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;Н;;О;;H!O0;@t%O`<<;;P`<`Xp,<0<p; < <P@+Op<@<;;P`0<`Xp,<<p;<<P#Ou<<;;P`z<`Xp,Ћ<<p;{<<P+OP]<o<`;P;P`P`<`Xp,k<@h<p;a<Pe<PH,O`<p<;;P`P<`Xp,0T<S<p;0Q<R<P$O0;< N<;;P`><`Xp,J<G<p;@<D<P'Oo<Pt<;p;P`p<`Xp,s<0s<p;p<@r<P0&O<p<;;P`<`Xp,@<<p;<<P&O<`<;p;P``<`Xp,Ю<0<p;<@<P*ON<PU< ;;P`P<`Xp,0T<S<p;0Q<R<P#O@2<9<;;P`3<`Xp,8<p7<p;3<5<Pp%O0#<01<;;P`@&<`Xp,`.<,<p;'< *<PH*O<p<@;0;P`<`Xp,<p<p;P<<P&O< <;;P`<`Xp,0<<p;<<PH"OPV<[<@;0;P` W<`Xp,[<@Z<p;W<@Y<Ph+Op<<`;P;P`<`Xp, <<p;<<P@)O<Щ<;;P`<`Xp, <<p; <<PX&O < "<;;P``<`Xp, <<p;<<P0$O<< ;;P``<`Xp,Ю<0<p;<@<P#O;<`;P;P`<`Xp,<@<p;P< <P0'O`<@<;;P`@<`Xp,<@<p;<<P0-Oj=g=g=h=>p-OPR=Q=g=h=Q=P.Opu=p===P=.O===И=>.Oj===h=p='>'>'>(>0(>P(>p(>(>/O>>0>P>>8/O>@>>P>`>/O>@>P>`>>1OPR=>>h=PUnknown exceptionbad array new lengthC:\agent\_work\2\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\agent\_work\2\s\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longstring too longC:\agent\_work\2\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.C:\agent\_work\2\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\agent\_work\2\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %I64u::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xtyinvalid string position...\\?\\/INVALID_ARGUMENT:C:\agent\_work\2\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., C:\agent\_work\2\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: CpuVIWEFai.onnx.trainingai.onnx.mlai.onnx.preview.training!=C:\agent\_work\2\s\include\onnxruntime\core/framework/tensor.h)IsTensor()Trying to get a Tensor, but got: C:\agent\_work\2\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableIsTensorSequence()ai.onnxCPUExecutionProviderDmlExecutionProviderSafeIntExceptionHandler::SafeIntOnOverflowInteger overflowC:\agent\_work\2\s\onnxruntime\core/common/safeint.hTensor sequence must contain only primitive typesonnxruntime::TensorSeq::SetTypeelem_type_ != nullptri < tensors_.size()C:\agent\_work\2\s\onnxruntime\core/framework/TensorSeq.hsize overflowonnxruntime::TensorSeq::Getnot enough space: expected , got type is not supported in this functionRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryMem pattern should be disabled when using DML execution provider.RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryonnxruntime_profile_Sequential execution should be enabled when using DML execution provider.output name cannot be emptyinput name cannot be emptyshape is invalidinput array is too shortout of indexspace is not enoughindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.Unsupported tensor element type in the input: in[idx]->IsTensor()Expecting all elements to be tensors. Got: C:\agent\_work\2\s\onnxruntime\core\session\onnxruntime_c_api.ccOrtCreateValueImplSeqHelperEach element of the sequence should be either tensor or map.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported input typeAt least one element in the sequence is of a type different from others.Either the key tensor or the value tensor has NumDimensions > 1For map type num_values MUST be 2Key type is not supported yet.Key and value tensors have unequal number of elements.opaque(Number of values should be at least 1.Specified domain and type names combination does not refer to a registered opaque type,OrtApis::CreateOpaqueValueml_type != nullptrnon_tensor_base != nullptrOpaque type is not a non_tensor type!!!1.3.0OrtApis::GetOpaqueValueT utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. onnxruntime::Tensor::Dataonnxruntime::Tensor::MutableDataTensor must always contain primitive types. Found: Invalid index requested for map type.OrtCreateValueImplMapHelpervalue_type != nullptrMap is missing type entry for its valueValue type is not supported yet: onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::check++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkC:\agent\_work\2\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkcalled_ < 2Check for duplicate types in MLTypeCallDispatcherRetSequence is missing type entry for its elementonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Getonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::check != invalid vector subscriptlen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::DataTypeImpl::GetType() == type_Encountered nullptr.OrtCreateMapMLValueAttempt to use DefaultLogger but none has been registered.not implementedgraph_optimization_level is not validexecution_mode is not validOrtApis::SetIntraOpNumThreadsSince openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads.C:\agent\_work\2\s\onnxruntime\core\session\abi_session_options.ccunknown is not implementedonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughUnsupported version 'onnxruntime::CreateCustomRegistryAC:\agent\_work\2\s\onnxruntime\core\session\custom_ops.ccenv_ptr == p_instance_OrtEnv::ReleaseC:\agent\_work\2\s\onnxruntime\core\session\ort_env.ccbad cast]CUDAExecutionProviderProvider onnxruntime::ExecutionProviders::Add has already been registered.Trying to get a SparseTensor, but got: IsSparseTensor()C:\agent\_work\2\s\onnxruntime\core/framework/execution_providers.h allocator already registered.onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\agent\_work\2\s\onnxruntime\core/framework/feeds_fetches_manager.hCompute_Create_State_Release_State_MemcpyTransformer] [json.exception.other_error%Y-%m-%d_%H-%M-%SThe environment variable contained the value: ORT_LOAD_CONFIG_FROM_MODELReading the provided model for the ORT configonnxruntime::FinalizeSessionOptions are '0' and '1'. The only supported values for the environment variable is_model_proto_parsedCould not finalize session options while constructing the inference session. Error Message: C:\agent\_work\2\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itgraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truestatus.IsOK()onnxruntime::InferenceSession::ConstructorCommonUsing 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.intra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionsession_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerError during EndProfiling(): onnxruntime::InferenceSession::~InferenceSessionCould not parse model successfully while constructing the inference sessionresultMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderUnknown error during EndProfiling()Received nullptr for exec providerThis session already contains a loaded model.onnxruntime::InferenceSession::Loadonnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryEncountered unknown exception in Load()Exception during loading: Unknown exception in Load()onnxruntime::InferenceSession::TransformGraphCould not find an implementation for the node ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoNode placements].( ( Provider: [Main Graph instance should have populated all subgraphs when being resolved.All nodes have been placed on [: [CreateSubgraphSessionState should have created an entry earlier.subgraph_session_statesubgraphonnxruntime::InferenceSession::CreateSubgraphSessionStateInitializing session.onnxruntime::InferenceSession::Initializeonnxruntime::InferenceSession::InitializeSubgraphSessionsp_op_kernelSession has already been initialized.Adding default CPU execution provider.Model was not loadedModel was not loaded.Session successfully initialized.Exception during initialization: Parallel execution mode doesn't support CUDA Execution Provider currently.Serializing Optimized ONNX 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. Please fix either the inputs or the model. Expected: Encountered unknown exception in Initialize()session_initialization for the following indices Got invalid dimensions for input: Got: Invalid rank for input: Unexpected input data type. Actual: ( elements. index: ) , expected: (Invalid Feed Input Name: is not expected to be of type tensor.elements, but feeds has Size mismatch: feed_names has is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Input with name: onnxruntime::InferenceSession::ValidateInputsp_fetches->size(): Output vector incorrectly sized: output_names.size(): Output vector pointer is NULLAt least one output should be requested.onnxruntime::InferenceSession::RunSession not initialized.Invalid Output Name:Session was not initializedmodel_runonnxruntime::InferenceSession::GetModelMetadataRunning with tag: Encountered unknown exception in Run()onnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersCould not write a profile because no model was loaded.Profiler is disabled.onnxruntime::InferenceSession::EndProfilingInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunExceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevelsession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggeronnxruntime::InferenceSession::AddPredefinedTransformers961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_list too longlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeString0sEcom.microsoft.mlfeaturizerscom.microsoftcom.microsoft.nchwc[TypeInferenceError] expected to have tensor typeInput Output unknownElement type of input tensor(uint32)tensor(uint64)tensor(float16)tensor(float)tensor(int32)tensor(int64)tensor(uint16)tensor(int8)tensor(double)tensor(uint8)tensor(int16)tensor(complex64)tensor(complex128)tensor(string)tensor(bool)) from file version: line , but it is already registered from file known by the checker., but it its domain is not (domain: Trying to register schema with name bumped the operator version but ] (usually, this means you in onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange Schema error: in the inclusive range [, but it its version is not inter-opC:\agent\_work\2\s\onnxruntime\core\session\environment.ccinputXMemcpyFromHostTConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.outputYMemcpyToHostException caught: p_provider: value'parse_errorparse errorinvalid_iteratortype_error, column at line session_optionsUnsupported value for intra_op_num_threads: out_of_rangeort_configSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetIntraOpNumThreadsC:\agent\_work\2\s\onnxruntime\core\session\inference_session_utils.ccUnsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeonnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Setting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLSequential modeParallel modeSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetEnableProfilingSetting enable_profiling to Unsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::InferenceSessionUtils::ParseOrtConfigJsonInModelProtoORT config json from the model: The Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelDid not find session options in the model file to be used while running the modelonnxruntime::InferenceSessionUtils::ParseSessionOptionsFromModelProtoJson stored in the `ort_config` key cannot be parsed. Error message: The Model Proto hasn't been checked for the ORT config json.inter_op_num_threadsinter_op_num_threads option in the model file must be an integerintra_op_num_threadsintra_op_num_threads option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integerIgnoring unsupported session option in ORT config: enable_profilingenable_profiling option in the model file must be an integercannot use at() with ' not foundkey 'nullinvalid map keycannot use key() for non-object iteratorsstringbooleanobjectarraydiscardednumbercannot compare iterators of different containerscannot get valuesyntax error while parsing unexpected ; expected - ; last read: 'invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if givennull literalstring literaltrue literalfalse literal'{'']'number literal'['',''}'':'unknown tokeninvalid number; expected digit after '-'end of input'[', '{', or a literalinvalid number; expected digit after exponent signinvalid string: missing closing quoteinvalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: ill-formed UTF-8 bytevector too longobject keyobject separatornumber overflow parsing 'cannot use erase() with type must be number, but is iterator does not fit current valueiterator out of rangeC:\agent\_work\2\s\include\onnxruntime\core/graph/graph.h Max:onnxruntime::Graph::NodeAtIndexImplValidating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::GraphTransformer::RecurseC:\agent\_work\2\s\include\onnxruntime\core/optimizer/graph_transformer.hCasttoCastDef_%dcast node to cast from float16 to float32 on cpuC:\agent\_work\2\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImplRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplACLExecutionProviderEliminateSliceIdentityEliminateIdentityConvBNFusionConvConvMulFusionSliceConvAddFusionRandomUniformLikeRandomNormalRandomUniformConstantFoldingUnsqueezeUnsqueezeEliminationMultinomialRandomNormalLikeEliminateDropoutMatMulAddFusionGemmActivationFusionConvActivationFusionShapeToInitializerReluFuseReluClipDropoutGeluFusionBiasGeluNchwcTransformerShapeSkipLayerNormFusionLayerNormFusionFastGeluFusionGeluApproximationExpandEliminationAttentionFusionReshapeFusionEmbedLayerNormFusionLevel_RuleBasedTransformerCastEliminationExpandUnsupported level C:\agent\_work\2\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelonnxruntime::optimizer_utils::GenerateTransformersOpenVINOExecutionProviderNGRAPHExecutionProviderDnnlExecutionProviderTensorrtExecutionProviderNupharExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::TransformerMemcpyImpl::ProcessDefsC:\agent\_work\2\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImplCopy from/to host memoryMemcpyExecution type '' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()dup_replacements.find(&arg) == dup_replacements.end()C:\agent\_work\2\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::Applyonnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered C:\agent\_work\2\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::Initializer::Initializerunsupported data type: size_ == sizesize is differentC:\agent\_work\2\s\onnxruntime\core/optimizer/initializer.hReadExternalRawData() failed: onnxruntime::BiasGelu::ApplyImplFastGeluGeluAddC:\agent\_work\2\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and GeluCould not find OrtValue with name 'can't constant fold node 'C:\agent\_work\2\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplUnsupported output type of . Can't constant fold fetches.size() == node->OutputDefs().size()Could not find a CPU kernel and hence Cast mask from int64 to int32MaskCastMaskIndex_Int32Count number of wordsqkv_weightsqkv_biasMask_Int32Mask shape is unknown or not 2D, or data type unknownonnxruntime::ProcessMaskC:\agent\_work\2\s\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32ReduceSumMaskIndexaxeskeepdimsAttention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.Total fused Attention node count: ReshapeTransposeonnxruntime::AttentionFusion::ApplyImplLayerNormalizationshape of layer norm bias tensor not expectedMatMulFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3v_reshape initializer value is not expectedreshape initializer value is not expectedFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for nodes in path vpermFailed to find path for maskOutput edge count not expected for mask nodesaxisSoftmax attribute axis is expected to be 3Failed in match v_matmul and v_add input shapeSoftmaxMulSubDivFailed to find path for qq root should be layer normalizationq_reshape const not matchedmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedk root is not layer normk_transpose perm attribute not matchedk_matmul and k_add shape not matchedk_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedq_matmul and q_add shape not matchedFailed to find path for kAttentionnum_headsFused an attention node.Failed 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 create mask indexFused Attention subgraphs data type is not supportedonnxruntime::Initializer::ToProtoC:\agent\_work\2\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoepsilonbn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyBatchNormalizationconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_Unexpected data type for Clip input of maxonnxruntime::ConvActivationFusion::ApplyImplSigmoidTanhonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()C:\agent\_work\2\s\onnxruntime\core\optimizer\conv_activation_fusion.ccClipminFusedConvfused activationactivation_paramsLeakyRelualphawith activation fused Conv C:\agent\_work\2\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccUnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::Applyis not supported.C:\agent\_work\2\s\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantFuseReluClip_data type Unexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyGelu approximationTotal Gelu Approximation (FastGelu) node count: onnxruntime::GeluApproximation::ApplyImplC:\agent\_work\2\s\onnxruntime\core\optimizer\gelu_approximation.ccC:\agent\_work\2\s\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: Concatonnxruntime::ReshapeFusion::ApplyImplGatherCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph1Cast Input from int64 to int32_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInput_Int32Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.C:\agent\_work\2\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchPositionSubgraphNonZeroConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraph1Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.SqueezeRangeFailed to find path 1.onnxruntime::MatchPositionEmbeddingSubgraph2Output edge count not expected for nodes in path 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.position_embeddingsonnxruntime::EmbedLayerNormFusion::ApplyImplInput id is not valid. Segment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.EmbedLayerNormalizationInput_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs ̼+endsstartsFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.The model has input '' onnxruntime::FreeDimensionOverrideTransformer::ApplyImplConflicting free dimension overrides.C:\agent\_work\2\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccwith a fixed dimension size which does not equal the specified override of Gemmfused Matmul and Add C:\agent\_work\2\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplHardSigmoidEluSoftplusSeluC:\agent\_work\2\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplFusedGemmfused Gemm ScaledTanhSoftsignThresholdedReluParametricSoftplusactivation_C:\agent\_work\2\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplyConvMulFusion_W_mul_B_tensor_protoConvMulFusion_Mul_B_SkipLayerNormalizationonnxruntime::SkipLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs C:\agent\_work\2\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cc̼+kernel_shapedilationsNOTSETauto_padReorderInputreorderstridespadsspatial_nchwcSAME_LOWERSAME_UPPERgroupVALIDnearestmodeasymmetriccoordinate_transformation_modeReorderOutput_bn_nchwcchannels_lastchannelsAveragePoolMaxPoolGlobalAveragePoolGlobalMaxPoolfloornearest_modescalesUpsampleResizeSumC:\agent\_work\2\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImpldeque too longC:\agent\_work\2\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplSqrtReduceMeanfused LayerNorm subgraphs Pow'7onnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeC:\agent\_work\2\s\onnxruntime\core\optimizer\rule_based_graph_transformer.ccErffused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplC:\agent\_work\2\s\onnxruntime\core\optimizer\gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplC:\agent\_work\2\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccConvAddFusion_Add_B_add_B_tensor_protoConvAddFusion_B_onnxruntime::ConvAddFusion::ApplyC:\agent\_work\2\s\onnxruntime\core\optimizer\conv_add_fusion.ccNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING model_path must not be empty. Ensure that a path is provided when the model is created or loaded.onnxruntime::Initializer::ReadExternalRawDataC:\agent\_work\2\s\onnxruntime\core\optimizer\initializer.ccExternal data type must not be UNDEFINED or STRING.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length Computed size: TensorProto external data size mismatch. Satisfied, but should not be: model_path.IsEmpty() , external_data.length: Failed to get allocator for optimizerallocator_ptr_cpu_execution_provideronnxruntime::OptimizerExecutionFrame::Info::InfoC:\agent\_work\2\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccProvided CPU execution provider is a nullptronnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()Tried to allocate without valid type information, ort_value index=invalid unordered_map keyonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsC:\agent\_work\2\s\include\onnxruntime\core/framework/op_kernel.hseedT2T1onnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shape0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1ratio input should have a single value.onnxruntime::Tensor::DataAsSpanonnxruntime::OpKernelContext::InputMissing Input: onnxruntime::Tensor::MutableDataAsSpanEinsumC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsuminfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeequationEinsum op: An implementation for the input type is not supported yetC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\einsum.cconnxruntime::Einsum::ComputeThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one inputbatch_dimsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::ComputeGatherNDTindGatherNDBase PrepareForCompute: Input count mismatchinvalid index found, index = last dimension of indices must not be larger than rank of input tensorindices tensor must has rank larger than 0onnxruntime::GatherNDBase::PrepareForComputeinput_tensor != nullptr && indices_tensor != nullptraxis is not in valid range [-C:\agent\_work\2\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1ReduceL2ReduceL1ArgMinArgMaxReduceSumSquareReduceProdReduceMinReduceMaxReduceLogSumExpReduceLogSumonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()C:\agent\_work\2\s\include\onnxruntime\core/common/optional.honnxruntime::optional<__int64>::valuehas_value_onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseselect_last_indexC:\agent\_work\2\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::PrepareForReduceinput_tensor_ptr != nullptrInput to be reduced is nullC:\agent\_work\2\s\onnxruntime\core/providers/cpu/containers.honnxruntime::GetAllocatorin_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:input_tensor_ptr->Shape().Size() == input_shape_override->Size()The input shape override's size does not match the input tensor's shape sizeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.GlobalLpPoolC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/autopad_type.honnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringinfo.GetAttr("storage_order", &storage_order).IsOK()storage_orderinfo.GetAttr("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()C:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesinput_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] > 0onnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()ponnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeNot satisfied: pooling_dims == pool_attrs_.kernel_shape.size() kernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size.Not satisfied: input_dims >= 3 C:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.C:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/pool_base.hLpPoolIUnsupported pooling size : Not satisfied: x_shape.NumDimensions() >= 3 onnxruntime::Pool::Computeonnxruntime::MaxPoolV8::ComputeImplonnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()Unsupported data type: onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::GetPlease fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Output is invalid.Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterAbsNegFloorCeil by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::BroadcastIterator::InitC:\agent\_work\2\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::AppendMeanBitShiftNotAndOrXorUnsupported X type: Must have 1 or more inputsReciprocalExpLogMaxMinLessGreaterEqualonnxruntime::Max_6::Computeonnxruntime::Mean_6::ComputeSinCosTanAsinAcosAtaninput_count >= 1onnxruntime::Sum_6::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapedata_1.Shape() == shapedata_n.Shape() == shapeinputCount >= 1onnxruntime::Min_6::Compute(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModModfailed to get first output!onnxruntime::mod_internal::BroadCastMFloat16FMod vs X and Y input types do not match: fmod attribute must be true for float, float16 and double typesSinhCoshAsinhAcoshAtanhPRelufmodfmod must have value either 0 or 1directiononnxruntime::BitShift::BitShiftLEFTfmod_onnxruntime::Mod::Computeonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: onnxruntime::BroadcastVariadicShape must be 1 dimensional as it's tensor data is a shapetensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8::Computeonnxruntime::Expand_8::ComputeRIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()onnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::utils::MLTypeCallDispatcher::Invokehelper.called_ == 1onnxruntime::TensorAllocator::TensorAllocatoronnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocator((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseC:\agent\_work\2\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl::operator ()C:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.max->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::utils::MLTypeCallDispatcher::Invokemin should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl::operator ()round_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or (default) or . It can only be mode attribute is linearcubicinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/upsample.hinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuehalf_pixelround_prefer_ceilceil] is not supported!nearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.scale >= 1onnxruntime::UpsampleBase::ScalesValidationonnxruntime::UpsampleBase::StringToUpsampleModepytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resize] is not supportted!coordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeonnxruntime::UpsampleBase::ParseScalesDataScale value should be greater than 0.scale > 0Resize operatorUpsample operator'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)scales size should be greater than 0.scales_size > 0bad allocationT3OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computekop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()TopKop_kernel_info.GetAttr("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeonnxruntime::TopkOpset11ConstructorCommonlargestk argument [output count mismatch, expected 2 outputs to be present for TopK operator] should not be greater than specified axis dim value [Unsupported tensor type of Uniqueinput count mismatchlimit in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!delta in Range operator should be scalar like tensor, yet got shape:onnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueInvalid 'pads' attribute valueC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/pad.hconstantedgereflectC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkipsonnxruntime::SliceIteratorBase::Initdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Paddims.size() == extents.size() && dims.size() >= steps.size()pads_tensor.IsDataType()Pads tensor should be an INT64 tensorC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::Pad::ComputeUnexpected mode of Input tensor has no dimensionsonnxruntime::Pad<__int64>::Computeonnxruntime::PadCpuImpldata_rank > 0data_rank * 2 == pads.size()'pads' has wrong number of valuesonnxruntime::PadInputWithDimValueOfZeropads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->IsDataType() && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensoronnxruntime::Pad::Computeonnxruntime::Pad::Computeinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueGatherElements op: Cannot operate on scalar inputGatherElements op: Value in indices must be within bounds [GatherElements 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: Data type of input 'data' should match the data type of the outputC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElementsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::parse_and_validate_indices_tensor , ]. Actual value is onnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2C:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\gemm_helper.h K: N:Gemm: Invalid bias shape for broadcastGEMM: Dimension mismatch, W: info.GetAttr("transB", &temp).IsOK()transBbetainfo.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()M_ >= 0 && K_ > 0 && N_ >= 0info.GetAttr("transA", &temp).IsOK()transAC:\agent\_work\2\s\onnxruntime\core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemmc_shape is required if c_data is providedonnxruntime::Gemm::ComputeGemmc_shape != nullptronnxruntime::ScatterND::ComputeScatterNDC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDBase::PrepareForComputeinput_tensor != nullptrupdate_tensor != nullptrindice_tensor != nullptrinput shape: , indices shape: , data shape: input tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: invalid indice found, indice = Tensor size () != new size (onnxruntime::Tensor::Reshapeshape_.Size() == new_shape.Size()Sonnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxI != nullptrGot nullptr input for index tensorInvalid sequence index () specified for sequence of size (SequenceEmptySequenceAt: Got nullptr for output tensorGot nullptr for sequence input.SequenceLengthonnxruntime::SequenceLength::ComputeX != nullptrSequenceLength: Got nullptr for output tensorC:\agent\_work\2\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceAtY != nullptr), input tensor data type (Got nullptr for input tensor.SequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceErase::ComputeSequenceEraseSequenceConstructSequenceErase: Got nullptr for output sequenceSequenceEmpty: Got nullptr for output sequencedtypeUnsupported 'dtype' value: onnxruntime::SequenceEmpty::Computeonnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertonnxruntime::SequenceInsert::ComputeS != nullptrsplit_size_sum () != split_dim_size (onnxruntime::GetScalarSplitInputInvalid data type for split tensor onnxruntime::GetSplitSizesInputonnxruntime::SequenceConstruct::Computenum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequence yetSplitToSequenceonnxruntime::SplitToSequence::ComputeSplitToSequence operator does not support split_scalar > 0Split should be > 0Invalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributenew_axisC:\agent\_work\2\s\onnxruntime\core/providers/cpu//tensor/concat.hConcatFromSequenceC:\agent\_work\2\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\controlflow\if.ccthen_branchnum_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::Infoelse_branchinfo.GetAttr("else_branch", &proto).IsOK()info.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::IfBV outputs which doesn't match the subgraph's 'If' node has If outputs. did not.Subgraph must have the shape set for all outputs but session_stateonnxruntime::IfImpl::Initializeonnxruntime::IfImpl::ExecuteFailed to create output tensor for Failed to create output tensor for If output onnxruntime::If::SetupSubgraphExecutionInfoCreateFeedsFetchesManager must be called prior to execution of graph.SetupSubgraphExecutionInfo should only be called once for each subgraph.info == nullptr' attribute.Subgraph SessionState was not found for 'then_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::ComputeX num_dims does not match W num_dims. group: output_padding W: kernel_shape is not compatible with W shape. X: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.dim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hInvalid input shape: pad type not supported*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeOnly 1D and 2D ConvTranspose is supported. num_input_channels: output_shapegroup count is <= 0Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForCompute filter_number: filter number not equal to input channel number.ConvTransposeonnxruntime::ConvTranspose::DoConvTransposeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cc M: Output channels M is not divisible by group. C: Input channels C is not equal to kernel channels * group. kernel channels: onnxruntime::math::Im2colNd::operator ()C:\agent\_work\2\s\onnxruntime\core/util/math.hC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\conv.ccd_iter[d_i] < d_maxonnxruntime::Conv::ComputeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.dilation == 1onnxruntime::ConvAttributes::InferOutputShapeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ComputePadAndOutputShapepad type not supported.Out of bound access to arrayC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0info.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolpads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Not satisfied: tensor_shape->Shape().GetDims().size() == 1 inferred output shape:Not satisfied: I_shape == X_shape Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape:Not satisfied: given_shape.Size() >= shape.Size() MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 Index tensor shape should be same as that of the input data tensor to unpool.onnxruntime::MaxUnpool::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\Unpool.cccenter_point_boxNot satisfied: boxes_shape.NumDimensions() == 3 scores must be a 3D tensor.scores_tensorboxes must be a 3D tensor.Not satisfied: boxes_dims[0] == scores_dims[0] boxes and scores should have same spatial_dimension.Not satisfied: scores_shape.NumDimensions() == 3 boxes and scores should have same num_batches.onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseC:\agent\_work\2\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::PrepareComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccNonMaxSuppressionboxes_tensoroutput != nullptrNot satisfied: boxes_dims[2] == 4 iou_threshold must be in range [0, 1].Not satisfied: boxes_dims[1] == scores_dims[2] The most inner dimension in boxes must have 4 data.onnxruntime::NonMaxSuppression::ComputeNot satisfied: ret.IsOK() onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) onnxruntime::Scatter::ScatterIndices dim=Unknown tensor type of is greater than input dim= at pos=onnxruntime::Scatter::ComputeExpecting indices to be either int32_t or int64_tScatterElementsdata type is different from updates typeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\scatter.ccScatter. Input rank=Indices must have the same rank as Input. Indices rank=Indices and updates must have the same rankIndices vs updates dimensions differs at position=indices element out of data bounds, idx= must be within the inclusive range [onnxruntime::Det::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\det.ccDetInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedonnxruntime::UnsqueezeBase::UnsqueezeBaseC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::UnsqueezeBase::PrepareComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::Unsqueeze::ComputeDimension of input must be 1 instead of . shape=C:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' attribute. All values must be > 0splitC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase Axis= NumOutputs=Split 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::ComputeImplC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::ComputeSplit operator does not support Cannot split using values in 'split' attribute. Axis=!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attributeStarts must be a 1-D array'step' value cannot be 0'axes' has duplicates'axes' has an axis outside of the tensor dimension countEnds must be a 1-D arrayC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::FillVectorsFromInputstart_tensor.Shape().NumDimensions() == 1onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of Unsupported input data type of Cannot slice scalarsonnxruntime::SliceBase::ComputeMissing input tensor to be processedStarts and axes shape mismatchstart_tensor.Shape() == ends_tensor.Shape()Starts and ends shape mismatchends_tensor.Shape().NumDimensions() == 1Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got nullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Starts and steps shape mismatchnullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endType for Tind not supported yet in Gather.C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase and Not satisfied: data_n.DataType() == p.output_tensor->DataType() Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of Cannot concatenate scalarsNot satisfied: input_count >= 1 C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: inputs_0_rank > 0 axes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()CompressC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeonnxruntime::Flatten::Flattenonnxruntime::Flatten::Computegsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/flatten.hFlattenC:\agent\_work\2\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.onnxruntime::Scan<9>::Scaninfo.GetAttr("body", &proto).IsOK()bodyscan_input_directionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()num_scan_inputsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptr Expected dimensions or more but input had shape of onnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.Number of entries in 'scan_input_axes' was but expected scan_input_axesscan_output_directionsgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Subgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputoutput_mlvalueOutput OrtValue has not been created for loop state variable output onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensors but input '' dimension has length of Invalid scan input:Invalid value in scan_input_axes for input of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was Outputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::ScanImpl::ExecuteScanonnxruntime::ScanImpl::TransposeOutputblocksizeAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpaceinput.Shape().NumDimensions() == 4onnxruntime::DepthToSpace::Computeinput_depth % (blocksize_ * blocksize_) == 0CRDonnxruntime::SpaceToDepth::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccinput_height % this->blocksize_ == 0input_width % this->blocksize_ == 0SpaceToDepthDepthToSpaceinput_shape.NumDimensions() == 4 outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputs Got:static_cast(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoC:\agent\_work\2\s\onnxruntime\core\providers\cpu\controlflow\loop.cc but has Loop inputs. Found:Graph in 'body' attribute of Loop should have onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::Execute was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output onnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Expected:Inconsistent shape in loop output for output. onnxruntime::Loop::Looponnxruntime::Loop::SetupSubgraphExecutionInfoLogSoftmax. N=Hardmax inputs N, D and N * D must be < Hardmax, D=DynamicQuantizeLinearC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeRoundCumSumAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`C:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\cumsum.ccreverseexclusiveextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()starts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initonnxruntime::CumSum::Computeonnxruntime::CumSum<__int64>::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Inittime_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axis. Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of onnxruntime::ReverseSequenceOp::ReverseSequenceOpC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccReverseSequence. batch_size=RoiAlignNull rois_ptrNull input X ptrC:\agent\_work\2\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBaseoutput_widthoutput_heightavgInvalid mode of value specified. It should be either avg or maxFirst dimension (num_rois) of batch_indices and rois don't matchNumber of dimensions for batch indices should be exactly 1Null batch_indices_ptrSecond dimension for rois should be exactly Number of dimensions for rois should be exactly onnxruntime::RoiAlignBase::RoiAlignBaseSampling ratio should be >=0, but it was sampling_ratiospatial_scalesampling_ratio_ >= 0onnxruntime::IsInf::ComputeData type X must be float or double, but instead got C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfFailed to obtain detect_negativedetect_negativeIsInfFailed to obtain detect_positivedetect_positivetype == dtype_Tensor type mismatch.onnxruntime::IdentityOp<0>::Computeonnxruntime::IdentityOp<1>::ComputeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::Tensor::DataRawonnxruntime::Tensor::MutableDataRawT4QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConvonnxruntime::math::Im2colNd::operator ()IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)onnxruntime::QLinearConv::ComputeIsScalarOr1ElementVector(X_zero_point)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1C:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\qlinearconv.ccMust be a scalar or 1D tensor or size 1.ConvIntegeronnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.C:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\conv_integer.ccIsScalarOr1ElementVector(W_Zero_Point)onnxruntime::MatMulComputeHelper::ComputeNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 C:\agent\_work\2\s\onnxruntime\core/providers/cpu/math/matmul_helper.ha != nullptr && b != nullptrMatMulIntegerC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\matmul_integer.cconnxruntime::MatMulInteger::ComputeNot satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: K_ == right_shape[0] Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] MatMul dimension mismatchNot satisfied: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim MatMulInteger: Unsupported input types with zero pointonnxruntime::MatMulInteger::Computet->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1Currently only scalar zero_point is supported. TODO: add per channel zero point support.t->IsDataType() || t->IsDataType()onnxruntime::MatMulInteger::Compute::::operator ()IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1QLinearMatMulIsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1C:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1x_scale must be 1D tensor with size onnxruntime::DequantizeLinear::Computex_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dimQuantizeLinearDequantizeLinearonnxruntime::QuantizeLinear::ComputeIsScalarOr1ElementVector(&y_zero_point)onnxruntime::QuantizeLinear::Computey_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dimIsScalarOr1ElementVector(&y_scale)y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcast_dimx_zero_point must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&x_scale)onnxruntime::DequantizeLinear::ComputeIsScalarOr1ElementVector(&x_zero_point)x_zero_point must be 1D tensor with size C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccx_scale must be a scalar or 1D tensor or size 1.x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcast_dimonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelinput_size < std::numeric_limits::max()Attibute name and type don't matchC:\agent\_work\2\s\onnxruntime\core/providers/cpu/activation/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::ComputeNo attribute with name:''is defined.onnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::InitC:\agent\_work\2\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ScaledTanh::Initunknown kernel typegammaC:\agent\_work\2\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::HardSigmoid::Init*attribute case_change_action is not setLOWERonnxruntime::StringNormalizer::StringNormalizercase_change_actionis_case_sensitiveattribute is_case_sensitive is not setC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleConversion Error:Please, install necessary language-pack-XX and configure localesStringNormalizerConversion ErrorInput dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: wstr != wconv_errorSingle dimension value must be greater than 0p.secondStopword contains invalid utf8 chars!sw.empty()Duplicate stopwords not allowedstopwordsEmpty stopwords not allowedattribute case_change_action has invalid valuelocaleUPPERNONEbad conversiononnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::ComputeWhereC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::Where::Computecondition && X && Yonnxruntime::Where::Computecondition, X, and Y inputs are required!onnxruntime::TensorAllocator::TensorAllocatoronnxruntime::Where,class std::allocator > >::Computeonnxruntime::TensorAllocator,class std::allocator > >::TensorAllocatoronnxruntime::Where<__int64>::Computeonnxruntime::Where::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::ComputeX input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::ComputeTfIdfVectorizer >= min_gram_length >= max_gram_length required: impl_->min_gram_length_ > 0max_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->weighting_criteria_ != kNonemin_gram_length is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexessize_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_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countsmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsstatus.IsOK() && !pool_int64s.empty()-gramspool_int64snon-empty pool_int64s is required if pool_strings not providedpool_strings must not be empty if specified!impl_->pool_strings_.empty()impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings but ngram_indexes size: Got weights of size: weights 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; }) id: Duplicate ngram detected, size: Input shape must have either [C] or [B,C] dimensions with B > 0.Number of items must compose whole (items % ngram_size == 0)p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGramsop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()C:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkbiasShrinkonnxruntime::utils::MLTypeCallDispatcher::InvokeSignNull input ptrIsNaNinvalid stoul argumentstol argument out of rangeinvalid stol argumentstoi argument out of rangeinvalid stoi argumentstod argument out of rangeinvalid stod argumentstof argument out of rangeinvalid stof argumentstoull argument out of rangeinvalid stoull argumentstoll argument out of rangeinvalid stoll argumentstoul argument out of rangeCast op must have 'to' argument of type DataTypeInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast::ComputeCasting from 'float16' to 'string' is not supported yet.onnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast<__int64>::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::ComputeUnexpected 'to' argument value: C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast::Computeonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast,class std::allocator > >::CastAttribute to is not set.onnxruntime::Cast,class std::allocator > >::Computeonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast<__int64>::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::CastFromStringDataINF-INFNaNonnxruntime::CastToStringDatabufferlen > 0onnxruntime::CastFloat16Dataallocator != nullptrC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\eye_like.cconnxruntime::EyeLike::ComputeT1 != nullptrEyeLikeEyeLike : Input tensor dimension is not 2normalize_varianceC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsInput is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()Unsupported value attribute datatype with sizeof=: input_shape.NumDimensions() > 0Must have a valid input shape.t_proto.dims()[0] == 1Must have a single dimension of 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaset_proto.dims_size() == 1Must have a single dimensionUnsupported value attribute datatype: unpack_status.IsOK()Value attribute unpacking failed:TensorProto::DataType_IsValid(t_proto.data_type())C:\agent\_work\2\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoutils::HasDataType(t_proto)C:\agent\_work\2\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.honnxruntime::ConstantOfShapeBase::SetValueonnxruntime::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase::PrepareComputeonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsC:\agent\_work\2\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.C:\agent\_work\2\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueonnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was but has batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::ComputeEither scales or sizes MUST be provided as input.sizes == nullptrOnly one of scales or sizes must be provided as input.scales != nullptrroi_input_idx_ > 0Invalid roi input index.C:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeUpsample: unexpected modeResize: unexpected mode'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Upsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.output_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.sizes != nullptr && sizes->Shape().Size() != 0Upsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensionUpsample: input/output value's dimension mismatchResize: input/output value's dimension mismatchUpsample: input/output value is nullptrResize: input/output value is nullptrv >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: Mismatched data types between input and output Tensors. onnxruntime::Transpose::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\transpose.ccInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\tile.ccTileInput count of Tile OP mismatch, the first one is emptySizeA dimension cannot be less than -1.requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperC:\agent\_work\2\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/tensor/reshape.hshapeAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1Reshape_1At most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betaactivationsinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()forwardbidirectionalRNNonnxruntime::RNN::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\rnn\rnn.ccAffineclipinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNN'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::DeepCpuLstmOp::ValidateInputs}. Actual:Input initial_c must have shape {Input P must have shape {onnxruntime::DeepCpuLstmOp::ComputeImplonnxruntime::rnn::detail::MakeDirectionC:\agent\_work\2\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()onnxruntime::DeepCpuLstmOp::DeepCpuLstmOpC:\agent\_work\2\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.finput_forgetsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 3LSTMLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cc.. rows[lda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointeronnxruntime::DeepCpuGruOp::DeepCpuGruOpC:\agent\_work\2\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hlinear_before_resetonnxruntime::DeepCpuGruOp::ComputeImplinfo.GetAttr("linear_before_reset", &int64_value).IsOK()activation_func_names.size() == static_cast(num_directions_) * 2GRUGRU operator does not support double yetInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccMaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::ComputeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0beta_ > 0.0falpha_ > 0.0fonnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4LRNC:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr("size", &size).IsOK()sizeC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNsize_ % 2 == 1size_ > 0LpNormalizationonnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()C:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/lp_norm.hp_ == 1 || p_ == 2Invalid input data: number of dimensions is less than 3: vs. Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Mismatch between input data and B: size of B != input channel count onnxruntime::InstanceNorm::ComputeInstanceNormalizationop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()C:\agent\_work\2\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccC:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNormInvalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Invalid input scale: dimension != Invalid input B: 0th dimension != Invalid input B: NumDimensions() != Invalid input mean: NumDimensions() != Invalid input B: Invalid input mean: Invalid input mean: 0th dimension != Invalid input var: 0th dimension != Invalid input var: NumDimensions() != onnxruntime::BatchNorm::ComputeInvalid input var: st.IsOK()C:\agent\_work\2\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()lowsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikeonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("mean", &mean_).IsOK()meanC:\agent\_work\2\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("scale", &scale_).IsOK()scaleInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDhighonnxruntime::RandomNormalLike::RandomNormalLikeInvalid data type of num_samples is < 1onnxruntime::RandomNormalComputeFLOAT16 is not supportedonnxruntime::RandomUniformComputeonnxruntime::RandomNormalLike::ComputeCould not infer data type from input tensor with data type C:\agent\_work\2\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computenum_classes is < 1batch_size is < 1onnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2 and the number of values is ) must have the same length. However, the number of key is attribtues in LabelEncoder (name: num_keys == num_valuesThe onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2info.GetAttr("default_string", &default_string_).IsOK()default_stringinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64Input of tensor(string) must have output of tensor(int64)LabelEncoderkeys_floatsInput of tensor(int64) must have output of tensor(string)_Unusedvalues_stringsvalues_floatskeys_stringskeys_int64sdefault_floatinfo.GetAttrs(_key_field_name, keys).IsOK()values_int64sclasslabels_int64sZipMapMust provide classlabels_strings or classlabels_int64s but not both.classlabels_stringsonnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Zipmap does not support empty dim countC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\zipmap.cc] != number of classlabels[Zipmap only supports 1D or 2D input tensorsInput features_per_batch[BRANCH_LEQTreeEnsembleRegressorBRANCH_GTEBRANCH_GTLEAFBRANCH_LTSOFTMAXSOFTMAX_ZEROBRANCH_EQLOGISTICMINAVERAGESUMnodes_treeidsnodes_nodeidsnodes_valuesnodes_truenodeidsnodes_hitratesnodes_featureidsnodes_modesnodes_missing_value_tracks_truebase_valuesaggregate_functionnodes_falsenodeidsn_targetsonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hInput shape needs to be at least a single dimension.n_targets_or_classes > 0target_weightstarget_idspost_transformtarget_treeidstarget_nodeidsonnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon (weights).Unknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeEnsembleCommon::computepredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()predictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionnodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()Node (truenode). is already there. in tree One falsenode is pointing either to itself, either to another tree. (falsenode).-Unable to find node onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresclass_weightsclass_idsX dims is empty.class_treeidsclass_nodeidsTreeEnsembleClassifierpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binarysupport_vectorsrhon_supportsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\svmregressor.cccoefficientsinfo.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMRegressor::SVMRegressorRBFLINEARPOLYinfo.GetAttrs("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonkernel_typekernel_paramsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hSVMRegressorone_classnum_features == feature_count_info.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()onnxruntime::ml::SVMRegressor::ComputeUnexpected mode:proba_.size() == probb_.size()classlabels_intsonnxruntime::ml::SVMClassifier::SVMClassifierC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cccoefficients_.size() > 0onnxruntime::ml::SVMClassifier::Computeinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0Unsupported data type of C:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\ml_common.hprob_aprob_bSVMClassifiervectors_per_classUnexpected value for 'add_second_class' of scores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplace) != (Scale size: (onnxruntime::ml::ScalerOp::ScalerOpC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\scaler.cc) or 1Either both scale and offset can be of feature size (scale_.size() == offset_.size()Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOpScalerEmpty scale in attributes!scale_.empty()offsetUnknown Category and zeros = 0.OneHotEncoderzeroscats_int64stmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/ml_common.hL2Invalid normalize value of MAXL1Unexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got info.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::Normalizeronnxruntime::ml::MakeNormalizenormC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerLinearRegressorinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorinterceptstargetsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=Unsupported input element type of scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplshape_size == out.length()onnxruntime::ml::CastInputToFloatLinearClassifiermulti_class Found:Scores output is incorrect size. Expected:onnxruntime::ml::LinearClassifier::LinearClassifierC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\linearclassifier.ccEmpty input dimensions.Empty value of imputed values.imputed_value_floatsImputeronnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64sMust provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64C:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\imputer.ccInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensions) does not match number of inputdimensions values ().FeatureVectorizerC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hC:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs (Invalid input type:onnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpstring_vocabularyDictVectorizerC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularyonnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs("cats_strings", string_categories).IsOK()CategoryMappernum_entries == int_categories.size()info.GetAttrs("cats_int64s", int_categories).IsOK()C:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/category_mapper.hInput of int64 must have output of string Input of string must have output of int64%f Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64TO_STRINGTO_FLOATSPARSEDENSEonnxruntime::ml::MakeCastInvalid CAST_TO value of cast_toonnxruntime::ml::MakePackInvalid PACK_MAP value of Expected DENSE or SPARSEonnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of C:\agent\_work\2\s\onnxruntime\core\providers\cpu\ml\cast_map.ccmap_formC:\agent\_work\2\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapinfo.GetAttr("cast_to", &attr).IsOK()max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()max_mapinfo.GetAttr("map_form", &attr).IsOK()Invalid input type of value: Expected std::map or std::mapCastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0Unexpected CAST_TO value of 0.fBinarizer is NaNthresholdInput data with index: Invalid argument: X input has empty dimensions.Invalid Y argument: index is out of range: Y[] () >=Invalid Y argument: num_indices = 0ArrayFeatureExtractoronnxruntime::EinsumOp::IsTransposeRequiredinput_rank == permutation.size()The rank of the input must match permutation size for Transposeonnxruntime::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 Inputs have ellipses in them but the provided output subscript does not contain an ellipsis...dim_iter == rankThe broadcasted dimensions of the inputs are incompatibleOutput subscript contains repeated lettersThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Found '.' not part of an ellipsis in the output subscript providedFound a '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains letters not seen in the inputsonnxruntime::EinsumOp::CopyOutputCandidateIntoOpOutoutoutput.SizeInBytes() == candidate.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapeC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\einsum_utils.ccEinsum subscripts string contains too many subscript labels when compared to the rank of the inputFound a '.' not part of an ellipsis in input: Number of subscripts in the input equation does not match number of input tensorsonnxruntime::EinsumComputePreprocessor::Run in the same dimensionThe only subscript labels allowed are lowercase letters (a-z)Found '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsEinsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of left_rank == right_rankRanks of pair-wise operands must be equalright.Shape().Size() == right_shape_override.Size()onnxruntime::EinsumOp::PairwiseOperandProcessEinsum op: Input shapes do not alignleft_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsonnxruntime::EinsumOp::FinalizeOutputcandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputleft.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shapecandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output'. 0 == forward. 1 == reverse.Invalid values in 'validonnxruntime::scan::detail::CreateFeedsFetchesManager' was Number of entries in 'num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesonnxruntime::scan::detail::ReadDirectionsonnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::Initializeonnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::InfoC:\agent\_work\2\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc inputs but Scan was only given onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.Failed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *SoftmaxCPU inputs N, D and N * D must be < Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {Input X must have 3 dimensions only. Actual:Input W must have shape {Input R must have shape {onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameonnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: scaledtanhhardsigmoidelusoftsignaffinereluleakyreluthresholdedreluInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of softplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountC:\agent\_work\2\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInverseonnxruntime::utils::MLTypeCallDispatcher::Invokeepsilon_ >= 0input is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNormC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccLast dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNormLast 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 op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNormC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("beta", &beta_).IsOK()Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale::ScaleC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\element_wise_ops.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeInput is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerBias size () does not match the number of channels (C:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerinfo.GetAttrs("bias", bias_).IsOK()DynamicSliceC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::Crop::Compute) + scale_[0] (Input's width (Crop) + scale_[1] (Input's height () needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder () + rightBorder (Attribute border needs to be specified with four border elements, got border) + bottomBorder ( input dimensions insteadC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::Computeonnxruntime::contrib::BiasGelu::Computeuse_approximationInput shape dimensions mismatch:The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: sqeuclideanC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDisteuclideanmetricCDistonnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()Number of dimensions for crop size should be exactly 1Null crop_size_ptronnxruntime::contrib::CropAndResize::CropAndResizeC:\agent\_work\2\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hbilinear specified. It should be either bilinear or nearestCropAndResizeConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\transpose_matmul.ccinfo.GetAttr("transB", &trans_b_attr_).IsOK()info.GetAttr("transA", &trans_a_attr_).IsOK()onnxruntime::contrib::TransposeMatMul::TransposeMatMulTransposeMatMulonnxruntime::contrib::TransposeMatMul::ComputeC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccA != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeMatMulInteger16MurmurHash3keyspositiveinput_num_bytes % 4 == 0Invalid assumption of output element sizesizeof(uint32_t) == output_element_bytesonnxruntime::contrib::MurmurHash3::ComputeC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccconv_window_sizechar_embedding_sizeembedding_sizeConv kernal size 1 does not match conv_window_size attribute . Char embedding size: conv kernal size 1: conv_window_size attribute: embedding_size attribute: Conv filter size does not match embedding_size attribute. conv filter size: WordConvEmbeddingonnxruntime::contrib::WordConvEmbedding::ComputeC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cc Conv kernal size 2 : Char embedding size does not match conv kernal size 2. char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute.Either one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpseparatorstokenexpattribute mincharnum must have a positive valuemincharnum_ > 0mincharnumattribute mincharnum is not setCan not digest tokenexp: Input string contains invalid utf8 chars: !char_tokenezation_ || mincharnum_ < 2Can not digest separators: !separators.empty()mincharnum is too big for char level tokenezation!tokenexp.empty()separators must not be emptypad_valueattribute pad_value is not setonnxruntime::contrib::Tokenizer::TokenizerC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\tokenizer.ccmarkattribute mark is not setTokenizerInput dimensions are either [C] or [N][C] allowedMatch contains invalid utf8 chars: tensor(string) expected as inputstatic_cast(activation_func_names.size()) == num_directions_ * 3C:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention v weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{}, Got: Attention memory layer weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTM, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, ], while found!Attention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {}, actural: C:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()ExpandDimsaxis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is C:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.input index out of rangeonnxruntime::contrib::EmbedLayerNorm::ComputeC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNormC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::Attention::ComputeInput 2 dimension 0 should have same length as dimension 1 of input 1Input 2 is expected to have 1 dimension, got Input 1 dimension 1 should be 3 times of dimension 0Input 1 dimension 0 should have same length as dimension 2 of input 0Input 1 is expected to have 2 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 0 is expected to have 3 dimensions, got unidirectionalInputs 3 and 0 shall have same length at dimension 0Input 3 is expected to have 1 dimension, got Input 3 (mask_index) is not allowed for unidirectionalSampleOpC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::contrib::ReorderOutput::ReorderOutputinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsampleinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBasepool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcConv::NchwcConvinfo.GetAttr("channels_last", &channels_last_).IsOK()channels_ > 0invalid channel countonnxruntime::contrib::ReorderOutput::ComputeX_rank == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0C:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cconnxruntime::contrib::ReorderInput::ComputeX_shape.NumDimensions() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1scales_.size() == 4onnxruntime::contrib::NchwcUpsample::Computeonnxruntime::contrib::NchwcPoolBase::NchwcPoolNot satisfied: Y->Shape() == sum_shape output and sum shape must matchUnsupported convolution size.(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)onnxruntime::contrib::NchwcConv::Computechannels_ <= X_shape[1]onnxruntime::EinsumOp::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::DiagonalInnermostDimsinput_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsC:\agent\_work\2\s\onnxruntime\core\providers\cpu\math\einsum_auxiliary_ops.cconnxruntime::EinsumOp::TransposeLength of permutation must match the rank of the input to be permutatedinput_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 MatMulinput_shape_1_override[2] == input_shape_2_override[1]Incompatible matrix dimensions for matMulinput_shape_1_override[0] == input_shape_2_override[0]Batch dimension should match for MatMul;Input 1 is expected to have 1 dimensions, got 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 [seqno= row[onnxruntime::contrib::BahdanauAttention::BahdanauAttentionC:\agent\_work\2\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemorynot support normalize yet.!normalize_unimplemented activation: activation_params count mismatchbeta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1Tensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType. is not supported name: id:CudaOrtMemoryInfo: [CudaPinned mem_type: alloc_type:C:\agent\_work\2\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignmentSpecified device is not supported.Argument is not a tensorC:\agent\_work\2\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtValue is not a TensorNot implementedOrtApis::GetTensorTypeAndShapetype != nullptrtype_proto is not of type sequence!type_proto is not of type map!shape.Size() must >=0C:\agent\_work\2\s\onnxruntime\core\framework\tensor.cctensor size overflowtensor failed memory size calculationonnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytesdtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Tensorp_type != nullptronnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())utils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatibleonnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeC:\agent\_work\2\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleproto != nullptrOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::SetC:\agent\_work\2\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::utils::ContainerChecker::ContainerChecker expected to be a registered ONNX typeonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::Setvalue_proto != nullptr(null)onnxruntime::NonTensorTypeBase::ToDataContainertensor type tensor(bfloat16) is not currently registered or supportedInvalid DataTypeImpl TypeProto definitionMLDataType for: thisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->sequence_type())utils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::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,class std::allocator > >::Setonnxruntime::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::Setonnxruntime::data_types_internal::SetSequenceType::Setelem_proto != nullptronnxruntime::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::TensorShape::Slicedimstart <= dimend && dimend <= size()}{ for SizeFromDimension. Tensor has dimensions.dimension <= num_dimsInvalid dimension of C:\agent\_work\2\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimensionInvalid tensor shape slice argument.onnxruntime::TensorShape::SizeFromDimensioninvalid allocatorC:\agent\_work\2\s\onnxruntime\core\framework\utils.cconnxruntime::utils::BatchOrCopyMLValueexec_plan_ptronnxruntime::utils::FindMemoryInfoForValueonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::CalculateStaticCopyInfoForFeedRknpuExecutionProviderNnapiExecutionProviderOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphonnxruntime::utils::ExecuteGraphImplfeed_locations.size() == copy_info.size()onnxruntime::utils::InitializeFeedFetchCopyInfofetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedscopy_info.size() == num_feedsonnxruntime::utils::FinalizeCopyInfoForFetchesonnxruntime::utils::CopyInputsAcrossDevicesNo attribute with this name is defined.C:\agent\_work\2\s\onnxruntime\core\framework\op_kernel_info.cccannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoC:\agent\_work\2\s\onnxruntime\core/framework/node_index_info.hnode_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetonnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundInvalid arg_num of arg_num < arg_counts.size()onnxruntime::OpKernelContext::GetOrCreateOutputMLValueonnxruntime::OpKernelContext::OpKernelContextC:\agent\_work\2\s\onnxruntime\core\framework\op_kernel.ccExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is OpKernel was nullkernel != nullptrC:\agent\_work\2\s\onnxruntime\core\framework\session_state.ccSaveMLValueNameIndexMappingonnxruntime::SessionState::SetGraphCould not find OrtValue with idx 'Device: [ memory_type: type: device_id:onnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::UpdateToBeExecutedNodesSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::AddOutputNameToNodeInfoMappingOnly one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionState for attribute Entry exists in node . Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index: with error: Kernel creation failed for node: Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: ) and node is used by node as there's no execution provider allocated.Could not create kernel for node: Done saving OrtValue mappings.Saving initialized tensors.ort_value_name_idx_map.MaxIdx() > -1 failed.onnxruntime::SaveInitializedTensorsOrtValue indexes should have been populated.onnxruntime::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::SaveInputOutputNamesToNodeMappingFailed to copy tensor to execution provider: onnxruntime::SaveInputOutputNamesToNodeMapping::::operator ()SubgraphGraph is not used by any node. input with name Execution plan was not found in SessionState. CreatePlan must be called first.Invalid allocation info. Provider name = onnxruntime::SessionStateInitializer::CreatePlanC:\agent\_work\2\s\onnxruntime\core\framework\session_state_initializer.cc, Got Internal error. The preallocated buffer is too small. Requires string tensor is not supported for copying between allocatorsonnxruntime::DeserializeTensorProtoDone saving initialized tensorsDeserialize tensor The node is not placed on any Execution Provider (node in KernelRegistryManagerfound duplicated provider The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for Failed to find kernel for UnpackTensor: the pre-allocated size does not match the raw data size, expected onnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of in 'Constant' node 'Unsupported attribute value type of The buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedonnxruntime::utils::TensorProtoToMLValuetensor can't contain negative dimsonnxruntime::utils::ConstantNodeProtoToTensorProtoInitialized tensor with unexpected type: corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in proto) in proto) does not match the data size(C:\agent\_work\2\s\onnxruntime\core\framework\tensorprotoutils.ccstring tensor can not have raw datadata overflowonnxruntime::utils::GetFileContentC:\agent\_work\2\s\onnxruntime\core/framework/endian_utils.hNot satisfied: source_bytes.size_bytes() == destination.size_bytes() rank == dims.size() && rank > 0onnxruntime::utils::CopySparseData`anonymous-namespace'::UnpackTensorWithRawDataInvalid TensorProtosource and destination buffer size mismatchonnxruntime::utils::ReadLittleEndiancur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:C:\agent\_work\2\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypenullptr != type_protoC:\agent\_work\2\s\onnxruntime\core\framework\execution_provider.cconnxruntime::IExecutionProvider::InsertAllocatorduplicated allocatorError mapping output names: Error mapping feeds: C:\agent\_work\2\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownProvider doesn't return correct number of compiled functionsonnxruntime::GraphPartitioner::PartitionNo provider specified.node_compute_funcs.size() == nodes_need_compile.size()C:\agent\_work\2\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernelC:\agent\_work\2\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PlaceNode1 == capability->nodes.size()create_func(&context, &func_state_) == 0C:\agent\_work\2\s\onnxruntime\core\framework\data_transfer_manager.ccdata_transfer registered is nullptr.onnxruntime::DataTransferManager::CopyTensorsThere's no data transfer registered for copying tensors from to Tensor size mismatchUnloading DSO Caught exception while destructing CustomOpsLoader with message: Failed to unload DSO: C:\agent\_work\2\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::~ExLibLoaderCaught exception while loading custom ops with message: onnxruntime::ExLibLoader::LoadExternalLibA dso with name has already been loaded.C:\agent\_work\2\s\onnxruntime\core/framework/execution_frame.honnxruntime::NodeIndexInfo::GetMLValueIndexonnxruntime::IExecutionFrame::GetMLValueort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_offset >= 0 && static_cast(offset) < node_values_size_Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model., block not found in target location. fall back to default allocation behaviorFor ort_value with index: ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::AllocateSparseTensormlvalue.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferort_value.Fence() == nullptrTensor shape cannot contain any negative valueTrying to allocate memory for unused optional inputs/outputs, block in memory pattern size is: but the actually size is: , fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()invalid index onnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(mem_patterns_->locations[i]) == buffers_.end()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of C:\agent\_work\2\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:onnxruntime::ExecutionFrame::TraceFreeonnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx= size=Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=Invalid allocation kind: Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan failed: onnxruntime::ExecutionFrame::GetAllocationPlanort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImpl failedlocationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infomodel format error!checksumlengthparsing model format error! Missing 'location'C:\agent\_work\2\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::KernelRegistry::TryCreateKernelFailed to add kernel for : Conflicting with a registered kernel with op versions.Kernel not found kernel is not supported in Encountered following errors: ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (kernel def can't be NULL but the node in the model has the following type (),Found kernel for Op with name ( kernel start version: kernel_end_version: Op with name ( and type ( Version mismatch. node_version: onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()onnxruntime::IDataTransfer::CopyTensorsonnxruntime::CPUDataTransfer::CopyTensorsrc.SizeInBytes() == dst.SizeInBytes()C:\agent\_work\2\s\onnxruntime\core\framework\data_transfer.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()C:\agent\_work\2\s\onnxruntime\core\framework\node_index_info.cconnxruntime::OrtValueTensorSlicer::Creategsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated(). Shape:Insufficient dimensions to slice on Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer::CreateC:\agent\_work\2\s\onnxruntime\core\framework\ort_value_tensor_slicer.cc' Status Message: node. Name:'Non-zero status code returned while running Exiting due to terminate flag being set to true.Got nullptr from GetKernel for node: op_name_fence_beforeParallelExecutor::ExecuteBegin executiononnxruntime::ParallelExecutor::RunNodeAsyncMultiple errors were found.onnxruntime::ParallelExecutor::ExecuteC:\agent\_work\2\s\onnxruntime\core\framework\parallel_executor.ccAll implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalC:\agent\_work\2\s\onnxruntime\core/framework/op_kernel_context_internal.h does not.'. Exception running nodes starting at provider_kernel_time_fence_afterUnknown exception was caught by catch-all handler.exec_plan_indexactivation_sizeparameter_sizeoutput_sizeonnxruntime::SequentialExecutor::ExecuteC:\agent\_work\2\s\onnxruntime\core\framework\sequential_executor.ccgraph_indexonnxruntime::ReleaseNodeMLValuesSequentialExecutor::Execute not found.onnxruntime::FuncManager::GetFuncsC:\agent\_work\2\s\onnxruntime\core\framework\fuse_nodes_funcs.cc already exist.func info for node: Can't use func with null ptr' failedGet preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::Trace is not foundMem pattern for initializer ' is not foundWeight buffer for initializer 'duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanC:\agent\_work\2\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error.Failed to get allocator for location: onnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()C:\agent\_work\2\s\onnxruntime\core\framework\allocation_planner.cc[) : onnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::CreatePlanNo suitable kernel definition found for op Can not find the execution provider onnxruntime::PlannerImpl::GetLocationForNodeInputkernel_create_info != nullptr && kernel_create_info->kernel_def != nullptrnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeCan not find the node onnxruntime::PlannerImpl::ComputeUseCountsid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reuseonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)h < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b) bins of max chunk size C:\agent\_work\2\s\onnxruntime\core\framework\bfc_arena.ccBinForSize(bin_size) == BinFromIndex(b)Creating onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::BFCArenaCreating BFCArena for p_int >= base_intC:\agent\_work\2\s\onnxruntime\core/framework/bfc_arena.hp_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()!chunk->in_use()!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtrExtending BFCArena for . bin_num:tried to allocate 0 bytes rounded_bytes:onnxruntime::BFCArena::AllocateRawInternalonnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()h != kInvalidChunkHandleAllocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendFailed to allocate memory for requested buffer of size Extended allocation by bytes.cudaMalloconnxruntime::BFCArena::ChunkFromHandleonnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.. onnxruntime::BFCArena::FreeAndMaybeCoalesceCould not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinc->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)Failed to get allocator for initializer '', location: invalid ort_value_index:C:\agent\_work\2\s\onnxruntime\core\framework\simple_tensor_allocator.cconnxruntime::SimpleTensorAllocator::GetPreallocatedBuffer[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Dimension=Can't merge shape info. Both source and target dimension have values but they differ. Source= Target=Tensor element type mismatch. Type mismatch. Current=SparseTensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapeC:\agent\_work\2\s\onnxruntime\core\graph\graph.cconnxruntime::MergeShapeInfo Input=Output: target:. Falling back to lenient merge.Error merging shape info for output. '' source:ConstantNoOpArgument mismatch when removing edge.Invalid destination node arg slot specified when removing edge. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::BuildConnectionsInvalid node indexes specified when removing edge.Argument type mismatch when adding edge.Invalid source node arg slot specified when removing edge.onnxruntime::Graph::RemoveEdgeonnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Graph output (onnxruntime::Graph::SetOuterScopeNodeArgsThis is an invalid model. Error: Duplicate definition of name (Graph ctor should have created NodeArg for initializer.onnxruntime::Graph::InitializeStateFromModelFileGraphProto) does not exist in the graph.node_argInitializer appears in graph inputs and will not be treated as constant value/weight. graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This may fail some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, graph_proto cannot be nullThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graphgraph_proto != nullptr) type inference failedNode () does not match expected type () output arg () bound to different types ( in node () Op (Type Error: Type parameter () of operator () in node (This is an invalid model. Type Error: Type '' of input parameter () input arg () does not have type information set by parent node.) is invalid.This is an invalid model. Node (UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesonnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()Size mismatch validating subgraph inputs. Got inputs but subgraph has Subgraph input missing type.Node:No Graph instance was found for attribute in node inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.!foundgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::RemoveInitializedTensorAddInitializedTensor already has tensor with name but different TensorProto.onnxruntime::Graph::AddInitializedTensorexisting->second == &tensoronnxruntime::Graph::Resolveonnxruntime::Graph::ForThisAndAllSubgraphsouter_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.Error: Duplicate definition-site for (onnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::VerifyNodeAndOpMatchFatal error: ) attribute () is required but not specified.Type Error: Shape of initializer does not match. is not a registered function/opThis is an invalid model. Error in Node: but usage of initializer in graph expects This is an invalid model. Model input (Type Error: Data in initializer '' has element type ) of output arg () of node () does not have type information.Type Error: Type (onnxruntime::Graph::InlineFunctionnode.MutableOutputDefs().size() == subgraph.GetOutputs().size()input->Exists()Input to set must exist.onnxruntime::Graph::FuseSubGraph's number of outputs is different from function body graph's number of outputs.'s number of inputs is different from function body graph's number of input.nodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.nullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::AllocateNodeRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node.onnxruntime::Graph::CleanUnusedInitializers'was added but does not exist. onnxruntime::Graph::RemoveNodeonnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name ' as it still has output edges._token_node->GetOutputEdgesCount() == 0Can't remove node C:\agent\_work\2\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::ConstPointerContainer > >::atindex < data_.size()onnxruntime::Graph::SetInputsthan the operator set version known by the checker., but it its version is higheronnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalC:\agent\_work\2\s\onnxruntime\core\graph\schema_registry.cc is less than 0. less than 0.Protobuf serialization failed.onnxruntime::Model::Saveonnxruntime::Model::LoadFailed to load model with error: Protobuf parsing failed.No graph was found in the protobuf.ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.C:\agent\_work\2\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelModelProto does not have a graph.Unknown model file format version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.system error number Load model failed. File doesn't existonnxruntime::SaveModelC should specify a shapeIncompatible dimensionsNegative values are not allowed in a shape specificationAttribute Scale and Zero-point must be a scalarinputs are expected to have tensor type.Pads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Input data type does not match the expected data type. Current data type is Input data type does not match the expected data typeWhether every token can only attend to previous tokens. Default value is 0.weightword_embedding should have 2 dimensions and dimension size is known.Number of attention headsC:\agent\_work\2\s\onnxruntime\core\graph\contrib_ops\contrib_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.Inputs 0 shall be 2 dimensionsC_zero_pointPadding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Constrain input and output types to 8 bit signed and unsigned tensors.Output scale. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_scaleSecond operand.Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_scaleA_scaleA_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.{name} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} First operand.{additionalDocumentation}segment_embeddingword_embeddingposition_embeddinginput_idssegment_idsweight_zero_pointThe epsilon value to use to avoid division by zero.Constrain input and output types to int8 tensors.weight_scaleinput_zero_pointinput_scaleConstrain input and output types to float tensors.QAttentionConstrain mask index to integer typesMmask_index 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. 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D 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], ] extra_shapeinput_as_shape 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. 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.Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format.Saved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationConstrain mean and inv_std_var to float tensors.inv_std_varUskipinput tensorConstrain input and output types to float or half tensors.output tensormaskConstrain input and output float tensors types.Constrain input and output integer tensors typesThe inner-most 2 dimensions must have the same size (mat_w: != mat_h:Value of betaValue of alphaGaussian 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.crop_size shape input tensor has wrong dimensionInput 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. first input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64batch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionaxis must be in [-rank, rank-1]. input rank was Incompatible dimensions for matrix multiplicationlast dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.inputs are expected to have tensor type and output type should not be null. 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 tensors of wrong rank (0). Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html Input axis is invalid: Second input does not have rank 2 The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. The dequantization formula is y = (x - x_zero_point) * x_scale. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').) + scale[0] () needs to be greater than or equal to the left_border (First input does not have rank 2) + scale[1] () + bottom_border ('Scale' must contain exactly 2 values - (height, width)) + right_border () needs to be greater than or equal to the top_border (The previous GRU hidden state.drop_statesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevOutput data after scalingInput data to be scaledBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitThe shape of filled tensorGivenTensorFillvaluesThe filled tensorSliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.Constrain indices to integer typesConstrain input and output types to all tensor types.dataTensor of data to extract slices from.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`Threshold valueResult, has same type as input, with H and W dimensions reduced.Output tensorInput tensorResult, has same shape and type as inputInput tensor of shape [N,C,H,W]A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).1D output tensor1D input tensorThe scale to apply.Bias applied to each channel, same size as C.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.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Constrain input and output types to float tensorsPads 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.input_1input_0WComplexMulConjonesidednormalizedComplexMulIrfftConstrain input0 and output types to float tensorsFor internal use.signal_ndimRfftScaling valueIf 0, normalize the mean only. Default is 1. Sample echo operator.The scaled hyperbolic tangent values of the input tensor computed element-wisehiddenThe new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.Array of sequence lengths. len(seq_lengths) should equal batch size N.gatesThe timestep for this operation.seq_lengthsBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Input/Output is a string tensorAn optional string. Token's regular expression in basic POSIX format (http://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.Constrain 'y', 'x_scale' to float tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Tokenized stringsStrings to tokenizeZero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.x_scaleN-D full precision output tensor. It has same shape as input 'x'.x_zero_pointConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized Input tensor to be de-quantized.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.y_scaleN-D quantized output tensor. It has same shape as input 'x'.y_zero_pointN-D full precision Input tensor to be quantized.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.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.xactivation_gammaScalar multiplier for input tensor C.ExpandDims echo operator.Specified axis to insert a dimensionWhether A should be transposedConstrain input and output types to float/int tensors.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedInput scale. It's a scalar, which means a per-tensor/layer quantization. Computes the mean of the low-precision input tensor's element along the provided axes. 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 above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True. Input and Output scales and zero points are used to requantize the output in a new range. This helps to improve accuracy as after ReduceMean operation the range of the output is expected to decrease. ``` "Output = Dequantize(Input) -> ReduceMean on fp32 data -> Quantize(output)", ``` Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_scaleC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointQLinearMulQLinearReduceMeanmultiplicationQLinearAddKeep the reduced dimension or not, default 1 mean keep reduced dimension.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointConstrain input type to 8-bit integer tensor.reducedA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8). Computes the sum of the low-precision input tensor's element along the provided axes. 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 above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.ReduceSumIntegerReduced output tensor.An input tensor.Whether A should be transposed on the last two dimensions before doing multiplicationConstrain 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).Matrix multiply resultsWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply results from A * BN-dimensional matrix BConstrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMinimum 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.N-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.Constrain indice type to int32 or int64Constrain input and output types to any tensor type.Integer representing the embedding vector size for each word.If not provide, use the 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]]] Tensor of rank q >= 1.Tensor of rank r >= 1.Tensor of rank q-1+r-indices[-1].indicesConstrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.Constrain input and output types to 8 bit tensors.32-bit hash value.An input tensor to hash.Whether to use ceil or floor (default) to compute the output shape.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedInput data tensor from the previous operator; dimensions for image case are (N x C x H x 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. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].QLinearAveragePool QLinearAveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Input and output scales and zero points are used to convert the output to a new quantization range. Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output) 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.Performs element-wise binary quantized multiplication (with Numpy-style broadcasting support). "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**" The output of this op is the int32 accumulated result of the mul operation ``` C (int32) = (A - A_zero_point) * (B - B_zero_point) ``` MulIntegerConstrain output types to 32 bit tensors.Constrain output to 32 bit tensorreduced_scaledata_zero_pointConstrain input types to 8 bit signed and unsigned tensors.reduced_zero_pointThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.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. 2D matrix with shape (M,N)The 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".A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' 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.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D input tensor that is to be processed.idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'(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`. 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.The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).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 arraySpecify bias of convSpecify weights of convConstrain to tensor(int32).Specify embedding vector of charInteger 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.SequenceSpecify batchs of sequence words to embeddingInput tensor. Every matrix in the batch must be invertible.The bias input data that is a 1D tensor.Output tensor of the same type and shape as the input tensor.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.The normal input data.The output.Bias tensor.Scale tensor.Constrain input and output types (except mean and inv_std_var) to float tensors.Output data tensor. 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 types to int tensors.Input data tensor from the previous layer.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).crop_size1-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.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].roisRoIs (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.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.̼+̼+onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Invalid input index for node . Index:Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputonnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing) ExplicitInputs: ImplicitInputs:Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameInitializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeonnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()index >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputAttempting to get an input that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput cannot be safely updated to in one of the subgraphs.onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0C:\agent\_work\2\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphinA node with a function body within a subgraph within another function body is currently not supported in ORToutResolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplC:\agent\_work\2\s\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyinput_arg->Type() != nullptrfused_function_subgraphstartConstrain input and output types.Can not get shape initializer data!Unsupported type:Unsupported non-raw-data data type!deltalimitinvalid scales valueC:\agent\_work\2\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cctensor rank too smallinvalid scales dimensioninitial_hsequence_lensRSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain seq_lens to integral tensors.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.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.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.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.Y_cY_hAWmemory_seq_lensMWQWPinitial_c Msg:[%ws] (caller: %p) %hs(%d) tid(%x) %08X %ws%hs(%u)\%hs!%p: %hs!%p: LogHrFailFastExceptionReturnHr fail: unexpected endMapFileIntoMemory is not implemented on Windows.SetFilePointerEx ReadFile Not satisfied: length <= buffer.size() open file Not satisfied: file_path Not satisfied: offset >= 0 onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBuffer fail, errcode = GetFileSizeEx onnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformationonnxruntimeFatal error: 0 count processors from GetSystemInfoSetThreadDescriptionkernel32.dllC:\agent\_work\2\s\onnxruntime\core\platform\windows\env.cc%wskernelbase.dllRaiseFailFastException[%hs] CallContext:[%hs] [%hs(%hs)] \*GetSystemTimePreciseAsFileTimeonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNamekernel32.dllFailed to unload libraryFailed to find symbol in libraryUNC\Failed to load libraryonnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathNot satisfied: result_length > 0 RemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: DeleteFile() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolder\/, error code: Default logger already set. default_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.ISink must be provided.onnxruntime is not a valid date [ is not a valid day is not a valid yearampmfn != nullptronnxruntime::concurrency::ThreadPool::Schedulenum_threads >= 1onnxruntime::concurrency::ThreadPool::ThreadPoolC:\agent\_work\2\s\onnxruntime\core\common\threadpool.cclength overflowSystemError[ONNXRuntimeError]onnxruntime::common::Status::StatusC:\agent\_work\2\s\onnxruntime\core\common\status.cccode != static_cast(common::OK)GENERAL ERRORINVALID_GRAPHEP_FAILMODEL_LOADEDNOT_IMPLEMENTEDRUNTIME_EXCEPTIONINVALID_PROTOBUFNO_MODELENGINE_ERRORFAILNO_SUCHFILESUCCESSsession_logger != nullptronnxruntime::profiling::Profiler::Initializeonnxruntime::profiling::Profiler::StartTimeC:\agent\_work\2\s\onnxruntime\core\common\profiler.ccenabled_SessionNode] } }, "" : ""name" :""args" : {"ts" :"ph" : "X","tid" :"dur" :{"cat" : ""pid" :[ ",Writing profiler data to file onnxruntime::profiling::Profiler::EndProfilingMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventHHonnxruntime::Path::ParseC:\agent\_work\2\s\onnxruntime\core\common\path.ccNot satisfied: has_trailing_separator Failed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRoot/\unexpected failureillegal input path:?@@@rB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KAQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;    ?? not in range [min=, max=Operator '' has been deprecated since version : failed validating the check: (inputs_.size() - 1) == iDuplicate type constraint nameRequired attribute '' is missing. has unknown expected type' is expected to have field 'graphs'' is expected to have field 'tensors'' is expected to have field 'strings'' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'g'' is expected to have field 'sparse_tensor'' is expected to have field 't'Mismatched attribute type in 'Unrecognized attribute: for operator Attribute '' appeared multiple times.)'s output ) has more outputs ()'s input is marked single but has an empty string in the graph) has more inputs () than declared () in op definition.has output size not in allowed output sizes.) has output size not in allowed input sizes.) has input size !(it.GetName().empty())(outputs_.size() - 1) == iONNX Schema 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.C:\agent\_work\2\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 size of each input in the input listThe input type must be a tensor of a numeric type.An integer vocabulary array.
One and only one of the vocabularies must be defined.A string vocabulary array.
One and only one of the vocabularies must be defined.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.map(string, double)map(string, float)map(int64, double)map(string, int64)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe output is a tensor of strings or integers. Its shape will be the same as the input shape.The input must be a tensor of strings or integers, either [N,C] or [C].If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.The output is a 1-D tensor of string, float, or integer.map(int64, float)map(int64, string)The input must be an integer map to either string or float.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]Input's shape should be 1D or 2DA float.An integer.A string.A list of strings. One and only one of 'value_*'s should be set.A list of floats.A list of ints.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.Only one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setInput type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.Label encoder has only one output.Label encoder has only one input.Value(s) to change to.A value that needs replacing.Value(s) to change toThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.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.One of 'MAX,' 'L1,' 'L2'The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.Weights of the model(s).Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.A collection of weights of the model(s).The output will be a tensor of strings or integers.The id of the tree that this node is in.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.Child node if expression is false.Child node if expression is true.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The number of support vectors.Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The input must be a tensor of a numeric type, either [C] or [N,C].The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))seq(map(string, float))The output will be a sequence of string or integer maps to float.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The weight for each targetThe index of the target that each weight is forThe node id of each weightThe id of the tree that each node is in.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Child node if expression is falseChild node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.The index of the class list that each weight is for.node id that this weight is for.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.Constrain input and output types to all numeric tensors.Constrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\math\defs.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).'axis' must be in []. Its actual value is: Constrain input and output types to high-precision numeric tensors.Value of beta.Value of alpha.slopeConstrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. CeluConstrain input and output types to float32 tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Elu_ResultX_alphaCoefficient of ELU.Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of leakage.ValuesKAxis has less than the requested k elements.K input must be of type int64.K input must be a one-dimensional tensor of size 1.Invalid value for attribute axissumConstrain input and output types to numeric tensors.data_0List of tensors for Constrain input and output types to all tensors.'shape' input must be 1D tensor of type INT64Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain index tensor to int64IndicesIf set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Constrain output Y data type as 32-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.b_zero_pointb_scaleba_zero_pointa_scaleainput and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.squeeze_maskinput_gather_element_transformconst_zero_floattransform_targetsexpanded_target_int64const_zero_target_typedconst_ignore_indexweight_gather_sumloss_sumloss_unweightedweight_gatherloss_Nddlossreductionnoneloss_N1ddloss_NCddinput_gather_elementignore_indexexpanded_targettargetconst_oneconst_zeroConstrain input and output types to floating-point tensors.axis tensor can be int32 or int64 onlyIf set to 1 will perform the sums in reverse direction.X_RSX_ExpX_SubX_MaxscoresType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.OutputInputsEinsum expression string.Rank of input does not match the equation indices.Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.NegativeLogLikelihoodLossConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Weight rank must be 1.Input and target dimension value mismatch.Target rank must be 1 less than the input rank.weight_gather_temp_1weight_gather_tempconst_one_floatSoftmaxCrossEntropyLosslabelslog_probX_LogX_DivDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.Constrains input/output to boolean tensors.Constrains input types to all numeric tensors.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains output to boolean tensor.Constrains input to boolean tensor.GreaterOrEqualLessOrEqualValue of attribute not specifiedAttribute expected to have a one-dim tensorAttribute expected to have tensor typevalue_floatsvalue_floatvalue_intsvalue_intsparse_value does not specify a valid type. should be of integer type and specify a type.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.Input tensor must be 2-dimensionalConstrain 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 float32Invalid shape value: Shape input must be a one-dimensional tensor.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, int64, output tensor.The value for the elements of the output tensor in sparse format.The value for the elements of the output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.Attribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.Attribute 'value_float' expect a float.Attribute 'value_ints' expect a list of integers.Attribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_stringsvalue_stringloop_body_attributeConstrain output types to integral tensors.Constrain input types to float tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.The mean of the normal distribution.Constrain output types to float tensors.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.(Optional) Seed to the random generator, if not specified we will auto generate one.Upper boundary of the output values.Lower boundary of the output values.Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.Constrain input types to common numeric type tensors.All inputs to 'Range' op must be of the same typevariadic_outputceil_result_relu_boolceil_result_relu_intceil_result_reluceil_resultdiv_resultdelta_castedsub_result_castedsub_resultrangecurrentcond_outprevcondiInput to 'Range' op should be scalars (Tensor with only one element and shape empty)Attribute kernel_shape has incorrect size.Attribute strides has incorrect size.Attribute pads has incorrect size.Input tensor X must have atleast 2 dimensions.MaxUnpool op must have either two or three inputs.Dilation 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.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\nn\defs.ccConstrain input and output types to float and 8 bit tensors.Second input tensor has wrong dimensionAttribute pads has incorrect sizeAttribute kernel_shape must be specifiedAttribute kernel_shape has incorrect sizeAttribute strides has incorrect sizeAttribute dilations has incorrect sizeInput tensor must have atleast 2 dimensionsThis 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. weight and zero_point pair is expected to have same type.number of groups input channels and output channels are divided into.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input W.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).Attribute pooled_shape must be specifiedAttribute pooled_shape has incorrect lengthRoIs tensor must have 2 dimensionsInput tensor must have at least 2 dimensionsp value of the Lp norm used to pool over the input data.'output_shape' must have same number of elements as the shape of input tensor X.'output_shape' must be rank 1 tensor.Attribute kernel_shape must be specified. 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. Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsStride along each spatial axis. If not present, the stride defaults to 1 along each axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain output y data type to 32-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.number of groups input channels and output channels are divided into. default is 1.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain bias type to 32-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.w_zero_pointw_scalewtraining_moderatiotraining_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The axis on which to apply normalization, -1 mean last axis.saved_varsaved_meanvarmomentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.1-D tensor of floatsInput is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.The exponent.Scaling parameter.The number of channels to sum overIndicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain input and output to all tensor types.Invalid value() for attribute 'axis'The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.Processed_STDX_varianceSTDVarianceE_XsquaredX_squaredEX_squaredX_RMEpsilonA 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.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.List of stop words. If not set, no word would be removed from X.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falsestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.reshapedDimension could not be inferred: incompatible shapesInvalid Target shape product of 0Invalid dimension value: Invalid position of 0Target shape may not have multiple -1 dimensionsC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output types. Casting to complex is not supported.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 TensorProtoOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepslength of each output. Values should be >= 0.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).outputsThe input is not evenly splittableMismatch between the sum of 'split' () and the split dimension of the input (Mismatch between number of splits () and outputs (Invalid value of attribute 'axis'. Rank= Value=Constrain output types to any tensor type.concat_resultinputsWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..All inputs to Concat must have same rankaxis must be in [-rank, rank-1].Required attribute axis is missingConstrain output to int64 tensor, which should be a scalar though.Constrain output to int64 tensor.Input tensor can be of arbitrary type.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]data tensor must have rank >= 1Input and output types can be of any tensor type.updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.}, input shape = {Invalid attribute perm {steps'step' cannot be 0Input axes has invalid dataInput steps has incorrect lengthInput axes has incorrect lengthIncorrect or missing input value for starts and endsSlice op must have either three, four or five inputs.Constrain repeat's type to int64 tensors.repeats'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Blocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalBlocksize must be positiveexpandedList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).values in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatessqueezedList 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).Input 'depth' must have exactly one element.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.Constrains to boolean tensors.condition(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).'axis' must be in [-rank(indices), rank(indices)-1]Indices tensor must have rank >= 1Constrain roi type to float or double.sizesroiWhen coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). 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).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)(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'sequence_lens' must have rank of 1'input' must have rank >= 2Constrain to all tensor types.Constrain 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) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.Constrain output types to boolean tensors.Constrain to any tensor type.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 have exactly two elements.Input 'values' must be rank 1 tensor.Constrains input and output to only numeric types.constant_valueSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.inverse_indices(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).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).C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\reduction\defs.ccConstrain input and output types to high-precision and 8 bit numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). but has rank expected to have rank Dimension mismatch in unification between expected to have rank >Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccboxesmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indicesInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.Input type was nullInput was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input element type of Output was expected to have tensor type. Got is invalid for a tensor of rank v_initialv_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.initial_state_and_scan_inputsfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Int64 tensor axis value ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified ( was not a tensor.Scan input outputs. Expected Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. but subgraphs produce If node has else= then=Mismatched type for output Mismatched tensor element type for output was Loop 'body' subgraph outputs should all be tensors but output Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor typesOnly boolC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccSequenceConstruct is expected to have at least 1 input.Element type of inputs are expected to be the same.output_sequenceConstrain input types to any tensor type. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=input_sequencetensorpositionConstrain position to integral tensor. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))Attribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\sequence\defs.ccConstrain input types to all tensor types.Constrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Stride along each spatial axis.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\nn\old.ccDilation value along each spatial axis of filter.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.legacy optimization attribute.consumed_inputs(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.The ratio of random dropoutConstrain output mask types to boolean tensors.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scaleTwo interpolation modes: nearest(default), bilinearConstrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be insertedConstrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\tensor\old.ccWhich axis to concat on. Default value is 1.rank must be greater than axisWhich axis to concat onoutputs...Which axis to split onlength of each outputList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.paddingsThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapetilesConstrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Invalid value of attribute 'axis'Which axis to split on. Attribute value for pads is requiredAttribute 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`.One float, indicates the value to be filled.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\math\old.ccWhether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Invalid value for attribute kNumber of top elements to retrieveDimension on which to do the sort. 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 broadcastingbroadcastIf set, defines the broadcast dimensions. See doc for details.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Value of alpha default to 0.2Value of beta default to 0.5Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinQ_MaxX_MinX_Min_AdjustedX_Max_AdjustedX_RangeMin_ScaledInitial_ZeroPoint_FPClipped_ZeroPoint_FPRounded_ZeroPoint_FPZeropointC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\object_detection\old.ccC:\agent\_work\2\s\cmake\external\onnx\onnx\defs\controlflow\old.ccAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.First input tensor must have rank 3Number of neurons in the hidden layerConstrain seq_lens to integer tensor.One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\rnn\defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.Couple the input and forget gates if 1.Enable broadcastingIf set, defines the broadcast dimensions.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\logical\old.ccConstrains input to float tensors.Constrains input to integral tensors.fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.C:\agent\_work\2\s\cmake\external\onnx\onnx\defs\rnn\old.cctensor(seq(map(sparse_tensor(seqmapopaquesparse_tensorfloatfloat16doubleint16int32int64uint8uint16uint32uint64complex64complex128 ==> Context: is required to be non-empty.' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field.Attribute (name: ) should refer to attribute in parent node.nodeop_type) has zero input and zero output., type: value_info' of nameField ' is required but missing.typeelem_typekey_typevalue_type): Unrecognized type value case (value_info name: data_type) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_data) is stored externally and should not have data field.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.) should be stored in ) is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!TensorProto (tensor name: ) should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: NodeProto (name: ConstantFillas possible.reject this operator. Please update your model as soon experimental ops. In the future, we may directly was a removed Warning: No opset import for domain ' with domain_version of No Op registered for is deprecated in domain_version of Op registered for graph' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph input is not output of any previous nodes.' of node: Nodes in a graph must be topologically sorted, however input 'Bad node spec: ' has been used as output names multiple times. inferred=Inferred shape and existing shape differ in dimension type case unsupported. existing=type case mismatch. existing=no nametype mismatch. existing=(op_type:, name: inputs but were provided is tensor type, but provided type is Graph has Graph input # is out of boundsoutput input does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.sequence_typetensor_typeopaque_typeNOT_SETsparse_tensor_typeInferred elem type differs from existing elem type: () vs (: (Inferred shape and existing shape differ in rank: (Dimension value inferred () is not equal to the existing dim value (Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'onnx.ValueInfoProtoonnx.AttributeProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.StringStringEntryProtoonnx.ModelProtoonnx.GraphProtoonnx.TensorAnnotationonnx.TensorProtoonnx.TensorProto.Segmentonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.OperatorSetIdProtoonnx.TypeProtoonnx.FunctionProtoUnknown encoding ': C:\agent\_work\2\s\cmake\external\re2\re2\re2.ccError compiling 'Error parsing 'Error reverse compiling 'pattern too large - compile failedpattern too large - reverse compile failedtext size: Invalid RE2: startpos: endpos: list count RE2: invalid startpos, endpos pair. [DFA out of memory: size bytemap range SearchOnePass inconsistencySearchDFA inconsistencySearchNFA inconsistencySearchBitState inconsistency0IIhIIIIЄIII@I(IhIXIHINumCapturesWalker::ShortVisit calledinvalid named capture groupC:\agent\_work\2\s\cmake\external\re2\re2/walker-inl.hStack not empty.Walk NULLRegexp not destroyed.Bad reference count C:\agent\_work\2\s\cmake\external\re2\re2\regexp.ccno errorUnexpected op in Regexp::Equal: invalid escape sequenceunexpected errorinvalid character class rangeinvalid character classmissing )missing ]no argument for repetition operatortrailing \bad repetition operatorinvalid repetition sizeinvalid UTF-8invalid perl operatorC:\agent\_work\2\s\cmake\external\re2\re2\compile.ccshould never happenNo ranges in char classCompiler::Copy called!Missing case in Compiler: AddFoldedRange recurses too much.RepetitionWalker::ShortVisit calledC:\agent\_work\2\s\cmake\external\re2\re2\parse.ccunknown round: Concat of Bad hex digit RE2: unexpected op: AnyBad call to ParseState::ParsePerlFlagsunhandled in AddToThreadqC:\agent\_work\2\s\cmake\external\re2\re2\nfa.ccUnhandled in stepcontext does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte|unhandled opcode: C:\agent\_work\2\s\cmake\external\re2\re2\dfa.ccNULL state in RunStateOnByteDeadState in RunStateOnByteStateSaver failed to restore state.Unexpected special state in RunStateOnByteRunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetFailed to analyze start state.njob_ = job_.size() = C:\agent\_work\2\s\cmake\external\re2\re2\bitstate.ccGrowStack() failed: Unexpected opcode: arg Bad arg in kInstCapture: Bad arg in kInstAltMatch: Cannot use SearchOnePass for unanchored matches.C:\agent\_work\2\s\cmake\external\re2\re2\onepass.ccC:\agent\_work\2\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: C:\agent\_work\2\s\cmake\external\re2\re2\simplify.ccCoalesceWalker::ShortVisit calledCase not handled in ComputeSimple: DoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is Simplify case not handled: SimplifyWalker::ShortVisit calledMalformed repeat ؒIIȒIIIIIIIIII8II(IIXIДIHIДIxIIhIIIIIIIIIIؓIIȓIIIIIIIIII8II(IIXIIHIIxIIhIII II IIؔIIؔIĒIIII\D\d\S\s\W\w[:^alnum:][:alnum:][:^alpha:][:alpha:][:^ascii:][:ascii:][:^blank:][:blank:][:^cntrl:][:cntrl:][:^digit:][:digit:][:^graph:][:graph:][:^lower:][:lower:][:^print:][:print:][:^punct:][:punct:][:^space:][:space:][:^upper:][:upper:][:^word:][:word:][:^xdigit:][:xdigit:]AZaz09AFafAZ09AZ__az ~!~09AZ__az 09AZaz!/:@[`{~ az0909IIзIII0IIIPI#طIJIJIJI JIIIIJI@JعIJȹI II(JIxJIxJIJG@I`IIpI IJ0II,I@ILIJ HJDI+J\I(JPIJlIJIdIIZPJLtI`IpIJIxIIIIHIIIIXIIJI@#JȺI@IIJII II IHII0II(IpJDII!PI8I*J`IIPIpIxIJ JpIII IIJIIII IIXII0II"JIиI`IIII@J(I+J II @I I+J0I`JXI*JLIItI@IhIIIIJ|IIXIIPIJlII`IhIIJtI,JIJ\I"IJ6II#J'0IIPI IIRI%IpIIPIHIPJXIIмIIIII@JؼI0IIIIJ I(JnPI8Ih*J IIIIHI"J0II`IXIXIJIIIixIIIHJIIIIII J IJCI:I8IIJ%pII0J$I8I II0IhI(I,I+JI&IIIIоIIIJIhIؾIIIpII(JIIIJ(IhII`I?0RTfhk ;@[epp$$   ?? 88ii--  ....:.;.@.@.0000000012XXcc 3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq  JMO`jooWW  ? ? 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