntime::CreateOpsetImportsForFunctionresult.secondONNX model does not support multiple opset versions for a domain. Model imports opset version Resolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplScale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is Input data type does not match the expected data type{name} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} inputs are expected to have tensor type.A_scale{additionalDocumentation}A_zero_pointB_zero_pointB_scaleC_zero_pointC_scaleMulIntegerKeep the reduced dimension or not, default 1 mean keep reduced dimension.b_scaleConstrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.Constrain 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.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A data type to 8-bit integer tensor.a_zero_pointQLinearReduceMeanmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointadditiondata_scalereduced_zero_pointreduced_scaledata_zero_pointaxis must be in [-rank, rank-1]. input rank was Constrain input and output types to 8 bit signed and unsigned tensors.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.y_scaleConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.y_zero_pointx_scaleConstrain 'y', 'x_scale' to float tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.x_zero_pointReduceSumIntegerA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).Constrain input type to 8-bit integer tensor.reducedinitial_hsequence_lensPinitial_cY_cY_hConstrain weights types to 8 bit tensors.Constrain seq_lens to integer tensor.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.QLinearAveragePoolConstrain input and output types to 8 bit tensors.Whether to use ceil or floor (default) to compute the output shape.X_scaleCoefficient of leakage.Y_scaleX_zero_pointSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Y_zero_pointCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Number of neurons in the hidden layerCouple the input and forget gates if 1.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.RConstrain input and output types.Unsupported type:Can not get shape initializer data!limitstartdeltaUnsupported non-raw-data data type!Constrain input and output types to singed/unsigned int8 tensors.input and zero_point pair is expected to have be same type.Output tensor must have at least 2 dimensionsInput tensor must have at least 2 dimensionsw_scalewD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccweight and zero_point pair is expected to have same type.w_zero_pointtensor rank too smallD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueinvalid scales dimensionCouple the input and forget gates if 1, default 0.Constrain seq_lens to integral tensors.Number of neurons in the hidden layer.QWMWAWmemory_seq_lenskernel32.dllSetThreadDescriptionD:\a\_work\1\s\onnxruntime\core\platform\windows\env.ccFatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfoonnxruntimeGetFileSizeEx open file fail, errcode = file_path == nullptrReceived negative size from stat callInvalid fd was supplied: SetFilePointerEx length > buffer.size()offset < 0onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBuffer\/MapFileIntoMemory is not implemented on Windows. fail: unexpected endReadFile RemoveDirectory() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: , error code: Failed to load library, error code: UNC\onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathGetFinalPathNameByHandle() failed: ReturnHrException%hs!%p: %hs(%u)\%hs!%p: FailFastLogHrMsg:[%ws] %hs(%d) tid(%x) %08X %ws(caller: %p) [%hs] [%hs(%hs)] CallContext:[%hs] %wsRaiseFailFastExceptionkernelbase.dllonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameFailed to find symbol in library, error code: Failed to unload library, error code: onnxruntime::LoopDir\*D:\a\_work\1\s\onnxruntime\core/platform/path_lib.h[ONNXRuntimeError]SUCCESSFAILINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemErrorEP_FAILGENERAL ERROR [ is not a valid dateampm is not a valid day is not a valid yearonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::LoggingManageronnxruntime::logging::LoggingManager::CreateDefaultLoggerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. D:\a\_work\1\s\onnxruntime\core\common\threadpool.ccn >= 0onnxruntime::concurrency::ThreadPool::ParallelForNested parallelism not supported!current_parallel_sectiononnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectionlength overflowonnxruntime::ToMBStringD:\a\_work\1\s\onnxruntime\core\common\str_helper.cconnxruntime::ToWideStringSessionNodeenabled_onnxruntime::profiling::Profiler::StartTimeD:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ onnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event."dur" :"ts" :"ph" : "X","name" :"",{"cat" : ""pid" :"tid" :}, ] "args" : {"" : "} UUFailed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootD:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::Path::Parse/\unexpected failureillegal input path:api-ms-win-eventing-provider-l1-1-0.dlladvapi32.dllEventSetInformationGetSystemTimePreciseAsFileTimekernel32.dllrB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KAQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;bad dimensions?@@@packing unavailable    QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!ElementSize must be power of 2 and less or equal than 16!??sequence_typetensor_typemap_type] for nowonnxruntime::experimental::utils::SaveTypeInfoOrtFormatonnxruntime::experimental::utils::SaveValueInfoOrtFormatWe do not support type [D:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::experimental::utils::SaveSequenceTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::experimental::utils::SaveMapTypeOrtFormatonnxruntime::experimental::utils::LoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.Null sequence type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadTypeInfoOrtFormat is not supported currentlyNull map type info. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadMapTypeOrtFormatopset id is null. Invalid ORT format model.onnxruntime::experimental::utils::LoadOpsetImportOrtFormatopset import domain is null. Invalid ORT format model.Null type info for Type:Model must have opset imports. Invalid ORT format model.onnxruntime::experimental::utils::LoadValueInfoOrtFormatSUUU not in allowed output sizes.) has output size ) in op definition.has output size not in range [min=, max= not in allowed input sizes.) has input size )'s output ) has more outputs (Attribute '' appeared multiple times.) has more inputs () than declared ()'s input is marked single but has an empty string in the graph' is expected to have field 'g'' is expected to have field 'sparse_tensor'' is expected to have field 'ints'' is expected to have field 'floats'Unrecognized attribute: for operator ' is expected to have field 't'Mismatched attribute type in 'Required attribute '' is missing.Duplicate type constraint name' is expected to have field 'tensors'' is expected to have field 'strings' has unknown expected type' is expected to have field 'graphs'Operator '' has been deprecated since version !(it.GetName().empty()): failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == iONNX Schema The input must be an integer map to either string or float.map(int64, float)map(int64, string)Values greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type. The output will be of the same tensor type.The input must be a tensor of strings or integers, either [N,C] or [C].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.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.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 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.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The 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)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.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.map(string, float)map(int64, double)D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.ZThe output will be a tensor of strings or integers.The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]A collection of intercepts.A collection of weights of the model(s).Input's shape should be 1D or 2DThe input must be a tensor of a numeric type.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).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.One of 'MAX,' 'L1,' 'L2'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 total number of regression targets, 1 if not defined.Weights of the intercepts, if used.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, 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.The size of each input in the input listThe input type must be a tensor of a numeric type.Label encoder has only one input.Only one of keys_*'s can be set in label encoder.Label encoder has only one output.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.Value(s) to change to.A value that needs replacing.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.Input type is not int64 tensor but keys_int64s is setInput type is not string tensor but key_strings is setOnly one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setAn integer.A string.A float.A list of ints.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.A list of floats.node id that this weight is for.The id of the tree that this node is in.The weight for the class in class_id.The index of the class list that each weight is for.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'For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.Child node if expression is false.Child node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Child node if expression is falseBase values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The node id of each weightThe id of the tree that each node is in.The weight for each targetThe index of the target that each weight is forThe keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The output will be a sequence of string or integer maps to float.seq(map(int64, float))seq(map(string, float))The input must be a tensor of a numeric type, either [C] or [N,C].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 the classlabels_* attributes is used. Its size will match the bactch size of the input.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.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.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The number of support vectors.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Tree id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.Constrain input and output types to high-precision numeric tensors.CeluConstrain input and output types to float32 tensors.Constrain input Y types to float/int tensors.slope Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. Constrain input X and output types to float/int tensors.List of tensors for Value of beta.data_0Value of alpha.X_ReduceMaxsumConstrain input and output types to numeric tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment'axis' must be in []. Its actual value is: Constrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.X_alphaThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Elu_ResultCoefficient 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).Coefficient of ELU.'shape' input must be 1D tensor of type INT64Constrain input and output types to all tensors.input and zero_point pair is expected to have same type.X_LogX_ExpX_SubX_ReduceSumKK input must be a one-dimensional tensor of size 1.Invalid value for attribute axisAxis has less than the requested k elements.K input must be of type int64.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).Whether to return the elements in sorted order.ValuesConstrain index tensor to int64Indiceslossreductionweight_gatherloss_Nddloss_NCddinput_gather_elementnoneloss_N1ddexpanded_target_int64const_zero_target_typedconst_zero_floattransform_targetsloss_sumloss_unweightedconst_ignore_indexweight_gather_sumTarget rank must be 1 less than the input rank.weight_gather_temp_1Weight rank must be 1.Input and target dimension value mismatch.squeeze_maskinput_gather_element_transformweight_gather_tempconst_one_floatSpecifies 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.Constrain target to integer typesConstrain input, weight, and output types to floating-point tensors.baConstrain output y and its zero point data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.axis tensor can be int32 or int64 onlyIf set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.targetconst_oneignore_indexexpanded_targetConstrain input and output types to floating-point tensors.const_zeroRank of input Einsum expression string.Number of input tensors does not match the operands in the equation.NegativeLogLikelihoodLoss does not match the equation indices.Ellipsis represents incompatible dimensions.X_MaxscoresX_DivX_RSConstrain input and output types to all numerical tensor types.InputsType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.SoftmaxCrossEntropyLosslabelslog_probsource sequence type missing element type.target sequence type missing element type.Mismatched type:Mismatched tensor element type: target= source=seq(tensor(uint32))seq(tensor(uint64))seq(tensor(uint8))seq(tensor(uint16))seq(tensor(double))seq(tensor(string))seq(tensor(float16))seq(tensor(float))seq(tensor(int32))seq(tensor(int64))seq(tensor(int8))seq(tensor(int16))seq(tensor(complex128))seq(tensor(bool))seq(tensor(complex64))Input was expected to have either tensor or sequence type. Got Input element type of Output was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input type was nullInput was expected to have tensor type. Got tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.v_initialAn optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.final_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.initial_state_and_scan_inputsAll Tensor typesAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Int64 tensor was not a tensor.Scan input ) is not equal to number of scan outputs (Number of scan output axes specified () is not equal to number of scan inputs (Number of scan input axes specified ( is invalid for a tensor of rank axis value If node has was then_branch and else_branch produce different number of outputs. but subgraphs produce was notScan 'body' subgraph outputs should all be tensors but output outputs. Expected Graph attribute inferencing returned type information for outputsGraph 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.condLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output Only boolD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor and Sequence typesConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinConstrain 'x' to float tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Initial_ZeroPoint_FPClipped_ZeroPoint_FPX_RangeMin_ScaledX_Min_AdjustedX_Max_AdjustedQ_MaxX_MinRounded_ZeroPoint_FPZeropointConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float or int32 tensor.Constrain input types. Casting from complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesInvalid position of 0Invalid dimension value: Target shape may not have multiple -1 dimensionsConstrain output types. Casting to complex is not supported.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output to int64 tensor.Input tensor can be of arbitrary type.reshapedAll inputs to Concat must have same rankWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Required attribute axis is missingaxis must be in [-rank, rank-1].Constrain output to int64 tensor, which should be a scalar though. should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedaxis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).data tensor must have rank >= 1Input and output types can be of any tensor type.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).List 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).expandedvalues in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatessqueezedInvalid value of attribute 'axis'. Rank=) and outputs ( Value=concat_resultConstrain output types to any tensor type.inputsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Mismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (Input axes has invalid data'step' cannot be 0Input axes has incorrect lengthInput steps has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {stepsIndices tensor must have rank >= 1sizesConstrain roi type to float or double.OneHot node must have three inputs.Constrains to boolean tensors.condition'axis' must be in [-rank(indices), rank(indices)-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).depth(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrain to any tensor type.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Blocksize must be positiverepeats'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Constrain input 'X' and output 'Y' to all tensor types.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.roiFour modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).constant_valueConstrains input and output to only numeric types.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`Constrain to boolean tensors.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.inverse_indicesaxes as an input and attribute cannot be specified at the same time.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Constrain input and output types to high-precision and 8 bit numeric tensors.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).p value of the Lp norm used to pool over the input data.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Attribute pooled_shape must be specifiedROI pool output shape (height, width).RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthStride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `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.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 sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Input tensor must have atleast 2 dimensionsDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\defs.ccConstrain input and output types to float and 8 bit tensors.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.saved_varsaved_meanvarThe axis on which to apply normalization, -1 mean last axis.ratio(Optional) Seed to the random generator, if not specified we will auto generate one.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.training_modePadding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter 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 axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.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.The pads attribute cannot be used simultaneously with auto_pad attributemomentum Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).EpsilonA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.List of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseX_varianceProcessed_STDVarianceSTDX_squaredE_XsquaredX_RMEX_squaredThe number of channels to sum overConstrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). ) for attribute 'axis'Invalid value(Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.Scaling parameter.The exponent.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.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.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum 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=11-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Input is ether string UTF-8 or int32/int64Input shape must have either [C] or [1,C] dimensions where C > 0list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.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\s\cmake\external\onnx\onnx\defs\math\old.ccThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).broadcastlegacy optimization attribute. If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Pass 1 to enable broadcastingDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeCoefficient of leakage default to 0.01.consumed_inputsIf set, defines the broadcast dimensions. See doc for details.Value of alpha default to 0.2Value of beta default to 0.5Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxNumber of top elements to retrieveDimension on which to do the sort.Invalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.GreaterOrEqualLessOrEqualConstrains input types to all numeric tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input to boolean tensor.Constrains output to boolean tensor.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).Constrains input/output to boolean tensors.Attribute expected to have tensor typeAttribute expected to have a one-dim tensorsparse_valuevalue_intAttribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.value_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_floatsvalue_stringvalue_intsvalue_floatThe value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the elements of the output tensor.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.(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 standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Constrain output types to integral tensors.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Input tensor must have rank 2sub_resultsub_result_castedcurrentrangeprevcond_outloop_body_attributeivariadic_outputAll inputs to 'Range' op must be of the same typeceil_result_relu_intceil_result_relu_boolceil_resultceil_result_reludelta_casteddiv_resultD:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.Constrain output types to be numerics.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 float32Shape input must be a one-dimensional tensor.Invalid shape value: Constrain 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'.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.The mean of the normal distribution.Constrain output types to float tensors.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Lower boundary of the output values.Upper boundary of the output values.Constrain input types to common numeric type tensors.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\old.ccOnly one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.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.(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.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain output mask types to boolean tensors.The ratio of random dropoutIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Stride along each spatial axis.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccp value of the Lp norm used to pool over the input data, default is 2.0.Stride along each axis.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.Dilation value along each spatial axis of filter.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testIf true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0List 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`.paddingslength of each outputoutputs...Which axis to split ontilesNew shapeheight_scaleTwo interpolation modes: nearest(default), bilinearwidth_scaleThe scale along height dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.Attribute 'scales' must have floats type.Attribute 'scales' is required.Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (D:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.cc 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).length of each output. Values should be >= 0.rank must be greater than axisWhich axis to concat onWhich axis to concat on. Default value is 1.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Attribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Incorrect or missing attribute value for starts and endsList 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 scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1](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)]List of non-negative integers, indicate the dimensions to be insertedOne float, indicates the value to be filled.Attribute pads has incorrect lengthList 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 value for pads is requiredWhich axis to split on. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices. expected to have rank >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'.default 1; Pooled output Y's height.default 1; Pooled output Y's width.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. max_output_boxes_per_classboxesD:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccselected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.score_thresholdiou_threshold expected to have rank Dimension mismatch in unification between but has rank 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. else=An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output Mismatched tensor element type for output then=Mismatched type for output (Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.Input type for input at index Element type of inputs are expected to be the same.SequenceConstruct is expected to have at least 1 input. is null. Type info is expected.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.ccinput_sequence Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=Input Sequence and Tensor are expected to have type info. Current type is null.output_sequenceConstrain input types to any tensor type.Input type for input at index 0 is null. Type info is expected.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).positionOnly supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrain split size to integral tensor.Constrain output types to all tensor types.Constrain input types to all tensor types.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Input 'split' can not be empty. sum of split values=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.], Value=Invalid value of attribute 'axis'. Accepted range=[new_axis must be either 0 or 1Which 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.If set, defines the broadcast dimensions.Enable broadcastingD:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccConstrains input to integral tensors.Constrains input to float tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\old.ccOne (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.First input tensor must have rank 3When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\defs.ccThe sequence output for the hidden is optional if 0. Default 0.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.fowardD:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\old.cctensor(complex128complex64opaquemapsparse_tensor(map(seq( ==> Context: ' should be stored in field '' instead of 'STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field.TensorProto (tensor name: ) is 0-element but contains data!TensorProto ( tensor name: Unrecognized data_type (tensor name: values of data_type '] out of range [0, Sparse tensor indices () has values, but NNZ is sparse_tensor_proto] not in lexicographic sorted order.] out of range.) second dimension size does not match rank of tensor.) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () index value at position [' of value_info is required to be non-empty.): value_typekey_typeelem_typetype is required but missing.Field 'nameint64_datastring_dataint32_datafloat_datasetting data_type field (tensor name: ) to UNDEFINED is not alloweddata_typeUnrecognized type value case (value_info name: ) is stored externally but doesn't have a location.) should be stored in , but it doesn't exist or is not accessible.Data of TensorProto ( tensor name: ) is stored externally and should not have data field.uint64_datadouble_dataraw_data imports opset version where as the model imports opset version ' has been used as output names multiple times.Bad node spec: Nodes in a graph must be topologically sorted, however input '' of node: ConstantFillattr) has no index values.) must have rank 1 or 2.) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values () must have rank 1.NodeProto (name: , type: ) has zero input and zero output.op_type) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.type field and data field mismatch in attribute ' has been used as graph input names multiple times.graphOp registered for is deprecated in domain_version of No Op registered for with domain_version of No opset import for domain 'Warning: Checker does not support models with experimental ops: is not output of any previous nodes. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty name in initializer but not in graph input initializer name is not uniqueTensor initializers must have a non-empty nameinitGraph must be in single static assignment (SSA) form, however '(op_type:, node name: Graph has output input is out of bounds) vs (NOT_SETsparse_tensor_typeopaque_type does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. models with experimental operators: type case unsupported. existing=type case mismatch. existing= inferred=Inferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: ( inputs but were providedONNX models don't support multiple opset version imports for a domain. Function Warning: Unsupported operator . No schema registered for this operator.Warning: Shape inference does not supportInput 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'Dimension value inferred () is not equal to the existing dim value (onnx.TrainingInfoProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.AttributeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TensorProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.ModelProtoonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TensorShapeProto   FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINEDonnx.FunctionProtolist count RE2: invalid startpos, endpos pair. [startpos: endpos: text size: Invalid RE2: pattern too large - reverse compile failedSearchNFA inconsistencySearchBitState inconsistencySearchOnePass inconsistencySearchDFA inconsistencyDFA out of memory: size bytemap range Error reverse compiling 'pattern too large - compile failedError compiling 'Error parsing '': D:\a\_work\1\s\cmake\external\re2\re2\re2.ccUnknown encoding Regexp not destroyed.invalid character class rangeinvalid character classinvalid escape sequenceunexpected errorno errorUnexpected op in Regexp::Equal: Bad reference count D:\a\_work\1\s\cmake\external\re2\re2\regexp.ccinvalid UTF-8invalid perl operatorbad repetition operatorinvalid repetition sizeno argument for repetition operatortrailing \missing )missing ]NumCapturesWalker::ShortVisit calledinvalid named capture groupWalk NULLD:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hStack not empty. VVVVV@V0V VVVVVVxVshould never happenD:\a\_work\1\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classMissing case in Compiler: AddFoldedRange recurses too much.D:\a\_work\1\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad hex digit AnyBad call to ParseState::ParsePerlFlags in stepUnhandled context does not contain textBad args: nsubmatch= in AddToThreadqunhandled D:\a\_work\1\s\cmake\external\re2\re2\nfa.ccUnexpected opcode in short circuit: in ComputeFirstByteunhandled opcode: |RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetFailed to analyze start state.D:\a\_work\1\s\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.Bad arg in kInstAltMatch: Bad arg in kInstCapture: job_.size() = njob_ = GrowStack() failed: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.cc arg Unexpected opcode: Cannot use SearchOnePass for unanchored matches.D:\a\_work\1\s\cmake\external\re2\re2\onepass.ccD:\a\_work\1\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: D:\a\_work\1\s\cmake\external\re2\re2\simplify.ccCase not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat )V@*V)V@*V)V*V*V*V*VX*V *VX*V0*Vh*VP)Vh*V`)V*Vp)V*V)V\*V)V\*V)V*V)V*V)V`*V(V`*V(V*V(V*V(V*V)V*V)VP*V )VP*V0)V*Vp(V*V(Vp*V(Vp*V(V*V(V*V<)Vd*V@)Vd*VD)V*VH)V*VL)V*V)V*V[:^upper:][:word:][:^word:][:xdigit:][:^xdigit:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:]\d\D\s\S\w[:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:]\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:]09AZaz 09az09 09AZ__az ~09AZ__az!~ !/:@[`{~AZazAZ09AFafpMVVxMVxVMVHyVMVPbVrV#MVVMVVMVV$NVV[V0NV[V@TV\VHTVxVXTV|V`TVشVpTV@VTV`[VTV0xVTVVSVyVSVVTVsVTVxVTVtV$TVuV,TVyV 4TV[VTVprVTVxVTVVTV0vVTVXxVTVDxVTVȫVUVPV4#UXVTV@VTVyVTVVTVVAdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanLimbuLinear_ALinear_BLisuLlLmLoLtCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicChamCherokeeCoCommonCopticCsCuneiformCypriotHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiPau_Cin_HauPcPdPePfPhags_PaPhoenicianPiOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyreneSharadaShavianSiddhamSignWritingSinhalaSkSmSoPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianNNabataeanNdNew_Tai_LueNewaNkoNlNoTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaZanabazar_SquareZlZpZsThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYi0J'  @n_n4Mh  89;>@DFFJPl!<Up4Vn!`lA0000@ U X _ $$   ?? 88ii@[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++++.N.//00000 00070<0?00000001111 2_222X33MM!09..ϩϩ[[>?0RTfhk ;@[epp3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq --  ....:.;.@.@.0000000012XXcc ooWW  ? ? 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