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i<i$<Dn)i)<i$<DnѿT>N>N[}NZNFNGZND<<N[<[DsNZND<[>\>[DlNZN<N[<[De>[Dba@S[cks'm/m7m?mM    2 S  oTҿ-TyLN NNN8NxNyLN NNN8NxN n!!nB"nc#nN1NRNsNN5NVNwNN9NZN{NN=N^NN K l     i<iA<(hNi<)i<)hNN[=N\=N[|TT(yLN NNN8NxN n!!nB"nc#nN1NN5NN9NN=N K l     iѩ<iA<(hNi<)i<)hNN==[F|FkH@\_N@_iNNZN$N)%N=N[=[D;=N;=Eȅ@܅_N@_iNNZN$N)%N=Nۅ=E;=N;=E@_N)@;_iNN[N$N)%N=N=F;=N;;=;FH@\_Ni@{_iNN[N$N)%N=N[=[F;=N{;={F"H@@(n0N=[D=E@@(n4N=E=E@)@(n8N=F=;FH@i@(nsۗcbBdGRB@bCc~9qۗSA[BcC{Ĩ_Bv:rۗrۗܗ^uwj  {StG[csۗwGҖbABO`B`ny<<^vxj ^`[qۗBbB^` @Dh#Sqۗ@`Ba!GvbE^$`bBG#@!OB@fj%da cB``=rۗcbBdGRB@bBc~92qۗSA[BcC{Ĩ_+ܗ"8rۗBwBtrۗFrۗ^vxj!   _$Ր{9p4A|_*@|_kaTA|5_   _$Ր{9p4A|_S@_kaTA|5_   _$Ր{9p4A|_*@_kaTA|5_   _$Ր{9p4A_*@|_kaTA5_   _$Ր{9p4A_@|_aTAȑ5_   _$Ր{9p4 _* |_ 1|5__$Ր{9p4 _ |_1|ȯ5__$Ր{9p4 _* _ 15__$Ր{9p4 _ _1ȯ5_{{_bad optional accessnarrowing_errorGetElementType is not implementedGetDeleteFuncUnknown ExceptionUse GetStringTensor*() API to retrieve strings!this->empty()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtCustomOp*; _Alloc = std::allocator; reference = const OrtCustomOp*&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_vector.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtCustomOpDomain*; _Alloc = std::allocator; reference = OrtCustomOpDomain*&]basic_string: construction from null is not validbasic_string::_M_createoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.lengths allocation failedstring buffer allocation failedOutput buffer allocation failedSynchronizeInputs/onnxruntime_src/onnxruntime/core/session/IOBinding.ccSynchronizeOutputs was false.Stacktrace: const T& OrtValue::Get() const [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/ort_value.hTrying to get a Tensor, but got: IsTensor()T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]Trying to get a TensorSeq, but got: IsTensorSequence()const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]Trying to get a SparseTensor, but got: IsSparseTensor()OrtValue should contain a Tensor or a Sparse TensorThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse datashape is invalidbuffer size is too small for string elementelement index is out of boundsstatic void SafeIntExceptionHandler::SafeIntOnOverflow()/onnxruntime_src/onnxruntime/core/common/safeint.hInteger overflowEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/TensorSeq.hTensor sequence must contain only primitive typeselem_type_ != nullptrconst OrtValue& onnxruntime::TensorSeq::GetAt(size_t) consti < tensors_.size()void onnxruntime::TensorSeq::Add(const OrtValue&)TensorSeq: tensor to be added has a different data type.IsSameDataType(tensor.Get())static void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(onnxruntime::logging::Severity)/onnxruntime_src/include/onnxruntime/core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.onnxruntime::SparseTensor& {anonymous}::ValidateFillInputArgs(OrtValue*, const onnxruntime::TensorShape&, const OrtMemoryInfo*)/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccStrings can only reside in CPU memorytried Filling sparse tensor with negative value in values shapethe ort_value must contain a constructed tensorT* onnxruntime::Tensor::MutableData() [with T = std::__cxx11::basic_string]/onnxruntime_src/include/onnxruntime/core/framework/tensor.hTensor type mismatch. T !=utils::IsPrimitiveDataType(dtype_)input array doesn't equal tensor sizeonnxruntime_profile_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]vector::_M_realloc_insertThe session already has a PrePackedWeightsContainer instanceintra op thread pool must have at least one thread for RunAsyncRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundtried creating tensor with negative value in shapesize overflownot enough space: expected MakeCooStrings/onnxruntime_src/onnxruntime/core/framework/sparse_tensor.ccFillSparseTensorCooOrtStatus* OrtApis::FillSparseTensorCoo(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t)get() != pointer()typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IDataTransfer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IDataTransfer&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/unique_ptr.hMakeCooDataUse MakeCooStringsMakeCsrStringsFillSparseTensorCsrOrtStatus* OrtApis::FillSparseTensorCsr(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int64_t*, size_t)MakeCsrDataUse MakeCsrStringsOrtStatus* OrtApis::FillSparseTensorBlockSparse(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int32_t*)tried Filling sparse tensor with negative value in block sparse indices shapeMakeBlockSparseStringsFillSparseTensorBlockSparseMakeBlockSparseDataUse MakeBlockSparseStringsUseCooIndicesSparse format must not be set. Already contains format: Not expecting an allocator setOrtStatus* OrtApis::UseCooIndices(OrtValue*, int64_t*, size_t)UseCsrIndicesThis method does not expect allocator to be setOrtStatus* OrtApis::UseCsrIndices(OrtValue*, int64_t*, size_t, int64_t*, size_t)UseBlockSparseIndicesOrtStatus* OrtApis::UseBlockSparseIndices(OrtValue*, const int64_t*, size_t, int32_t*)Can not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyPosixEnv/onnxruntime_src/onnxruntime/core/platform/posix/env.cconnxruntimecpuinfo_initialize failedRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in librarytypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IOBinding; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IOBinding&]ORT_LOAD_CONFIG_FROM_MODELopenLoad model failed. File doesn't existsystem error number onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const onnxruntime::PathString&)/onnxruntime_src/onnxruntime/core/session/inference_session.ccGiven model could not be parsed while creating inference session. Error message: status.IsOK()onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int)Could not parse model successfully while constructing the inference sessionresultModelProto 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_protosession.load_model_formatORTMORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().model_loading_array__n < this->size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&; size_type = long unsigned int]CopyOneInputAcrossDevices/onnxruntime_src/onnxruntime/core/framework/utils.ccthis->_M_is_engaged()constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/optionaltypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::DeviceStreamCollection; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::DeviceStreamCollection&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IStreamCommandHandleRegistry; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IStreamCommandHandleRegistry&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; const_reference = onnxruntime::Stream* const&; size_type = long unsigned int]BindInputonnxruntime::common::Status onnxruntime::IOBinding::BindInput(const std::string&, const OrtValue&)Size mismatch: index= it.second=mapped_feed_names_.size() == feed_names_.size()St19_Sp_counted_deleterIPN11onnxruntime18IExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN6google8protobuf11MessageLiteEN11onnxruntime22IKernelTypeStrResolverEN4onnx10ModelProtoEN11onnxruntime21KernelTypeStrResolverEN11onnxruntime29OpSchemaKernelTypeStrResolverEN11onnxruntime16InferenceSessionEN11onnxruntime7EnvTimeE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeEN11onnxruntime3EnvEN11onnxruntime9TelemetryE*N11onnxruntime12_GLOBAL__N_18PosixEnvESt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime10IAllocatorESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESt23_Sp_counted_ptr_inplaceIN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionE1.17.1The requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s ORT Build Info: build type=Release, cmake cxx flags: -DNDEBUG -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fstack-protector-strong -O3 -pipe -ffunction-sections -fdata-sections -Wno-restrict -DCPUINFO_SUPPORTEDout of indexinternal errorindex out of rangeSpecified provider is not supported.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; const_reference = const onnxruntime::NodeArg* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long int; _Alloc = std::allocator; reference = long int&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; const_reference = const std::__cxx11::basic_string&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = long int; _Alloc = std::allocator; reference = long int&; size_type = long unsigned int]vector::reservemax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesmax_power_of_two_extend_bytesInvalid key found: basic_string::appendbasic_string::_M_replace_auxbasic_string::_M_replaceonnxruntime::GetAvailableExecutionProviderNames()::/onnxruntime_src/onnxruntime/core/providers/get_execution_providers.ccMake the EP: name shorterprovider.name.size() <= kMaxExecutionProviderNameLenInvalid build with no providers availablethis API does not support stringslocation dimensions do not match shape sizeinvalid location rangegsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::__cxx11::basic_string]const T& OrtValue::Get() const [with T = std::map, std::__cxx11::basic_string >]onnxruntime::DataTypeImpl::GetType() == type_Invalid index requested for map type.const T& OrtValue::Get() const [with T = std::map, double>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = double; _Alloc = std::allocator; reference = double&]const T& OrtValue::Get() const [with T = std::map >]const T& OrtValue::Get() const [with T = std::map]const T& OrtValue::Get() const [with T = std::vector >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.hMap is missing type entry for its value++index < c.size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; size_type = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with T = std::map, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Sequence is missing type entry for its elementconst T& OrtValue::Get() const [with T = std::vector, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with T = std::map; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Input is not of one of the supported sequence types.Input is not of type sequence or map.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = std::__cxx11::basic_string; V = std::__cxx11::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, long int>]const T& OrtValue::Get() const [with T = std::map, float>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = std::__cxx11::basic_string; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map]Input is not of one of the supported map types.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)const T* onnxruntime::Tensor::Data() const [with T = std::__cxx11::basic_string]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]Tensor must always contain primitive types. Found: value_type != nullptrOrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]len >= 0 && static_cast(len) < std::numeric_limits::max()OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Value type is not supported yet: Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Key type is not supported yet.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)Expecting all elements to be tensors. Got: in[idx]->IsTensor()Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported input typeopaque(,OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)static void OrtEnv::Release(OrtEnv*)/onnxruntime_src/onnxruntime/core/session/ort_env.ccenv_ptr == p_instance_.get()Unload/onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc*ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_N11onnxruntime18IExecutionProviderEN11onnxruntime13NonTensorTypeISt3mapIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4lessIlESaISt4pairIKlS7_EEEEEN11onnxruntime7MapTypeISt3mapIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4lessIlESaISt4pairIKlS7_EEEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdSt4lessIS7_ESaISt4pairIKS7_dEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdSt4lessIS7_ESaISt4pairIKS7_dEEEEEN3gsl15narrowing_errorESt19_Sp_counted_deleterIPvPFvS0_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElSt4lessIS7_ESaISt4pairIKS7_lEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElSt4lessIS7_ESaISt4pairIKS7_lEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime12DataTypeImplEN4onnx9TypeProtoEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS7_ESaISt4pairIKS7_fEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS7_ESaISt4pairIKS7_fEEEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS8_ESaISt4pairIKS8_fEEESaISF_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS8_ESaISt4pairIKS8_fEEESaISF_EEEETensorrtExecutionProviderCUDAExecutionProviderMIGraphXExecutionProviderROCMExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderVitisAIExecutionProviderQNNExecutionProviderNnapiExecutionProviderJsExecutionProviderCoreMLExecutionProviderArmNNExecutionProviderACLExecutionProviderDmlExecutionProviderRknpuExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderCANNExecutionProviderAzureExecutionProviderCPUExecutionProvidertypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Graph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Graph&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]UnpackTensor: the pre-allocate size does not match the size in protodata overflowIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by CopyTensorCopySparseTensors/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccunordered_map::atstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = float; _Alloc = std::allocator; reference = float&]__pos <= size()std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::operator[](size_type) [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; reference = char&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/basic_string.h/build/Release/_deps/protobuf-src/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: vector::_M_range_insertinput array is too shortstatic const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger()void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)Tensor type mismatch.type == dtype_const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)Tensor size () != new size (shape_.Size() == new_shape.Size()T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const/onnxruntime_src/include/onnxruntime/core/graph/graph.hValidating no unexpected access using an invalid node_index. Got: Max:node_index < nodes_.size()int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast(offset) < node_values_size_T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.p_ml_valueonnxruntime::KernelDefBuilder& onnxruntime::KernelDefBuilder::VariadicAlias(int, int)/onnxruntime_src/onnxruntime/core/framework/kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::SparseTensor]Missing Input: const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]const T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor]Required input at index is not present.input_ptrT* OrtValue::GetMutable() [with T = onnxruntime::SparseTensor]onnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&)Required output at index output_ptrLoadDynamicLibraryFromProviderFailed to load dynamic library typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeArg; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeArg&]void onnxruntime::TensorSeq::Add(onnxruntime::Tensor&&)IsSameDataType(tensor)void onnxruntime::TensorSeq::Add(OrtValue&&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&]N4onnx13FunctionProtoEN11onnxruntime23NodeAttributes_IteratorEN11onnxruntime28NodeAttributes_Iterator_ImplEN11onnxruntime18Node__EdgeIteratorEN11onnxruntime23Node__EdgeIterator_ImplEN11onnxruntime18Node__NodeIteratorEN11onnxruntime23Node__NodeIterator_ImplEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime16OptionalTypeBaseEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime12OptionalTypeINS_9TensorSeqEmEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime12OptionalTypeINS_9TensorSeqElEEN11onnxruntime16SparseTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime16SparseTensorTypeINS_10Float8E5M2EEEN11onnxruntime16SparseTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime16SparseTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime10TensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime10TensorTypeINS_10Float8E5M2EEEN11onnxruntime10TensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime10TensorTypeINS_12Float8E4M3FNEEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeIaEEN11onnxruntime14TensorTypeBaseEN11onnxruntime10TensorTypeIbEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E5M2FNUZEEEN11onnxruntime17PrimitiveDataTypeINS_10Float8E5M2EEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E4M3FNUZEEEN11onnxruntime17PrimitiveDataTypeINS_12Float8E4M3FNEEEN11onnxruntime17PrimitiveDataTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime17PrimitiveDataTypeIbEESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime35TensorShapeProto_Dimension_IteratorEN11onnxruntime40TensorShapeProto_Dimension_Iterator_ImplEN4onnx11TensorProtoEN4onnx9NodeProtoEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io20ZeroCopyOutputStreamEN6google8protobuf2io26CopyingOutputStreamAdaptorEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io19OstreamOutputStreamEN4onnx10GraphProtoENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEN4onnx14AttributeProtoEN4onnx18TypeProto_SequenceEN4onnx18TypeProto_OptionalEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_TensorEN4onnx16TensorShapeProtoEN11onnxruntime12CPUAllocatorEN5Eigen19ThreadPoolInterfaceEN11onnxruntime9EnvThreadEN11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE*N11onnxruntime12_GLOBAL__N_111PosixThreadEN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEEN11onnxruntime7logging11OStreamSinkEN11onnxruntime7logging5ISinkEN11onnxruntime7logging8CLogSinkEN11onnxruntime15UserLoggingSinkESt14default_deleteIN11onnxruntime18IExecutionProviderEEPFvPvESt8functionIFvPvEESt19_Sp_counted_deleterIPvSt8functionIFvS0_EESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tag00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 CPU allocator not foundTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.CANN execution provider is not enabled in this build.oneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.Attribute type not supported yet.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; const_reference = onnxruntime::NodeArg* const&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::common::Status::State; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::common::Status::State&]Create_State_Release_State_onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t, gsl::span)/onnxruntime_src/onnxruntime/core/framework/tensor.ccelt_type != nullptrT* onnxruntime::Tensor::MutableData() [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FN]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FNUZ]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2FNUZ]const T* onnxruntime::Tensor::Data() const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FN]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FNUZ]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2FNUZ]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) const/onnxruntime_src/onnxruntime/core/session/standalone_op_invoker.ccinvalid arg_num.arg_num < static_cast(input_count_)CopyUnable to find a data transfer for copying from device type: to device type: libonnxruntime_providers_shared.soEnsurevoid onnxruntime::ProviderSharedLibrary::Ensure()Provider_SetHostGetonnxruntime::Provider& onnxruntime::ProviderLibrary::Get()GetProviderSessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared librarySessionOptionsAppendExecutionProvider_CANN: Failed to load shared librarySessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryTryGetProviderInfo_CUDATryGetProviderInfo_ROCMCUDA and/or ROCM execution provider is either not enabled or not available.device_typeenable_npu_fast_compilefalsedevice_idnum_of_threadscache_direnable_opencl_throttlingdisable_dynamic_shapesnum_streamsSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryProvider options key/value cannot be emptyMaximum string length for a provider options key/value is 1024.SessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryDMLSessionOptionsAppendExecutionProvider/onnxruntime_src/onnxruntime/core/session/provider_registration.ccOrtStatus* OrtApis::SessionOptionsAppendExecutionProvider(OrtSessionOptions*, const char*, const char* const*, const char* const*, size_t)QNNOpenVINOSNPEXNNPACKWEBNNAZUREJSVitisAIUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'OrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTryGetProviderInfo_TensorRTonnxruntime::ProviderInfo_TensorRT& onnxruntime::GetProviderInfo_TensorRT()TensorRT Provider not available, can't get interface for itAddTensorRTCustomOpDomainToSessionOptionThe custom op domain name is already in session option.InvokeOpmatching node is missinginvalid node input count: , expect: invalid node output countstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; reference = onnxruntime::NodeArg*&]standalone_CreateOpKernel definition was not found for node Domain:'' op_type:typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::KernelDef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::KernelDef&]kernel already mapped to existing nodeN11onnxruntime8FunctionEN11onnxruntime12FunctionImplEN11onnxruntime15OpKernelContextEN11onnxruntime10standalone23StandAloneKernelContextEN4onnx14ValueInfoProtoESt23_Sp_counted_ptr_inplaceISt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEN11onnxruntime11FuncManager8FuncInfoESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIKS6_S9_EEESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime11ExLibLoaderEN11onnxruntime12ProviderHostEN11onnxruntime16ProviderHostImplEN11onnxruntime15ProviderHostCPUEN11onnxruntime19ProviderHostCPUImplEgraph_optimization_level is not validEnv is nullProvided OrtMemoryInfo is nullNo allocator for this device has been registered for sharing.Invalid attribute.Size of data not large enough to hold a float.Attribute has no float value.Size of data not large enough to hold the array of floats.Size of data not large enough to hold an int64.Attribute has no int64 value.Size of data not large enough to hold the array of int64.Size of data not large enough to hold the string.Size of data not large enough to hold the array of strings.Unknown attribute type.No requested allocator availableFailed to fetch a stream hosting the requested resourceexecution_mode is not validAttribute does not exist.::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input name::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output nameOutput buffer is not large enough for ::OrtKernelInfo node nameAddInitializer/onnxruntime_src/onnxruntime/core/framework/session_options.ccAn OrtValue for this name has already been added: ::OrtKernelInfo cannot get a valid logger from its execution providerOrtMemoryInfo is nullProvided allocator is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.ComputeAsync`InlinedVector::at(size_type) const` failed bounds checkFailed to fetch type shape info for the index.Result buffer is not large enoughInvalid context::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; const_reference = const std::shared_ptr&; size_type = long unsigned int]SyncProvidersSession config entry '' was not found.Output buffer is not large enough for session config entrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = OrtDevice; _Alloc = std::allocator; reference = OrtDevice&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::IOBinding::BindOutputImpl(const std::string&, const OrtValue&, OrtDevice)Size mismatchmapped_output_names_.size() == output_names_.size()Input index: contains null pointersonnxruntime::common::Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span >, gsl::span)Expecting same size spansinit_num == values.size()AddExternalInitializersonnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&)/onnxruntime_src/onnxruntime/core/session/custom_ops.ccUnsupported version '' in custom op 'RegisterCustomOpsLibrary/onnxruntime_src/onnxruntime/core/session/abi_session_options.ccFailed to unload handle for dynamic library SetOutputTypeShapeInvalid shape infosymbolic and integer dims mismatch!IsCompatibleinput count does not matchstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; const_reference = const onnx::OpSchema::FormalParameter&; size_type = long unsigned int]custom op schemas mismatch, expecting input to be of optional type input to be of variadic type input to keep same homogeneity input to keep same arity input to be of single typeoutput count does not match output to be of optional type output to be of variadic type output to keep same homogeneity output to keep same arity output to be of single type is not implemented in CreateAndRegisterAllocatorV2()OrtShapeInferContext::OrtShapeInferContext(onnx::InferenceContext&)shape inference not yet supported for non-tensor typesvalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; reference = std::__cxx11::basic_string&]onnx::OpSchema onnxruntime::CreateSchema(const std::string&, const std::vector&)No kernels to registers.ops.size() > 0custom op registered at runtimeonnxruntime::CreateSchema(const std::string&, const std::vector&)::Only the last to a custom op may be marked variadic.i == count - 1inputsAnother version of operator ''has a different number of . onnxruntime allows the overloading of an operator if all versions have the same number of declared static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o))defined list of typesall typesThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.1 == undefinedCreateCustomRegistrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema; _Alloc = std::allocator; reference = onnx::OpSchema&]Domain already set in registryRegisterOpSet/onnxruntime_src/onnxruntime/core/graph/schema_registry.ccRegisterOpSchemaInternalTrying to register schema with name (domain: version: ) from file line , but it is already registered from file , but it its domain is notknown by the checker., but it its version is higherthan the operator set version Schema error: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Tensor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Tensor&]*ZN11onnxruntime12CreateSchemaERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKSt6vectorIPK11OrtCustomOpSaISB_EEEUlRN4onnx16InferenceContextEE0_*ZN7OrtApis25KernelContext_ParallelForEPK16OrtKernelContextPFvPvmEmmS3_EUllE0_FvRN4onnx16InferenceContextEEPFvRN4onnx16InferenceContextEEN4onnx8OpSchema20num_outputs_allowed_MUliE_EN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime27OnnxRuntimeOpSchemaRegistryESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime22CreateKernelCreateInfoERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPK11OrtCustomOpEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISH_EEE_N11onnxruntime14CustomOpKernelEN11onnxruntime8OpKernelEN3Ort9ExceptionE12OrtAllocatorN11onnxruntime16OrtAllocatorImplEN11onnxruntime34OrtAllocatorImplWrappingIAllocatorESt23_Sp_counted_ptr_inplaceIN11onnxruntime14LibraryHandlesESaIvELN9__gnu_cxx12_Lock_policyE2EECpustd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::DataTypeImpl*; _Alloc = std::allocator; const_reference = const onnxruntime::DataTypeImpl* const&; size_type = long unsigned int]unknowntensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)tensor(float8e4m3fn)tensor(float8e4m3fnuz)tensor(float8e5m2)tensor(float8e5m2fnuz)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)) Declared=Mismatch between inferred and declared type. Inferred=[ShapeInferenceError] Unsupported Source/Target type=XYlegacy optimization attribute.consumed_inputsConstrain input and output types to float tensors.Abs/build/Release/_deps/onnx-src/onnx/defs/math/old.ccAddArgMax/build/Release/_deps/onnx-src/onnx/defs/reduction/old.ccArgMinAveragePool/build/Release/_deps/onnx-src/onnx/defs/nn/old.ccCeilWhich axis to concat on. Default value is 1.concat_resultConstrain output types to float tensors.Concat/build/Release/_deps/onnx-src/onnx/defs/tensor/old.ccDivExpFloorp value of the Lp norm used to pool over the input data, default is 2.0.GlobalLpPoolHardmaxLogLogSoftmaxdata_0MaxMaxRoiPool/build/Release/_deps/onnx-src/onnx/defs/nn/defs.ccMeanminMinNegReciprocalReduceL1ReduceL2minus infinity (if supported by the datatype) or undefined otherwiseReduceLogSumReduceLogSumExpminus infinity (if supported by the datatype) or the minimum value of the data type otherwiseReduceMaxundefinedReduceMeanplus infinity (if supported by the datatype) or the maximum value of the data type otherwiseReduceMinReduceProdReduceSumReduceSumSquareReluSigmoidSoftmaxSubA 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.activationsWhen computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.linear_before_resetWRBGRU/build/Release/_deps/onnx-src/onnx/defs/rnn/old.ccCoefficient of leakage.alphaLeakyReluConstrain input and output types to signed numeric tensors.slopePReluSqrtTanhAcos/build/Release/_deps/onnx-src/onnx/defs/math/defs.ccConstrain input to boolean tensor.Constrain output to boolean tensor.T1And/build/Release/_deps/onnx-src/onnx/defs/logical/defs.ccAsinAtanCosThe ratio of random dropoutratioDropoutConstrain input to integral tensors.Equal/build/Release/_deps/onnx-src/onnx/defs/logical/old.ccACWhether A should be transposedtransAWhether B should be transposedtransBScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.betaGemmConstrain input to float tensors.GreaterThis 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. Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 LessOrSinTanNumber of times to sample.sample_size(Optional) Seed to the random generator, if not specified we will auto generate one.seed(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.dtypeT2Constrain input types to float tensors.Constrain output types to integral tensors.Multinomial/build/Release/_deps/onnx-src/onnx/defs/generator/defs.ccXorLpPoolList 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`.padsconstantThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled.PadConstrain input and output types to all tensor types.Which axis to split on. length of each outputWhich axis to concat onConstrain output types to any tensor type.reshapedReshapeConstrain input and output types to all tensors.ExpandIsequence_lensinitial_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.bodyAn attribute specifying the number of scan_inputs M. num_scan_inputsAn 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.directionsInt64 tensorAll Tensor typesScan/build/Release/_deps/onnx-src/onnx/defs/controlflow/old.cc22OrtDefaultCpuAllocatorN4onnx14InferenceErrorE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZN7OrtApis25KernelContext_ParallelForEPK16OrtKernelContextPFvPvmEmmS7_EUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime20CreateCustomRegistryEN3gsl4spanIKP17OrtCustomOpDomainLm18446744073709551615EEERSt10shared_ptrINS_14CustomRegistryEEEUlRN4onnx16InferenceContextEE_(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionConstrain to boolean tensors.Compress(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Constrain input types.Constrain output types to be numerics.ConstantOfShape/build/Release/_deps/onnx-src/onnx/defs/generator/old.cc(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.EyeLikeConstrain input types to all numeric tensors.The value for the elements of the output tensor.ConstantConstrain input and output types to float/int tensors.SinhCoshAsinhAcoshAtanhConstrain output types to boolean tensors.IsNaNThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.CastConstrain to all tensor types.NonZeronearestTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)scalesConstrain input 'X' and output 'Y' to all tensor types.Upsample/build/Release/_deps/onnx-src/onnx/defs/tensor/defs.ccResizecase_change_actionstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"is_case_sensitiveBoolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.stopwordsEnvironment 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.localeStringNormalizer/build/Release/_deps/onnx-src/onnx/defs/text/defs.ccValuesIndicesConstrain index tensor to int64Dimension on which to do the sort.TopKWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentfmodConstrain input and output types to high-precision numeric tensors.ModThreshold value { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } ThresholdedReluConstrain output mask types to boolean tensors.a_zero_pointb_zero_pointT3Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.MatMulIntegery_scaley_zero_pointConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.QuantizeLinear/build/Release/_deps/onnx-src/onnx/defs/quantization/old.ccx_scalex_zero_pointConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.DequantizeLinear(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.detect_positive(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.detect_negativeIsInfboxesscoresmax_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.center_point_boxNonMaxSuppression/build/Release/_deps/onnx-src/onnx/defs/object_detection/old.cc(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.time_axis(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.batch_axisInput and output types can be of any tensor type.ReverseSequenceZConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).BitShiftRoundConstrain input and output types to floating-point tensors.DetConstrain input and output types to all numeric tensors.Einsum expression string.equationConstrain input and output types to all numerical tensor types.EinsumThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]batch_dimsConstrain input and output types to any tensor type.GatherNDTindtargetlossType 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.reductionSpecifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.ignore_indexConstrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLosstraining_modeConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.CeluConstrain input and output types to numeric tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } LessOrEqual { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } GreaterOrEqualType 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.labelsweightslog_probSoftmaxCrossEntropyLossConstrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Pow/build/Release/_deps/onnx-src/onnx/defs/reduction/defs.ccsum squareproductlog sumlog sum exponentL1 normL2 normIf 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.exclusiveIf set to 1 will perform the sums in reverse direction.reverseaxis tensor can be int32 or int64 onlyCumSum { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.allowzero/build/Release/_deps/onnx-src/onnx/defs/rnn/defs.ccA 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.The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].layoutCouple the input and forget gates if 1.input_forgetinitial_cPY_cLSTMOne (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.RNNBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.upperTrilu { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } HardSwishConstrain input and output types to all tensor and sequence types.IdentityThe data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliType of the element in the optional outputOConstrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types.Optional/build/Release/_deps/onnx-src/onnx/defs/optional/defs.ccConstrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.OptionalHasElement/build/Release/_deps/onnx-src/onnx/defs/optional/old.ccConstrain output type to all tensor or sequence types.OptionalGetElementtarget_typeCastLike(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.start(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.endInput tensor can be of arbitrary type.Constrain output to int64 tensor.ShapeMultiplicative 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. spatial_scaledefault 1; Pooled output Y's height.output_heightdefault 1; Pooled output Y's width.output_widthNumber 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.sampling_ratioavgThe pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).coordinate_transformation_moderoisbatch_indicesConstrain types to float tensors.Constrain types to int tensors.RoiAlign/build/Release/_deps/onnx-src/onnx/defs/object_detection/defs.ccType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.updatesScatterNDWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Constrain indices to integer typesScatterElementsGraph 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.then_branchGraph 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.else_branchAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.Only boolIfConstrain input and output types to all tensor, sequence, and optional types.Constrain input and output types to all tensor types (including bfloat).Where { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.The epsilon value to use to avoid division by zero.epsilonType of Mean and InvStdDev. This also specifies stage one's computation precision.stash_typeScaleInvStdDevConstrain input types and output Y type to float tensors.Type of Mean and InvStdDev tensors.The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.input_sequenceadditional_inputsout_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMap/build/Release/_deps/onnx-src/onnx/defs/sequence/defs.ccIf onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.onesidedThe axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.inversedft_lengthConstrain scalar length types to int64_t.DFTHannConstrain the input size to int64_t.Constrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } HannWindowHamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } HammingWindowBlackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } BlackmanWindowThe data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. output_datatypenum_mel_binssample_ratelower_edge_hertzupper_edge_hertzConstrain to integer tensors.Constrain to float tensorsConstrain to any numerical types.MelWeightMatrixIf onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.signalframe_stepwindowframe_lengthConstrain signal and output to float tensors.STFT Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Supported modes: `constant`(default), `reflect`, `edge`input_dataoutput_dataIf provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.CenterCropPadConstrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } MishWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.num_outputs { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_square = Mul(data, data) sum_square = ReduceSum(data_square, axes) sum_square_dbl = Cast (sum_square) sqrt = Sqrt(sum_square_dbl) reduced = CastLike(sqrt, data) } Constrain input to integer tensors.BitwiseAndBitwiseOrBitwiseXorConstrain input/output to integer tensors.BitwiseNotThe number of groups of channels. It should be a divisor of the number of channels `C`.num_groupsbiasGroupNormalizationConstrain input types to all (non-complex) tensors.The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.saturateThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.The value for the elements of the output tensor in sparse format.sparse_valueThe value for the sole element for the scalar, int64, output tensor.value_intThe values for the elements for the 1D, int64, output tensor.value_intsThe value for the sole element for the scalar, float32, output tensor.value_floatThe values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.value_stringThe values for the elements for the 1D, UTF-8 string, output tensor.value_stringsoffsetDilation value along each spatial axis of the kernel. Default is 1 along each axis.dilationsNumber of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.groupShape of the convolution kernel. If not present, it is inferred from the shape of input W.kernel_shapeNumber of groups of offset. C must be divisible by offset_group. Default is 1.offset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.stridesDeformConv(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.'x_scale' determines the output type./build/Release/_deps/onnx-src/onnx/defs/quantization/defs.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9./build/Release/_deps/onnx-src/onnx/defs/controlflow/defs.cc Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` Supported modes: `constant`(default), `reflect`, `edge`, `wrap`(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.Constrain output to int64 tensor, which should be a scalar though.Sizeif align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.align_cornersthetagridConstrain grid types to float tensors.Constrain size's type to int64 tensors. { # naming one: 1, one_f: 1.0, one_1d: [1], one_f_1d: [1.0] one = Constant () two = Constant () zero = Constant () four = Constant () one_1d = Constant () zero_1d = Constant () minus_one = Constant () minus_one_f = CastLike (minus_one, theta) zero_f = CastLike (zero, theta) one_f = CastLike (one, theta) two_f = CastLike (two, theta) constant_align_corners = Constant () constant_align_corners_equal_zero = Equal (constant_align_corners, zero) size_ndim = Size (size) condition_is_2d = Equal (size_ndim, four) N, C, D, H, W = If (condition_is_2d) < then_branch = g1 () => (N_then, C_then, D_then, H_then, W_then) { N_then, C_then, H_then, W_then = Split (size) D_then = Identity (one_1d) }, else_branch = g2 () => (N_else, C_else, D_else, H_else, W_else) { N_else, C_else, D_else, H_else, W_else = Split (size) } > size_NCDHW = Concat (N, C, D, H, W) theta_3d = If (condition_is_2d) < then_branch = g3 () => (theta_then) { # theta: N by 2 by 3 => N by 3 by 4 # use of thetaN23 is a way to make shape inference happy when theta is N by 3 by 4. gather_idx_6 = Constant () shape_23 = Constant () gather_idx_23 = Reshape (gather_idx_6, shape_23) shape_N23 = Concat (N, shape_23) gather_idx_N23 = Expand (gather_idx_23, shape_N23) thetaN23 = GatherElements (theta, gather_idx_N23) # N by 2 by 3 => N by 3 by 2 r1, r2 = Split (thetaN23) # N by 1 by 3 r1_ = Squeeze (r1) # N by 3 r2_ = Squeeze (r2) r11, r12, t1 = Split (r1_) # N by 1 r21, r22, t2 = Split (r2_) r11_shape = Shape (r21) float_zero_1d_ = ConstantOfShape (r11_shape) # N by 1 float_zero_1d = CastLike (float_zero_1d_, theta) float_one_1d = Add (float_zero_1d, one_f) # N by 1 R1 = Concat (r11, r12, float_zero_1d, t1) # N by 4 R2 = Concat (r21, r22, float_zero_1d, t2) R3 = Concat (float_zero_1d, float_zero_1d, float_one_1d, float_zero_1d) R1_ = Unsqueeze (R1, one_1d) # N by 1 by 4 R2_ = Unsqueeze (R2, one_1d) R3_ = Unsqueeze (R3, one_1d) theta_then = Concat (R1_, R2_, R3_) # N by 3 by 4 # theta_then = Identity (theta) }, else_branch = g4 () => (theta_else) { theta_else = Identity (theta) } > two_1d = Constant () three_1d = Constant () five_1d = Constant () constant_D_H_W_shape = Slice (size_NCDHW, two_1d, five_1d) # [N, C, D, H, W] => [D, H, W] zeros_D_H_W_ = ConstantOfShape (constant_D_H_W_shape) zeros_D_H_W = CastLike (zeros_D_H_W_, theta) ones_D_H_W = Add (zeros_D_H_W, one_f) D_float = CastLike (D, zero_f) H_float = CastLike (H, zero_f) W_float = CastLike (W, zero_f) start_d, step_d, start_h, step_h, start_w, step_w = If (constant_align_corners_equal_zero) < then_branch = h1 () => (start_d_then, step_d_then, start_h_then, step_h_then, start_w_then, step_w_then) { # => (float, float, float, float, float, float) step_d_then = Div (two_f, D_float) step_h_then = Div (two_f, H_float) step_w_then = Div (two_f, W_float) step_d_half = Div (step_d_then, two_f) start_d_then = Add (minus_one_f, step_d_half) step_h_half = Div (step_h_then, two_f) start_h_then = Add (minus_one_f, step_h_half) step_w_half = Div (step_w_then, two_f) start_w_then = Add (minus_one_f, step_w_half) }, else_branch = h2 () => (start_d_else, step_d_else, start_h_else, step_h_else, start_w_else, step_w_else) { # => (float, float, float, float, float, float) D_float_nimus_one = Sub (D_float, one_f) H_float_nimus_one = Sub (H_float, one_f) W_float_nimus_one = Sub (W_float, one_f) # avoid divide by 0 D_equals_one = Equal (D, one) step_d_else = If (D_equals_one) < then_branch = g5 () => (step_d_else_then) { step_d_else_then = Identity (zero_f) }, else_branch = g6 () => (step_d_else_else) { step_d_else_else = Div (two_f, D_float_nimus_one) } > step_h_else = Div (two_f, H_float_nimus_one) step_w_else = Div (two_f, W_float_nimus_one) start_d_else = Identity (minus_one_f) start_h_else = Identity (minus_one_f) start_w_else = Identity (minus_one_f) } > grid_w_steps_int = Range (zero, W, one) grid_w_steps_float = CastLike (grid_w_steps_int, step_w) grid_w_steps = Mul (grid_w_steps_float, step_w) grid_w_0 = Add (start_w, grid_w_steps) grid_h_steps_int = Range (zero, H, one) grid_h_steps_float = CastLike (grid_h_steps_int, step_h) grid_h_steps = Mul (grid_h_steps_float, step_h) grid_h_0 = Add (start_h, grid_h_steps) grid_d_steps_int = Range (zero, D, one) grid_d_steps_float = CastLike (grid_d_steps_int, step_d) grid_d_steps = Mul (grid_d_steps_float, step_d) grid_d_0 = Add (start_d, grid_d_steps) zeros_H_W_D = Transpose (zeros_D_H_W) grid_d_1 = Add (zeros_H_W_D, grid_d_0) grid_d = Transpose (grid_d_1) zeros_D_W_H = Transpose (zeros_D_H_W) grid_h_1 = Add (zeros_D_W_H, grid_h_0) grid_h = Transpose (grid_h_1) grid_w = Add (grid_w_0, zeros_D_H_W) grid_w_usqzed = Unsqueeze (grid_w, minus_one) grid_h_usqzed = Unsqueeze (grid_h, minus_one) grid_d_usqzed = Unsqueeze (grid_d, minus_one) ones_D_H_W_usqzed = Unsqueeze (ones_D_H_W, minus_one) original_grid = Concat (grid_w_usqzed, grid_h_usqzed, grid_d_usqzed, ones_D_H_W_usqzed) constant_shape_DHW_4 = Constant () original_grid_DHW_4 = Reshape (original_grid, constant_shape_DHW_4) original_grid_4_DHW_ = Transpose (original_grid_DHW_4) original_grid_4_DHW = CastLike (original_grid_4_DHW_, theta_3d) grid_N_3_DHW = MatMul (theta_3d, original_grid_4_DHW) grid_N_DHW_3 = Transpose (grid_N_3_DHW) N_D_H_W_3 = Concat (N, D, H, W, three_1d) grid_3d_else_ = Reshape (grid_N_DHW_3, N_D_H_W_3) grid_3d = CastLike (grid_3d_else_, theta_3d) # grid = Identity (grid_3d) grid = If (condition_is_2d) < then_branch = g1 () => (grid_then) { # [N, D=1, H, W, 3] => [N, H, W, 2] grid_squeezed = Squeeze (grid_3d, one_1d) # [N, H, W, 3] grid_then = Slice (grid_squeezed, zero_1d, two_1d, three_1d) # [N, H, W, 2] }, else_branch = g2 () => (grid_else) { grid_else = Identity (grid_3d) } > } AffineGridIf `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.Constrain scalar length types to integers.Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.approximateRGBPixel format. Can be one of "RGB", "BGR", or "Grayscale".pixel_formatencoded_streamimageConstrain input types to 8-bit unsigned integer tensor.Constrain output types to 8-bit unsigned integer tensor.ImageDecoder/build/Release/_deps/onnx-src/onnx/defs/image/defs.ccRegex pattern to match on. This must be valid RE2 syntax.patternInputs must be UTF-8 stringsOutputs are bools and are True where there is a full regex match and False otherwise.RegexFullMatchInputs and outputs must be UTF-8 stringsStringConcatDelimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.delimiterMaximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.maxsplitThe input must be a UTF-8 string tensorTensor of substrings.The number of substrings generated.StringSplitstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; const_reference = const onnxruntime::AllocPlanPerValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; reference = onnxruntime::AllocPlanPerValue&; size_type = long unsigned int]An allocator for this device has already been registered for sharing.Device:[DeviceType: MemoryType: DeviceId:OrtMemoryInfo:[ id: OrtMemType: OrtAllocatorType:std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::shared_ptr; _Alloc = std::allocator >; reference = std::shared_ptr&; size_type = long unsigned int]Could not find OrtValue with name 'The input must be a tensor of a numeric type or string. The output will be of the same tensor type.ArrayFeatureExtractor/build/Release/_deps/onnx-src/onnx/defs/traditionalml/defs.ccThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.thresholdBinarizermap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.TO_FLOATA string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.cast_toDENSEIndicates 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'.map_formIf the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.max_mapCastMapThe input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequencecats_stringsThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.cats_int64s_UnusedA 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.default_stringAn 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.default_int64CategoryMapperThe input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The 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.A string vocabulary array.
One and only one of the vocabularies must be defined.string_vocabularyAn integer vocabulary array.
One and only one of the vocabularies must be defined.int64_vocabularyDictVectorizerThe input type must be a tensor of a numeric type.The size of each input in the input listinputdimensionsFeatureVectorizerThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toimputed_value_floatsA value that needs replacing.replaced_value_floatValue(s) to change to.imputed_value_int64sreplaced_value_int64ImputerThe input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.classes_stringsLabelEncoder/build/Release/_deps/onnx-src/onnx/defs/traditionalml/old.ccThe input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).coefficientsA collection of intercepts.interceptsIndicates whether to do OvR or multinomial (0=OvR is the default).multi_classClass labels when using string labels. One and only one 'classlabels' attribute must be defined.classlabels_stringsClass labels when using integer labels. One and only one 'classlabels' attribute must be defined.classlabels_intsNONEIndicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'post_transformLinearClassifierThe input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.targetsLinearRegressorMAXOne of 'MAX,' 'L1,' 'L2'normNormalizerList of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.zerosOneHotEncoderThe input type must be a tensor of a numeric type, either [C] or [N,C].LINEARThe kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.kernel_typeList of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.kernel_paramsChosen support vectorssupport_vectorsFlag indicating whether the regression is a one-class SVM or not.one_classSupport vector coefficients.The number of support vectors.n_supportsIndicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'rhoSVMRegressorFirst, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'ScalerTree id for each node.nodes_treeidsNode id for each node. Node ids must restart at zero for each tree and increase sequentially.nodes_nodeidsFeature id for each node.nodes_featureidsThresholds to do the splitting on for each node.nodes_valuesPopularity of each node, used for performance and may be omitted.nodes_hitratesThe 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'nodes_modesChild node if expression is truenodes_truenodeidsChild node if expression is falsenodes_falsenodeidsFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.nodes_missing_value_tracks_trueThe id of the tree that each node is in.target_treeidsThe node id of each weighttarget_nodeidsThe index of the target that each weight is fortarget_idsThe weight for each targettarget_weightsThe total number of targets.n_targetsIndicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'SUMDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'aggregate_functionBase values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)base_valuesTreeEnsembleRegressorseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.classlabels_int64sZipMapNhwcConv/onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccadditionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointQLinearAdd/onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.ccmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointQLinearMulreducedConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.keepdimsReduceSumIntegerBiasGelu/onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input X and output Y types to float tensors.BiasSplitGelu/onnxruntime_src/onnxruntime/core/graph/contrib_ops/diffusion_defs.ccskipBiasAddapply softmax to elements for dimensions axis or highertrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1is_inner_broadcastBiasSoftmaxsqeuclideanThe 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".metricConstrains input to only numeric types.CDistConstrain input and output types to float or half tensors.ComplexMulComplexMulConjInversenumber of dimensions comprising the signalsignal_ndimmust be 0, normalization currently not supportednormalizedmust be 1, only one sided FFTs supportedIrfftThe NGram size.ngram_sizeTidscores_outConstrain scores input and output types to float tensors.NGramRepeatBlock/onnxruntime_src/onnxruntime/core/graph/contrib_ops/bert_defs.ccMtoken_offsetConstrain token_offset to integer typesRestorePaddingnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)RfftConstrain input and output types to all numeric tensors and bool tensors.FConstrain to float tensors.Constrain to integer types.DynamicTimeWarpingInteger representing the embedding vector size for each word.If not provide, use the filter size of conv weightembedding_sizeThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.conv_window_sizeInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.char_embedding_sizeSequenceConstrain to tensor(int32).Constrain to tensor(float).WordConvEmbeddingGemmFastGeluValue of alphaValue of betaAffine/onnxruntime_src/onnxruntime/core/graph/contrib_ops/onnx_deprecated_operators.ccParametricSoftplusBias applied to each channel, same size as C.The scale to apply.ImageScalerA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).borderA 1-D values of (height, width).Cropinput_as_shapeextra_shapeGivenTensorFillBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_stateshidden_prevgatesseq_lengthshiddenGRUUnitIf 1, mean and variance are computed across channels. Default is 0.across_channelsIf 0, normalize the mean only. Default is 1.normalize_varianceMeanVarianceNormalizationScaling valueScaledTanhintra-opinter-opseq(tensor(bfloat16))MemcpyFromHost/onnxruntime_src/onnxruntime/core/session/environment.ccConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostException caught: const onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalTensor(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.hProvided type is not an optional tensorIsOptionalTensor(type)N11onnxruntime16GraphTransformerESt18bad_variant_accessN4onnx11SchemaErrorEZN4onnx19RegisterOpSetSchemaIN11onnxruntime18internal_nhwc_onnx24OpSet_Internal_NHWC_ONNXEEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib21OpSet_ONNX_DeprecatedEEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib20OpSet_Microsoft_ver1EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver4EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver3EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver2EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver1EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver20EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver19EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver18EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver17EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver16EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver15EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver14EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver13EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver12EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver11EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver10EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver9EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver8EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver7EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver6EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver5EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver4EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver3EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver2EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver1EEEvibEUlONS_8OpSchemaEE_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_map(string, int64)map(int64, double)map(string, float)map(string, double)[json.exception.] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Node; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Node&]AddCustomOpDomainsReceived nullptr for custom registryLoadOrtModelBytesLoad model from failed. Only / bytes were able to be read.onnxruntime::logging::Severity onnxruntime::GetSeverity(const SessionOptions&)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Sequential modeParallel modeThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const SessionOptions&, const onnx::ModelProto&, bool, SessionOptions&)ModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedort_configParseOrtConfigJsonInModelProto/onnxruntime_src/onnxruntime/core/session/inference_session_utils.ccFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: operator()The Model Proto hasn't been checked for the ORT config json.session_optionsParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelcannot compare iterators of different containersintra_op_num_threadsintra_op_num_threads option in the model file must be an integerSetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to inter_op_num_threadsinter_op_num_threads option in the model file must be an integerSetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_modeexecution_mode option in the model file must be an integerSetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to graph_optimization_levelgraph_optimization_level option in the model file must be an integerSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: enable_profilingenable_profiling option in the model file must be an integerSetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to Ignoring unsupported session option in ORT config: LoadWithLoaderThis session already contains a loaded model._M_get() != nullptrstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::Model]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/shared_ptr_base.hException during loading: Unknown exceptionEncountered unknown exception in LoadWithLoader()model_loading_uri failed:offFlush-to-zero and denormal-as-zero are void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&)Could not finalize session options while constructing the inference session. Error Message: TraceSessionOptionsSession Options { execution_mode: execution_order:DEFAULTPRIORITY_BASEDUNKNOWN enable_profiling: optimized_model_filepath: enable_mem_pattern: enable_mem_reuse: enable_cpu_mem_arena: profile_file_prefix: session_logid: session_log_severity_level: session_log_verbosity_level: max_num_graph_transformation_steps: graph_optimization_level: intra_op_param: inter_op_param: use_per_session_threads: thread_pool_allow_spinning: use_deterministic_compute: config_options: { }session.set_denormal_as_zerosession.force_spinning_stopConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinningsession--intra-opsession.dynamic_block_baseDynamic block base set to session.intra_op_thread_affinitiesAffinity string must not be empty!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fnsession.inter_op.allow_spinning-inter-opcustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)/onnxruntime_src/onnxruntime/core/common/profiler.ccsession_logger != nullptr%Y-%m-%d_%H-%M-%S.jsonError during EndProfiling(): ~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Can't register custom op domains with ORT for std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Provider has already been registered./onnxruntime_src/onnxruntime/core/framework/execution_providers.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]SaveToOrtFormat/onnxruntime_src/onnxruntime/core/graph/model.ccRegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'Failed to save ORT format model to file: Failed to load model because protobuf parsing failed.session.strict_shape_type_inferencesession.disable_aot_function_inliningInlineFunctionsAOT/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccThis model does not have any local functions defined. AOT Inlining is not performedAOT inlining completed. () functions of () pruned.TransformGraphsession.disable_quant_qdqsession.debug_layout_transformationCastFloat16TransformerMemcpyTransformerN11onnxruntime17MemcpyTransformerEN11onnxruntime21InsertCastTransformerEN11onnxruntime25EnsureUniqueDQForNodeUnitEN6google8protobuf14FatalExceptionEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail9exceptionEtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SessionState; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SessionState&]This transformer is already registered AddPredefinedTransformersUnexpected data type. Actual: ()) , expected: (cpugpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: feedfetch names has elements, but has Invalid with name: '' expected to be of type: but received a tensorValidateInputsOutputssparse_tensor but received a sparse tensor but received a tensor sequenceconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(onnxruntime::MLDataType)Provided type is not an optional sequence tensorIsOptionalSeqTensor(type)seqParseStringWithClassicLocale/onnxruntime_src/include/onnxruntime/core/common/parse_string.hFailed to parse value: "T onnxruntime::ParseStringWithClassicLocale(std::string_view) [with T = long unsigned int; std::string_view = std::basic_string_view]RunReplaying the captured CUDA Graph for this model with tag: onnxruntime::common::Status onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph()/onnxruntime_src/onnxruntime/core/session/inference_session.hIsGraphCaptured()Cached EP instance for graph replay is not set yet before calling ReplayGraph()Session was not initializedSession not initialized.At least one output should be requested.Invalid output name:memory.enable_memory_arena_shrinkageFeedsFetchesInfo/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = OrtDevice; _Alloc = std::allocator; const_reference = const OrtDevice&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; reference = onnxruntime::MLValueCopyInfo&; size_type = long unsigned int]Running with tag: const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions&, std::unique_ptr&)Invalid 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)ExecuteGraphcannot create std::vector larger than max_size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const OrtDevice*; _Alloc = std::allocator; reference = const OrtDevice*&; size_type = long unsigned int]disable_synchronize_execution_providersCleanUp/onnxruntime_src/onnxruntime/core/framework/device_stream_collection.ccShrinkMemoryArenasUnable to shrink arena: error message: model_runStart another run for necessary memory allocation or graph capture.Encountered unknown exception in Run()input name cannot be emptyNULL input supplied for input output name cannot be emptyonnxruntime::common::Status onnxruntime::InferenceSession::Run(const onnxruntime::RunOptions&, gsl::span, gsl::span, gsl::span, gsl::span)fetch_unique_ptrs[i] != nullptrGetModelMetadataModel was not loadedModel was not loaded.GetModelInputsGetOverridableInitializersGetModelOutputsEndProfilingWriting profiler data to file [ ] {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : " : "} }, Profiler is disabled.Could not write a profile because no model was loaded.NewIOBindingInitializeInitializing session.Session has already been initialized.Adding default CPU execution provider.InjectExternalInitializedTensors/onnxruntime_src/onnxruntime/core/graph/graph.ccReplaced external initializer: session.use_env_allocatorsThis session will use the allocator registered with the environment.session.save_model_formatoptimization.minimal_build_optimizationssaveapply value of 'save' is only valid when saving an ORT format model.Invalid value for This session cannot use the CUDA Graph feature as requested by the user This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session cannot use the CUDA Graph feature as requested by the user as all compute graph nodes have not been partitioned to the CUDA EP.This model has shape massaging nodes that will execute on CPU. Use the CUDA Graph feature with caution. As long as the intermediate shapes produced in the model using the representative input used to capture the CUDA graph, will match the shapes produced in the model for other inputs of the same shape as the representative input (common case), it is safe to use the CUDA Graph feature.This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA Graph feature as requested by the user.session.disable_cpu_ep_fallbackConflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.PartitionOrtFormatModelApplyOrtFormatModelRuntimeOptimizationsFinalizeSessionState/onnxruntime_src/onnxruntime/core/framework/session_state.ccUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerSerializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.session.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytes1024Session successfully initialized.session_initializationException during initialization: Encountered unknown exception in Initialize()LoadOrtModelWithLoaderThis session has already been initialized.ORT model verification failed.Serialized version info is null. Invalid ORT format model.The old ORT format model (version ) has saved runtime optimizations. They will be ignored.The ORT format model version [] is not supported in this build Missing Model. Invalid ORT format model.session.use_ort_model_bytes_for_initializersLoadOpsetImportOrtFormat/onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.ccModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.ai.onnxLoadFromOrtFormatGraph is null. Invalid ORT format model.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Model; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Model&]N11onnxruntime20CPUExecutionProviderESt19_Sp_counted_deleterIPN11onnxruntime20CPUExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime21SchemaRegistryManagerESt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESessionNodeKernelApi@@Bsession.allow_released_opsets_onlystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = char; _Alloc = std::allocator; reference = char&]invalid string: ill-formed UTF-8 bytevector::_M_default_appendsession.use_ort_model_bytes_directlyobjectnullarraynumberbooleanbinarydiscardedmap::atcannot use at() with key '' not foundtrue literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected HQNZWTifc`]Kcannot get valuestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>; reference = nlohmann::basic_json<>*&]type must be string, but is void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator]invalid number; expected digit after '.'invalid number; expected digit after '-'invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Foj  eNIDNF>6.&zrjbZRJ7(GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>]__position != end()std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(iterator) [with _Key = std::__cxx11::basic_string; _Val = std::pair, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st, nlohmann::basic_json<> > >; _Compare = std::less; _Alloc = std::allocator, nlohmann::basic_json<> > >; iterator = std::_Rb_tree, std::pair, nlohmann::basic_json<> >, std::_Select1st, nlohmann::basic_json<> > >, std::less, std::allocator, nlohmann::basic_json<> > > >::iterator]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_tree.hvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; reference = nlohmann::basic_json<>&]object keyobject separatornumber overflow parsing 'Apply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccGraphTransformer modified: with status: ApplyTransformers/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccLoadunknown exceptiontuning_resultsParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modeltype must be array, but is Tuning results stored in the model file cannot be parsed. Error message: . Ignoring...post_layout_transform_step_.onnxonnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph&, bool):: mutableN11onnxruntime30CommonSubexpressionEliminationEN8nlohmann6detail11parse_errorEN8nlohmann6detail12out_of_rangeEN8nlohmann6detail10type_errorESt14default_deleteIN11onnxruntime20CPUExecutionProviderEESt14default_deleteIN11onnxruntime5ModelEE*ZN11onnxruntime12_GLOBAL__N_123PartitionOrtFormatModelERNS_5GraphERKNS_18ExecutionProvidersERNS_21KernelRegistryManagerERNS_12SessionStateERKNS_13ConfigOptionsERKNS_7logging6LoggerEEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlRKS1_E1_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE0_St23_Sp_counted_ptr_inplaceIN11onnxruntime12CPUAllocatorESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanE*ZN11onnxruntime16InferenceSession8RunAsyncEPK13OrtRunOptionsN3gsl4spanIKPKcLm18446744073709551615EEENS5_IKPK8OrtValueLm18446744073709551615EEES9_NS5_IPSA_Lm18446744073709551615EEEPFvPvPSF_mP9OrtStatusESH_EUlvE_*ZN11onnxruntime16InferenceSession10InitializeEvEUlRKN4onnx8OpSchemaEE0_*ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_*ZN11onnxruntime16InferenceSession12LoadOrtModelERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUlvE_*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession13LoadOnnxModelERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUlRSt10shared_ptrINS_5ModelEEE_St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIvELN9__gnu_cxx12_Lock_policyE2EE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; reference = onnxruntime::NodeArg*&; size_type = long unsigned int]YieldOpsession.qdqisint8allowedsession.enable_quant_qdq_cleanupQDQFinalCleanupTransformeronnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::SatApplyContextVariant&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccUnsupported optimization level: session.disable_double_qdq_removerDoubleQDQPairsRemoverConstantSharingCommonSubexpressionEliminationConstantFoldingMatMulAddFusionReshapeFusionQDQPropagationTransformerRocmBlasAltImplTransposeOptimizerTransposeOptimizer_optimization.enable_gelu_approximationQDQS8ToU8TransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionGeluFusionLayerNormFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionGatherToSplitFusionGatherToSliceFusionMatmulTransposeFusionBiasGeluFusionSkipLayerNormFusionFastGeluFusionQuickGeluFusionGeluApproximationBiasSoftmaxFusionBiasDropoutFusionMatMulScaleFusionMatMulActivationFusionConvAddActivationFusiononnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)InsertedPrecisionFreeCast_cast node to cast from float16 to float32 on cpuRecurse/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.hApplyImpl/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.ccbool onnxruntime::IsIsolatedFp16NodeOnCpu(const Node&, Graph&, const KernelRegistry&)idx < input_defs.size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = int; _Alloc = std::allocator; const_reference = const int&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()RemoveDuplicateCastTransformerN11onnxruntime30RemoveDuplicateCastTransformerEN11onnxruntime6ActionEN11onnxruntime14ReplaceWithNewEN11onnxruntime19ReplaceWithNewFixedEN11onnxruntime15NhwcTransformerEN11onnxruntime20ConvActivationFusionEN11onnxruntime28QDQSelectorActionTransformerEN11onnxruntime32FreeDimensionOverrideTransformerEN11onnxruntime25RuleBasedGraphTransformerEN11onnxruntime11RewriteRuleEN11onnxruntime25SelectorActionTransformerEN11onnxruntime21DoubleQDQPairsRemoverEN11onnxruntime15ConstantSharingEN11onnxruntime15ConstantFoldingEN11onnxruntime15MatMulAddFusionEN11onnxruntime13ReshapeFusionEN11onnxruntime25QDQPropagationTransformerEN11onnxruntime15RocmBlasAltImplEN11onnxruntime18TransposeOptimizerEN11onnxruntime20QDQS8ToU8TransformerEN11onnxruntime20GemmActivationFusionEN11onnxruntime26MatMulIntegerToFloatFusionEN11onnxruntime27DynamicQuantizeMatMulFusionEN11onnxruntime10GeluFusionEN11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionEN11onnxruntime15AttentionFusionEN11onnxruntime20EmbedLayerNormFusionEN11onnxruntime19GatherToSplitFusionEN11onnxruntime19GatherToSliceFusionEN11onnxruntime21MatmulTransposeFusionEN11onnxruntime14BiasGeluFusionEN11onnxruntime19SkipLayerNormFusionEN11onnxruntime14FastGeluFusionEN11onnxruntime15QuickGeluFusionEN11onnxruntime17GeluApproximationEN11onnxruntime17BiasSoftmaxFusionEN11onnxruntime17BiasDropoutFusionEN11onnxruntime17MatMulScaleFusionEN11onnxruntime22MatMulActivationFusionEN11onnxruntime26QDQFinalCleanupTransformerEN11onnxruntime23ConvAddActivationFusionEtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::NodeRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::NodeRef&]com.microsoftBatchNormalizationInstanceNormalizationConvQLinearConvConvTransposeMaxPoolMaxUnpoolGlobalAveragePoolGlobalMaxPoolLRNGridSampleDepthToSpaceSpaceToDepthFusedConvQLinearAveragePoolQLinearGlobalAveragePoolchannels_lasttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::GraphRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::GraphRef&]com.ms.internal.nhwcstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::basic_string_view; _Alloc = std::allocator >; reference = std::basic_string_view&; size_type = long unsigned int]Layout/Transpose optimization for failed: cannot create std::deque larger than max_size()std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::front() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_deque.hvoid onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int]Unsupported data type: bool onnxruntime::GetTransposePerms(const Node&, std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_transpose_fusion.cctranspose_node.InputDefs().size() == 1permstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; reference = const onnxruntime::Node*&]size_t onnxruntime::UpdateConsumerCount(Graph&, NodeArg*, InlinedHashMap&)!node_consumers.empty()/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.ccfused LayerNorm subgraphs SimplifiedLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/matmul_activation_fusion.ccFusedMatMul_FusedActivationFusedMatMulActivation with activation activationactivation_/onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add virtual onnxruntime::common::Status onnxruntime::MatmulBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/matmul_bn_fusion.ccscale_tensorbias_tensormean_tensorvar_tensormatmul_b_tensorMatMulBnFusion_GemmB_MatMulBnFusion_GemmBiasMatMulBnFusion_GemmGenerated from Matmul BatchNormalization fusion/onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.ccMatMulIntegerToFloatstd::optional onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.ccConstant initializer NodeArg shape should not be null. NodeArg: GetScalarConstantInitializerstd::optional > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)div_inputs.size() == 2mul_inputs.size() == 2std::vector onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)input_node.InputDefs().size() == 2 && scale_and_index->second < 2std::vector onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)output_node.OutputDefs().size() == 1_FusedMatMulAndScaleFused MatMul and Scaleonnxruntime::Node* onnxruntime::ReorderCastAndTranspose(Graph&, Node*, InlinedHashMap&, std::deque&, bool&, bool&)cast != nullptr_transformedCreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodestransBatchAtransBatchBMatMul_With_Transposefused MatMul and Transpose FN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISJ_ESt8equal_toISJ_ESaISJ_EEEPFN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISJ_ESt8equal_toISJ_ESaISJ_EEESt19bad_optional_access     ReorderOutputcom.microsoft.nchwcNotchannelsNhwcTransformerNhwcFusedConvstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]EnsureUniqueDQForNodeUnitauto_pad/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/ensure_unique_dq_for_node_unit.cc/duplicatedAdded by EnsureUniqueDQForEachExplicitOutputEdge/onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.ccbool onnxruntime::GetQConstantLowerUpper(const Graph&, const Node&, float&, float&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/clip_quantizelinear.ccUnexpected data type for QuantizeLinear input y_zero_point of CleanUpNodeSequence/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_final_cleanup.ccCleaning up back-to-back nodes: with name "" and *ZN11onnxruntime12_GLOBAL__N_119CleanUpNodeSequenceENS0_12NodeSequenceERNS_5GraphEmRKNS_7logging6LoggerEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_        SqueezeUnsqueezestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; reference = const onnxruntime::Node*&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; const_reference = const onnxruntime::Node* const&; size_type = long unsigned int]const onnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const onnxruntime::Graph&, End) const/onnxruntime_src/onnxruntime/core/graph/extended_graph_edge.hconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge::NodeInfo; _Dp = std::_Optional_base]Invalid node index node != nullptrstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNode(const onnxruntime::Graph&, const onnxruntime::Node&, int)node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()std::optional onnxruntime::{anonymous}::GetPreviousPropagationEdge(const onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccsrc_node != nullptrstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput(const onnxruntime::Graph&, const onnxruntime::Node&, int)node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()std::optional onnxruntime::{anonymous}::GetNextPropagationEdge(const onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&)dst_node != nullptronnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEnd(onnxruntime::Graph&, End) constonnxruntime::common::Status onnxruntime::{anonymous}::InsertQDQPair(onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&, onnxruntime::NodeArg&, onnxruntime::NodeArg*, const std::string&, const onnxruntime::logging::Logger&)At least one graph node must be specified in the propagation edge.src_node || dst_nodeInsertQDQPairInserting Q/DQ pair between node ("", index: and at NodeArg "_pre_q_q_to_dq_post_dq_qInserted by QDQPropagationTransformerFailed to set op schema for added Q node._dqFailed to set op schema for added DQ node.ValidateNodeGroupDQNodes/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/shared/utils.ccQDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; const_reference = const onnxruntime::Node* const&]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge; _Dp = std::_Optional_base]PropagateQBackwardPropagateDQForwardweight_zp_s8_2_u8_s8_2_u8/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantdropdrop_no_int16_supportGatherdropDQ1DQ2DQQDQSelectorActionTransformerdropSplitQDQ*DQQGemm*ZNK11onnxruntime3QDQ22SplitNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_*ZNK11onnxruntime3QDQ23DropDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_*ZNK11onnxruntime3QDQ24DropQDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_N11onnxruntime3QDQ13SplitSelectorEN11onnxruntime3QDQ22SplitNodeGroupSelectorEN11onnxruntime3QDQ21SplitReplaceWithQuantEN11onnxruntime3QDQ26VariadicReplaceWithQLinearEN11onnxruntime3QDQ21InputVariadicSelectorEN11onnxruntime3QDQ25VariadicNodeGroupSelectorEN11onnxruntime3QDQ22ConvReplaceWithQLinearEN11onnxruntime3QDQ12ConvSelectorEN11onnxruntime3QDQ21ConvNodeGroupSelectorEN11onnxruntime3QDQ24MatMulReplaceWithQLinearEN11onnxruntime3QDQ14MatMulSelectorEN11onnxruntime3QDQ23MatMulNodeGroupSelectorEN11onnxruntime3QDQ20GemmReplaceWithQuantEN11onnxruntime3QDQ12GemmSelectorEN11onnxruntime3QDQ21GemmNodeGroupSelectorEN11onnxruntime3QDQ23WhereReplaceWithQLinearEN11onnxruntime3QDQ13WhereSelectorEN11onnxruntime3QDQ22WhereNodeGroupSelectorEN11onnxruntime3QDQ14BinarySelectorEN11onnxruntime3QDQ23BinaryNodeGroupSelectorEN11onnxruntime3QDQ18ReplaceWithQLinearEN11onnxruntime3QDQ23UnaryReplaceWithQLinearEN11onnxruntime3QDQ13UnarySelectorEN11onnxruntime3QDQ22UnaryNodeGroupSelectorEN11onnxruntime3QDQ19DropDQNodesSelectorEN11onnxruntime3QDQ23DropDQNodeGroupSelectorEN11onnxruntime3QDQ17NodeGroupSelectorEN11onnxruntime11RemoveNodesEN11onnxruntime15MergeIntoTargetEN11onnxruntime3QDQ20DropQDQNodesSelectorEN11onnxruntime3QDQ24DropQDQNodeGroupSelectorEN11onnxruntime12NodeSelectorEN11onnxruntime3QDQ12BaseSelectorEN11onnxruntime3QDQ17QDQReplaceWithNewE*N11onnxruntime12_GLOBAL__N_130GraphConstantInitializerGetterE  Slicestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = int; _Alloc = std::allocator; reference = int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::Node::EdgeEnd*; _Alloc = std::allocator; reference = const onnxruntime::Node::EdgeEnd*&; size_type = long unsigned int]startsForEachWithIndexonnxruntime::Node* onnxruntime::NodesToOptimize::GetNode(size_t, bool) const/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)onnxruntime::NodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() const/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.ccA target node must be set.target_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices(gsl::span, NodeIndex, gsl::span, int, int)::Node index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndex/onnxruntime_src/onnxruntime/core/optimizer/quick_gelu_fusion.ccQuickGelu/onnxruntime_src/onnxruntime/core/optimizer/rocm_blas_alt_impl.cc__backwardpass/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.ccUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constant/onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccConcatTrainingstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = int; _Alloc = std::allocator; const_reference = const int&]Fuse_SubgraphCannot replace concat node with initializer:Fused reshape node: Total fused reshape node count: ApplySelectorsAndActions/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]MatchAndProcessMatched typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Action; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Action&]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::NodesToOptimizeIndices; _Dp = std::_Optional_base]ApplySavedRuntimeOptimizationsApplying runtime optimization action for transformer Missing action Nodes to optimize are not valid, skipping action.SetOpSinceVersionForProducedNodesNot enough produced nodes in the runtime optimization record.New node op () does not match produced node op in runtime optimization record (Too many produced nodes in the runtime optimization record.Index out of rangeMoveInputOutputImplMoveInputOutput/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/actions.ccCreateReplacementNodeRunForSaveFailed to set node op schema.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]Failed to remove node.void onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const std::string&, const OpVersionsMap&, std::unique_ptr, std::unique_ptr)Existing registration with name inserted_in_name_to_entry/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc_Float_CastCast Input to floatSkipLayerNormalizationfused SkipLayerNorm subgraphs     std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = long int; _Alloc = std::allocator; const_reference = const long int&; size_type = long unsigned int]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&; size_type = long unsigned int]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::unique_ptr; _Dp = std::_Optional_base, false, false>]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::basic_string_view; _Alloc = std::allocator >; const_reference = const std::basic_string_view&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::TensorRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::TensorRef&]!(__hi < __lo)constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = long int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_algo.hnoop_with_empty_axes after before MemcpyAddCopyNode/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccAdd Copy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const onnxruntime::InitializedTensorSet&)::SNPEExecutionProviderInternalTestingExecutionProvidervoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const onnxruntime::InitializedTensorSet&)ProcessInitializers Memcpy nodes are added to the graph . It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISA_ESt8equal_toISA_ESaISt4pairIKSA_SE_EEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISA_ESt8equal_toISA_ESaISt4pairIKSA_SE_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISC_ESt8equal_toISC_ESaISt4pairIKSC_SG_EEEEUlRKNS_7NodeArgEmE_const_transpose_optimizerlinearstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = long unsigned int; _Alloc = std::allocator; const_reference = const long unsigned int&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string_view; _Alloc = std::allocator >; reference = std::basic_string_view&]Unsupported ONNX opset: NhwcMaxPoolstorage_ordervirtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimization/ort_optimizer_api_impl.ccPermutation length does not match rank perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation entry out of bounds for shape 0 <= p && p_int < shape_proto->dim_size()virtual size_t onnxruntime::ApiTensor::NumElements() constFailed to get size of TensorProtosize >= 0virtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) constNo NodeArg found for name node_arg_ != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]int onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map, int>&) domain not found in opset imports.opset_import_iter != domain_to_version_map.end()Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.since_version.has_value()virtual void onnxruntime::ApiGraph::CopyValueInfo(std::string_view, std::string_view)Existing destination type is not compatible with source type.dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Datavirtual std::vector onnxruntime::ApiTensor::Data() constAdded in transpose optimizer_out/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimizer.ccTranspose optimizer failed: virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector&)Failed to find initializer for name: successTransposeInitializervirtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector&)Failed to find initializer to reshape with name Cannot reshape initializer to have different number of elementsnew_num_elts == old_num_eltsN27onnx_transpose_optimization3api8GraphRefEN11onnxruntime8ApiGraphEN27onnx_transpose_optimization3api12ValueInfoRefEN11onnxruntime12ApiValueInfoEN27onnx_transpose_optimization3api9TensorRefEN11onnxruntime9ApiTensorEN27onnx_transpose_optimization3api7NodeRefEN11onnxruntime7ApiNodeEUnsqueezeElimination_/onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node CheckNodesInPathK/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.hStart CheckNodesInPathKk_transpose has not perm attributek_reshape const not matchedPass CheckNodesInPathKCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQMatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find mask pathOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3where const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFindPath/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccFailed since multiple edges matched:->Failed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializeronnxruntime::optimizer_utils::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::/onnxruntime_src/onnxruntime/core/optimizer/utils.ccUnexpected data type for Clip input of bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector&, const std::vector&, const onnxruntime::logging::Logger&)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)/onnxruntime_src/onnxruntime/core/providers/common.haxis is not in valid range [-IsAxisInRange(axis, tensor_rank)Mask_Int32MaskCastCast mask from int64 to int32FuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseOutput edge count not expected for nodes in path vMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs num_headsunidirectionalmask_filter_valueFused an attention node for GPT./onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedFuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFuseSubGraphQKFused an attention node.FuseSubGraphQKDistilBertstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; reference = std::reference_wrapper&; size_type = long unsigned int]Failed in match input mask subgraphTotal fused Attention node count: BiasDropoutBitmaskBiasDropout/onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.ccBitmaskDropoutfused Add-Dropout-(Add) for /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccFastGelufused Add and Geluqkv_weightsqkv_bias>yE>    typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::{anonymous}::InitializerValue; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::{anonymous}::InitializerValue&]virtual std::string onnxruntime::{anonymous}::actions::FuseConvActivationAction::OpType(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccUnsupported operator: and domain: virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddRelu::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) constExpected Conv then Add.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_act_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.ccfused into softmax(input + bias)/onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrepresentative.output_index != kInvalidOutputIndexNot eliminating output of node ] because it's the graph's output./onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccConstantFoldIfNodeIf node condition initializer: `' is expected to have a single boolean elementUnable to constant fold. InlineIfSubgraph failed node '': Could not find a CPU kernel and hence can't constant fold typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeIndexInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeIndexInfo&]virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) ImplicitInputs:Invalid input index for node . Index: ExplicitInputs:/onnxruntime_src/onnxruntime/core/optimizer/constant_sharing.ccTotal shared scalar initializer count: virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.ccconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_HardSigmoidvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constExpected activation node.activation != nullptrFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)activation_paramsvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constConvActivationFusionConvActConvAddReluConvAddAct*N11onnxruntime12_GLOBAL__N_19selectors25ConvAddActivationSelectorE*N11onnxruntime12_GLOBAL__N_17actions27FuseConvAddActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors22ConvActivationSelectorE*N11onnxruntime12_GLOBAL__N_17actions24FuseConvActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors11ConvAddReluE*N11onnxruntime12_GLOBAL__N_17actions15FuseConvAddReluE*ZNK11onnxruntime15ConstantFolding9ApplyImplERNS_5GraphERbiRKNS_7logging6LoggerEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_N11onnxruntime15IExecutionFrameEN11onnxruntime23OptimizerExecutionFrameEortshared_RandomUniformRandomNormalRandomUniformLikeRandomNormalLike _Int32Cast Input from int64 to int32virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.ccbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.ccDynamicQuantizeLinearDynamicQuantizeMatMul/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64Conflicting free dimension overrides./onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccThe model has input '' with a fixed dimension size which does not equal the specified override of Invalid free dimension override.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output 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.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeRangeMatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.mask_indexEmbedLayerNormalizationfused EmbedLayerNorm subgraphs 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.Position embedding data type shall be float or float16.position_embeddingsFailed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). FuseSubGraphDistilBert/onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.ccfast_gelu_outputGPT2Gelufused GPT2Gelu subgraphs FreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)*ZN11onnxruntimeL28IsReducibleDoubleQDQSequenceERNS_5GraphERmmS2_S2_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_#B ;  std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; reference = std::reference_wrapper&]Fused Gemm with Transposestd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::KernelRegistry; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::KernelRegistry]TryCreateKernel/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.ccvirtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/gemm_sum_fusion.ccother_sum_input != nullptrvoid onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1void onnxruntime::Initializer::scale_by_axis(const onnxruntime::Initializer&, int, bool)/onnxruntime_src/onnxruntime/core/optimizer/initializer.ccInvalid other(scalers) sizescalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]onnxruntime::Initializer& onnxruntime::Initializer::add(const onnxruntime::Initializer&)Expecting the same data typedata_type() == other.data_type()Expecting the same sizesize() == other.size()onnxruntime::Initializer& onnxruntime::Initializer::sub(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::mul(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::div(const onnxruntime::Initializer&)/onnxruntime_src/onnxruntime/core/optimizer/gather_fusion.ccvirtual onnxruntime::common::Status onnxruntime::GatherToSliceFusion::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrange_input_defs.size() == 3unsqueeze_output_Unsqueeze_Unsqueeze for Fused Gather nodesUnsqueezeAxesInitializerSliceAxesInitializerSlice for Fused Gather nodes/onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.ccErffused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.ccEluSeluSoftplusSoftsignFusedGemmfused Gemm with activation virtual onnxruntime::common::Status onnxruntime::GemmSumFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) constnew_gemm_input_defs.size() == 3new_gemm_output_defs.size() == 1_sum_transformedFused Gemm with Sumgemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0!sum_input_movedgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&)Initializer must have a datatypeutils::HasDataType(tensor_proto)model_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()InitializerSplit for Fused Gather nodesSqueeze for Fused Gather nodesSqueezeAxesInitializeronnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const onnxruntime::InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)Failed to get allocator for optimizerallocator_ptr_InfoTried to allocate without valid type information, ort_value index=string tensor requires allocator to be provided.TensorProtoToOrtValueImpl/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISG_ESt8equal_toISG_ESaISt4pairIKSG_SK_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSQ_EEEUlRKNS_7NodeArgEmE_N11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferE    std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::shared_ptr; _Alloc = std::allocator >; reference = std::shared_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr >; _Alloc = std::allocator > >; reference = std::unique_ptr >&]generated at runtimeQLinearopsetRegisterOnnxOperatorKernels/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterCPUKernelsRegisterFp16KernelsRegisterOnnxMLOperatorKernelsRegisterCpuContribKernels/onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterQuantizationKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() const/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccGetThresholdsFromInputsiou_threshold must be in range [0, 1].static void SafeIntExceptionHandler::SafeIntOnDivZero()Divide by zeroVALIDSAME_UPPERSAME_LOWERonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string&)Unknown AutoPadType Stringonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.hA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSETstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::FusedConvFp16::UseSharedPrePackedBuffers(std::vector >&, int, bool&)/onnxruntime_src/onnxruntime/core/providers/cpu/fp16/fp16_conv.ccprepacked_buffers[0].get() == nullptronnxruntime::FusedConvFp16::FusedConvFp16(const onnxruntime::OpKernelInfo&)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const std::string&, int)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList]/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensiont_proto_p->dims_size() == 1Must have a single dimension of 1t_proto_p->dims()[0] == 1void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList]utils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)SetValueFromTensorProtoUnsupported value attribute datatype: RzDl6init_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aPrepareComputeMust have a valid input shape.Compute/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: Z shape does not match output shape. Z: Output: vector::_M_fill_insert(NHWC):[(NCHW):[/onnxruntime_src/onnxruntime/core/providers/cpu/fp16/fp16_pool.ccInput dimension cannot be less than 3.virtual onnxruntime::common::Status onnxruntime::PoolFp16::Compute(onnxruntime::OpKernelContext*) constInvalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || N == 0Invalid kernel shape. Input shape , ] Kernel shape:[`InlinedVector::at(size_type)` failed bounds checkN11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJlNS_9MLFloat16EfdasihtjmbEEEEE*N11onnxruntime12_GLOBAL__N_115ConstantOfShapeE*ZNK11onnxruntime8PoolFp167ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime8PoolFp16EN11onnxruntime13BufferDeleterEFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEEPFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEE*ZNK11onnxruntime13FusedConvFp167ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime13FusedConvFp16E28MLAS_HALF_GEMM_POSTPROCESSOR35MLAS_HALF_GEMM_ACTIVATION_PROCESSORN11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIvELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime20CPUExecutionProvider25CreatePreferredAllocatorsEvEUliE_N11onnxruntime3QDQ24BinaryReplaceWithQLinearE  start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!delta in Range operator should be scalar like tensor, yet got shape:Output type not supported in this build: _M_stddev > _RealType(0)std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/random.hstd::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]_M_a <= _M_bstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double](null)input count mismatchCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDhighonnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hmin_ <= max_onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span, gsl::span)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1MultinomialCompute/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccInvalid data type of void onnxruntime::SliceIteratorBase::Init(gsl::span, gsl::span, gsl::span)dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()Cannot apply CumSum operator on a scalar/onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cconnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime6CumSumIlEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_6Clip_6IfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplImEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIlEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIjEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIiEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIhEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIaEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIdEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIfEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_N11onnxruntime4ClipEN11onnxruntime13clip_internal10Clip_6BaseIfEEN11onnxruntime6Clip_6IfEEN11onnxruntime5RangeEN11onnxruntime11MultinomialEN11onnxruntime17RandomUniformLikeEN11onnxruntime13RandomUniformEN11onnxruntime16RandomNormalLikeEN11onnxruntime12RandomNormalE? Einsum op: There must be atleast one inputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; reference = const onnxruntime::Tensor*&]There was a problem acquiring temporary memory allocator in Einsum opNumber of subscripts in the input equation does not match number of input tensorsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; const_reference = const onnxruntime::Tensor* const&; size_type = long unsigned int]Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum 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 in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Found a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsvoid onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = long int]dims.size()=starts.size()=dims.size() == starts.size()extents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = double]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = float]onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccX != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedonnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.hMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()basic_string::substr%s: __pos (which is %zu) > this->size() (which is %zu)onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&, void*)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span&)The rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccnum_broadcasted_indices < num_of_ellipsis_dims_The broadcasted dimensions of the inputs are incompatibledim_iter == rankstd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr)The innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]Einsum op: Unsupported data type for Diagonal std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, onnxruntime::AllocatorPtr, void*, const DeviceHelpers::Transpose&)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*)Cannot parse the diagonal elements along dims for input shape rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]Rank of the input must match number of subscript labels corresponding to the inputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; reference = onnxruntime::TensorShape&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::EinsumEquationPreprocessor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::EinsumEquationPreprocessor&]...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisDeviceCompute/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; const_reference = const onnxruntime::TensorShape&; size_type = long unsigned int]Einsum op: An implementation for the input type is not supported yetFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEPFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEPFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEN11onnxruntime6EinsumEN11onnxruntime3DetIfEE?void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = float]The override dims are not compatible with given tensor's shape. Left shape: Left shape override: left.Shape().Size() == left_shape_override.Size()Right shape: Right shape override: right.Shape().Size() == right_shape_override.Size()Ranks of pair-wise operands must be equal. Right shape: left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]Data types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()Only 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = long int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = long int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.hAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)Can broadcast 0 by 0 or 1. is invalid.largest <= 1void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)void onnxruntime::InputBroadcaster::AdvanceBy(size_t)InputBroadcaster can only start at span boundary!offset % span_size_ == 0onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t)Invalid start/ending offset [) for tensor of length:start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenBroadcast Output range [) are not at boundary of span with size:start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&)N11onnxruntime3SubIdEEN11onnxruntime3SubIfEEN11onnxruntime3AddIlEEN11onnxruntime3AddIiEEN11onnxruntime3AddIdEEN11onnxruntime3AddIfEEµµµrBrBrBrB@K@K@K@K;?;?;?;?@@@@r1r1r1r1????n/ (*>(*>(*>>>>> ElementWiseKernel/onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]N11onnxruntime8functors4SqrtIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEEN11onnxruntime8functors10ReciprocalIdEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEEN11onnxruntime8functors10ReciprocalIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEEN11onnxruntime8functors4CeilIdEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIdEEEEN11onnxruntime8functors4CeilIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEEN11onnxruntime8functors5FloorIdEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIdEEEEN11onnxruntime8functors5FloorIfEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEEN11onnxruntime8functors3NegIlEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEEN11onnxruntime8functors3NegIiEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEEN11onnxruntime8functors3NegIaEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEEN11onnxruntime8functors3NegIdEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEEN11onnxruntime8functors3NegIfEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEEN11onnxruntime8functors26ElementWiseRangedTransformImEEN11onnxruntime8functors3AbsImEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEEN11onnxruntime8functors26ElementWiseRangedTransformIjEEN11onnxruntime8functors3AbsIjEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEEN11onnxruntime8functors26ElementWiseRangedTransformItEEN11onnxruntime8functors3AbsItEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEEN11onnxruntime8functors26ElementWiseRangedTransformIhEEN11onnxruntime8functors3AbsIhEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEEN11onnxruntime8functors26ElementWiseRangedTransformIlEEN11onnxruntime8functors3AbsIlEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEEN11onnxruntime8functors26ElementWiseRangedTransformIiEEN11onnxruntime8functors3AbsIiEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEEN11onnxruntime8functors26ElementWiseRangedTransformIsEEN11onnxruntime8functors3AbsIsEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEEN11onnxruntime8functors26ElementWiseRangedTransformIaEEN11onnxruntime8functors3AbsIaEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEEN11onnxruntime8functors26ElementWiseRangedTransformIdEEN11onnxruntime8functors3AbsIdEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEEN11onnxruntime8functors26ElementWiseRangedTransformIfEEN11onnxruntime8functors3AbsIfEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEEN11onnxruntime3DivIlEEN11onnxruntime3DivIiEEN11onnxruntime3DivIdEEN11onnxruntime3DivIfEEN11onnxruntime3MulIlEEN11onnxruntime3MulIiEEN11onnxruntime3MulIdEEN11onnxruntime3MulIfEEN11onnxruntime3SubIlEEN11onnxruntime3SubIiEE onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]N11onnxruntime14GreaterOrEqualIlEEN11onnxruntime14GreaterOrEqualIiEEN11onnxruntime14GreaterOrEqualIdEEN11onnxruntime14GreaterOrEqualIfEEN11onnxruntime11LessOrEqualIlEEN11onnxruntime11LessOrEqualIiEEN11onnxruntime11LessOrEqualIdEEN11onnxruntime11LessOrEqualIfEEN11onnxruntime5EqualINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5EqualIdEEN11onnxruntime5EqualIfEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIbEEN11onnxruntime7GreaterIlEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIdEEN11onnxruntime7GreaterIfEEN11onnxruntime4LessIlEEN11onnxruntime4LessIiEEN11onnxruntime4LessIdEEN11onnxruntime4LessIfEEN11onnxruntime5Min_8EN11onnxruntime5Min_6IfEEN11onnxruntime5Max_8EN11onnxruntime5Max_6IfEEN11onnxruntime5Sum_8IdEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Sum_6IdEEN11onnxruntime5Sum_6IfEEN11onnxruntime8functors3LogIdEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEEN11onnxruntime8functors3LogIfEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEEN11onnxruntime8functors3ExpIdEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEEN11onnxruntime8functors3ExpIfEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEEN11onnxruntime3PowEN11onnxruntime8functors4SqrtIdEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEE     Unsupported Y type: Unsupported X type: onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccMust have 1 or more inputsinputCount >= 1All inputs must have the same shapedata_n.Shape() == shapeonnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]data_1.Shape() == shapeonnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)fmod must have value either 0 or 1(fmod == 0) || (fmod == 1)void onnxruntime::UntypedExpand(OpKernelContext&, const ProcessBroadcastSpanFuncs&)Tensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1void onnxruntime::ExpandBroadcastLooper(BroadcastHelper&, const ProcessBroadcastSpanFuncs&)ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()N11onnxruntime3ModEN11onnxruntime8Expand_8INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5PReluIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime4SinhIfEEN11onnxruntime4AtanIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AsinIfEEN11onnxruntime3TanIfEEN11onnxruntime3CosIfEEN11onnxruntime3SinIdEEN11onnxruntime3SinIfEEN11onnxruntime3XorEN11onnxruntime2OrEN11onnxruntime3AndEN11onnxruntime3NotEN11onnxruntime3ErfIfEEN11onnxruntime10BitwiseXorImEEN11onnxruntime10BitwiseXorIjEEN11onnxruntime10BitwiseXorItEEN11onnxruntime10BitwiseXorIhEEN11onnxruntime10BitwiseXorIlEEN11onnxruntime10BitwiseXorIiEEN11onnxruntime10BitwiseXorIsEEN11onnxruntime10BitwiseXorIaEEN11onnxruntime9BitwiseOrImEEN11onnxruntime9BitwiseOrIjEEN11onnxruntime9BitwiseOrItEEN11onnxruntime9BitwiseOrIhEEN11onnxruntime9BitwiseOrIlEEN11onnxruntime9BitwiseOrIiEEN11onnxruntime9BitwiseOrIsEEN11onnxruntime9BitwiseOrIaEEN11onnxruntime10BitwiseNotImEEN11onnxruntime10BitwiseNotIjEEN11onnxruntime10BitwiseNotItEEN11onnxruntime10BitwiseNotIhEEN11onnxruntime10BitwiseNotIlEEN11onnxruntime10BitwiseNotIiEEN11onnxruntime10BitwiseNotIsEEN11onnxruntime10BitwiseNotIaEEN11onnxruntime10BitwiseAndImEEN11onnxruntime10BitwiseAndIjEEN11onnxruntime10BitwiseAndItEEN11onnxruntime10BitwiseAndIhEEN11onnxruntime10BitwiseAndIlEEN11onnxruntime10BitwiseAndIiEEN11onnxruntime10BitwiseAndIsEEN11onnxruntime10BitwiseAndIaEEN11onnxruntime6Mean_8IfEEN11onnxruntime6Mean_6IfEE  void onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if::value, void>::type = void]fmod attribute must be true for floating point typesvoid onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = double; typename std::enable_if::value, void>::type = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext*) constonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]N11onnxruntime8BitShiftImEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftIhEE((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO"?"?"?"?]G]G]G]GɿɿɿɿZZZZ4§4§4§4§ s7 s7 s7 s7nnnnpMpMpMpMӅ<Ӆ<Ӆ<Ӆ<*=*=*=*=****GGGG-DT! >::Compute(onnxruntime::OpKernelContext*) const::Invalid usage. Input 1 is a shape with no data.*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = double]input_count >= 1onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]input_size < std::numeric_limits::max()onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]static void onnxruntime::IAllocator::ValidateAllocation(void*, size_t)/onnxruntime_src/include/onnxruntime/core/framework/allocator.hMemory allocation failed. Size=p != nullptr || size == 0onnxruntime::GemmBase::GemmBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_base.hinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hleft.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2left[i] >= 0right[i] >= 0 K: N:GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0N11onnxruntime4GemmINS_9MLFloat16EEEN11onnxruntime4GemmIdEEN11onnxruntime8GemmBaseEN11onnxruntime4GemmIfEE@C@C̐̐@@??;f?;f?Ó´?Ó´?RV??RV??.>r,@.>r,@MK1@MK1@i@i@~}c@~}c@ &@ &@!^Q@!^Q@??&3T@&3T@3N,F@3N,F@% 7@% 7@9B.?9B.?KKKKCCCC5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>r1?r1?r1?r1?ʡE.@ʡE.@ʡE.ʡE.+eG?+eG?@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?fff@f@ mlas.enable_gemm_fastmath_arm64_bfloat16/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_SBGEMM_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_SBGEMM_DATA_PARAMS&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_SGEMM_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_SGEMM_DATA_PARAMS&; size_type = long unsigned int]Signstatic void onnxruntime::IAllocator::ValidateAllocator(const T&) [with T = std::shared_ptr]allocator != nullptrvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const TensorShape*, T*) [with T = float; ptrdiff_t = long int]c_shape is required if c_data is providedc_shape != nullptrvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const TensorShape*, T*) [with T = MLFloat16; ptrdiff_t = long int]Prepacking of B is supported by MLAS half gemm API, but not implemented by this kernel yetvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const TensorShape*, T*) [with T = double; ptrdiff_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=N11onnxruntime7SoftmaxIdEEN11onnxruntime7SoftmaxIfEEN11onnxruntime4SignEN11onnxruntime5RoundIdEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime6MatMulIlEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIfEEN11onnxruntime7HardmaxIfEEZNK11onnxruntime4GemmIdE17ComputeActivationEPdlPNS_11concurrency10ThreadPoolEEUlllE_ZNK11onnxruntime4GemmIfE17ComputeActivationEPflPNS_11concurrency10ThreadPoolEEUlllE_ZNK11onnxruntime4GemmINS_9MLFloat16EE17ComputeActivationEPS1_lPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIvEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPvE_input 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 negativek argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 1 input - the tensor to be processedComputeImplOpset13/onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.ccvoid onnxruntime::TopkOpset9ConstructorCommon(const OpKernelInfo&, int&, unsigned int&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const OpKernelInfo&, int&)void onnxruntime::TopkOpset11ConstructorCommon(const OpKernelInfo&, int&, bool&, bool&)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_N11onnxruntime4TopKILi11EiEEN11onnxruntime4TopKILi11ElEEN11onnxruntime4TopKILi11EdEEN11onnxruntime4TopKILi11EfEEN11onnxruntime4TopKILi10EdEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi9EdEEN11onnxruntime4TopKILi9EfEEInput data with index: is NaNInput of string must have output of int64Input of int64 must have output of string Input of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = float; _Alloc = std::allocator; const_reference = const float&; size_type = long unsigned int]Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK()TO_STRINGTO_INT64onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64info.GetAttr("map_form", &attr).IsOK()SPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string&)Invalid PACK_MAP value of Expected DENSE or SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)num_entries == int_categories.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; reference = std::__cxx11::basic_string&; size_type = long unsigned int]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.hinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::__cxx11::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, long int>]onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccNumber of inputs () does not match number of inputdimensions values (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()input_tensor_ptr != nullptrInvalid input type:onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constEmpty value of imputed values.Empty input dimensions.Invalid type/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()Invalid input type of value: Expected std::map or std::maponnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.ccNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = float]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = long int]%fonnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::__cxx11::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = std::__cxx11::basic_string]Unexpected CAST_TO value of N11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml9ImputerOpEN11onnxruntime2ml17FeatureVectorizerEN11onnxruntime2ml16DictVectorizerOpIldEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdEEN11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14CategoryMapperEN11onnxruntime2ml7CastMapEN11onnxruntime2ml11BinarizerOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIfEEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = float; _Alloc = std::allocator; reference = float&; size_type = long unsigned int]default_tensorT onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = std::__cxx11::basic_string; std::string = std::__cxx11::basic_string]LabelEncoder could not unpack default tensor result.IsOK()T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = float; std::string = std::__cxx11::basic_string]keys_floatsvalues_stringsLabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::__cxx11::basic_string]num_keys == num_valueskeys_stringsvalues_floatsdefault_floatonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = float]keys_int64sonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]values_int64sonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int]keys_tensorvalues_tensoronnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int]Keys and values must have the same length.keys.size() == values.size()onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = std::__cxx11::basic_string]N11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_EEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14LabelEncoder_4IflEEN11onnxruntime2ml14LabelEncoder_4IfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IffEEN11onnxruntime2ml14LabelEncoder_4IlfEEN11onnxruntime2ml14LabelEncoder_4IlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IllEEN11onnxruntime2ml14LabelEncoder_2IllEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14LabelEncoder_2IlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_2IffEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_EEN11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml14LabelEncoder_2IfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEstd::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = long int; std::string = std::__cxx11::basic_string]LabelEncoder is missing attribute or LabelEncoder could not unpack tensor attribute std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = std::__cxx11::basic_string; std::string = std::__cxx11::basic_string]T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = short int; std::string = std::__cxx11::basic_string]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = short int; std::string = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = short int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = short int; _Alloc = std::allocator; reference = short int&; size_type = long unsigned int]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = float; std::string = std::__cxx11::basic_string]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = double; std::string = std::__cxx11::basic_string]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = double; _Alloc = std::allocator; reference = double&; size_type = long unsigned int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::__cxx11::basic_string]LOGISTICSOFTMAXSOFTMAX_ZEROonnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc!coefficients_.empty()N11onnxruntime2ml16LinearClassifierEN11onnxruntime2ml14LabelEncoder_4IldEEN11onnxruntime2ml14LabelEncoder_4IdlEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdEEN11onnxruntime2ml14LabelEncoder_4IdNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IddEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEsEEInvalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.ccvoid* onnxruntime::IAllocator::AllocArray(size_t, size_t)Invalid size requested for allocation: * Unsupported data type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&)onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()L1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string&)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.ccOne and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()num_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::__cxx11::basic_string]POLYRBFSVMCommon/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&)vectors_per_classprob_aprob_bSVMClassifieronnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&)proba_.size() == probb_.size()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Input shape needs to be at least a single dimension.void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = int]shape_size == out.size()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = long int]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = double]Unsupported input element type of void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span, ptrdiff_t, ptrdiff_t, ptrdiff_t, const std::vector&, const std::vector&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) constScores output is incorrect size. Expected:scores_output_data.size() >= scores_output_sizeInput shape had more than 2 dimension. Dims=Unknown Category and zeros = 0.onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccEmpty scale in attributes!scale_.empty()Scale size: () != (scale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int]*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfLm18446744073709551615EEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime2ml9SVMCommonEN11onnxruntime2ml13SVMClassifierEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_N11onnxruntime2ml8ScalerOpIiEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml15OneHotEncoderOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml10NormalizerEN11onnxruntime2ml15LinearRegressorEZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_static size_t onnxruntime::IAllocator::ValidatedCalcMemSizeForArray(size_t, size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]TreeEnsembleClassifier/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]base_values_as_tensor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.honnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]nodes_hitrates_as_tensornodes_values_as_tensortarget_weights_as_tensoronnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]Invalid argumentUnexpected mode:onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]class_weights_as_tensorclass_idsclass_nodeidsclass_treeidsclass_weightsonnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = double; _Alloc = std::allocator; const_reference = const double&; size_type = long unsigned int]computeonnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = int; ThresholdType = float; OutputType = float]TreeEnsemble only works on 1D, 2D tensors.One path in the graph requests feature but input tensor has features.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::ScoreValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 6, std::allocator > >; _Alloc = std::allocator, 6, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 6, std::allocator > >&; size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hpredictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == (size_t)n_targets_or_classes_onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float]Unknown aggregation function in TreeEnsemble.void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]N11onnxruntime2ml6detail18TreeEnsembleCommonIiffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIiffEEN11onnxruntime2ml22TreeEnsembleClassifierIiEEN11onnxruntime2ml6detail18TreeEnsembleCommonIlffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIlffEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml6detail18TreeEnsembleCommonIddfEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIddfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml6detail28TreeEnsembleCommonAttributesEN11onnxruntime2ml6detail18TreeEnsembleCommonIfffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIfffEEN11onnxruntime2ml22TreeEnsembleClassifierIfEEZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPfE_N11onnxruntime2ml12SVMRegressorIfEEX dims is empty.classlabels_int64sbase_values_as_tensoronnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = double; ThresholdType = double; OutputType = float]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::ScoreValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 3, std::allocator > >; _Alloc = std::allocator, 3, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 3, std::allocator > >&; size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = int; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]Tree id mismatch. Expected but got at position std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&; size_type = long unsigned int]False node must always be the next node, but it isn't at index with flags size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = long int; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = double; ThresholdType = double; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&; size_type = long unsigned int]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = float; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_target_weightsbase_values_as_tensorAttribute:'' is specified but is empty.onnxruntime::common::Status onnxruntime::ml::GetNumberOfElementsAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, size_t&, onnx::TensorProto&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_helper.ccAttribute '' must be a vector.n_dims == 1Unexpected type ((for attribute 'proto.data_type() == proto_type' has one dimension but is empty.n_elements > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T* OrtValue::GetMutable() [with T = std::vector, float> >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::map, float>; _Alloc = std::allocator, float> >; reference = std::map, float>&; size_type = long unsigned int]T* OrtValue::GetMutable() [with T = std::vector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::map; _Alloc = std::allocator >; reference = std::map&; size_type = long unsigned int]N11onnxruntime9MaxUnpoolEN11onnxruntime2ml8ZipMapOpEN11onnxruntime2ml21TreeEnsembleRegressorIdEEN11onnxruntime2ml21TreeEnsembleRegressorIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMaxIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMaxIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMinIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMinIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorSumIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorSumIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_21TreeAggregatorAverageIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_21TreeAggregatorAverageIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_24TreeAggregatorClassifierIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_24TreeAggregatorClassifierIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMaxIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMaxIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_output_paddingoutput_shapeFlatten/onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccUnsupported pooling size.Index tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:spatialmomentumonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.hTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double]float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.hratio input should have a single value.ratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fgsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double]X and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double]float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float]virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)info.GetAttr("axis", &axis_).IsOK()onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = double]Invalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.ccoutput and sum shape must matchbool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*)/onnxruntime_src/onnxruntime/core/util/math_cpu.ccdims[d_i] < d_maxgroup count is <= 0 group: X num_dims does not match W num_dims. X: W: filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group.PrepareForCompute/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const*out_size >= 0void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, int64_t, onnxruntime::ConvAttributes::ConvPadVector*, onnxruntime::TensorShapeVector*, bool) constInvalid input shape: dim_size > 0DoConvTranspose/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccN11onnxruntime7FlattenEN11onnxruntime7DropoutIddEEN11onnxruntime7DropoutIdfEEN11onnxruntime7DropoutIfdEEN11onnxruntime7DropoutIffEEN11onnxruntime13ConvTransposeIfEEN11onnxruntime4ConvIfEEN11onnxruntime9BatchNormIdEEN11onnxruntime9BatchNormIfEEonnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()LpNormalizationInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.ccSize of X.shape()[axis:] == . Size of scale and bias (if provided) must match this. Got scale size of and bias size of ComputeImpl/onnxruntime_src/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cconnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cckernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size : onnxruntime::LpPoolV18::LpPoolV18(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.hN11onnxruntime13MaxPool3DTaskIhEEN11onnxruntime13MaxPool2DTaskIhEEN11onnxruntime13MaxPool1DTaskIhEEN11onnxruntime13MaxPool3DTaskIaEEN11onnxruntime13MaxPool2DTaskIaEEN11onnxruntime13MaxPool1DTaskIaEEN11onnxruntime13MaxPool3DTaskIdEEN11onnxruntime13MaxPool2DTaskIdEEN11onnxruntime13MaxPool1DTaskIdEEN11onnxruntime13MaxPool3DTaskIfEEN11onnxruntime13MaxPool2DTaskIfEEN11onnxruntime13MaxPool1DTaskIfEEN11onnxruntime9LpPoolV18IfEEN11onnxruntime4PoolIfNS_6LpPoolEEEN11onnxruntime9MaxPoolV8EN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime14AveragePoolV19IfEEN11onnxruntime8PoolBaseEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime3LRNIfEEN11onnxruntime8functors4PowxIfEEN11onnxruntime6LpNormIdEEN11onnxruntime6LpNormIfEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIdfEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIddEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIffEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime13LayerNormImplEN11onnxruntime9LayerNormEN11onnxruntime12InstanceNormIfEE  std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Alloc = std::allocator; reference = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr&]Null input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchUnsupported kernel dimension : std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; reference = onnxruntime::{anonymous}::PreCalc&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; reference = onnxruntime::{anonymous}::PreCalc&; size_type = long unsigned int]ShrinkTfIdfVectorizeronnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.ccR->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeRoiPool/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.hcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SelectedIndex; _Alloc = std::allocator; reference = onnxruntime::SelectedIndex&]void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Sequence = std::vector; _Compare = std::less]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_queue.hvirtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constoutput != nullptronnxruntime::Optional::Optional(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.hOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.ccmode is requiredTFIDFTFIDFmode: is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNonemin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0max_gram_lengthmin_gram_length >= max_gram_length required: >= impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0ngram_countsNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })No attribute with name: is defined.Attribute: expected to be of type: but is of type: Got weights of size: but ngram_indexes size: must be of equal sizeimpl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringsRequested attribute: is expected to have type: pool_strings must not be empty if specified!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for -gramsend_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = gsl::details::span_iterator; Map = std::unordered_map > >; size_t = long unsigned int]Duplicate ngram detected, size: id: p.first->second->id_ == 0size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::__cxx11::basic_string; ForwardIter = __gnu_cxx::__normal_iterator >*, std::vector > > >; Map = std::unordered_map >, std::unique_ptr > >, std::hash >, std::equal_to > >; size_t = long unsigned int]min_gram_lengthInput shape must have either [C] or [B,C] dimensions with B > 0.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::TfIdfVectorizer::Impl; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::TfIdfVectorizer::Impl&]onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hInvalid mode of value specified. It should be either avg or maxSampling ratio should be >=0, but it was sampling_ratio_ >= 0RoiAlignBaseThe existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.N11onnxruntime18OptionalGetElementEN11onnxruntime18OptionalHasElementEN11onnxruntime8OptionalE*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime8RoiAlignIdEEN11onnxruntime12RoiAlignBaseEN11onnxruntime8RoiAlignIfEEN11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionE*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE3_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE2_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE1_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE0_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE4_N11onnxruntime15TfIdfVectorizerEN11onnxruntime6ShrinkEN11onnxruntime7RoiPoolIfEEN11onnxruntime17AveragePool3DTaskIfEEN11onnxruntime17AveragePool2DTaskIfEEN11onnxruntime17AveragePool1DTaskIfEEN11onnxruntime10Pool3DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool2DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool1DTaskIfNS_6LpPoolEEEN11onnxruntime12LpPool3DTaskIfEEN11onnxruntime12LpPool2DTaskIfEEN11onnxruntime12LpPool1DTaskIfEE ConvIntegerT4PropagateInputOrtValueToFirstOutput/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableTrying to use OptionalGetElement on an optional type OrtValue which contains no datatypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::OpKernelInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::OpKernelInfo&]The TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/dynamicquantizelinear.ccx_ptr != nullptronnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = signed char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/qlinearconv.cconnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = unsigned char]void onnxruntime::PrepareForQDQ(const TensorShape&, const Tensor&, const Tensor*, int64_t, int64_t&, int64_t&, int64_t&)/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear.ccx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/conv_integer.ccMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = int; _Alloc = std::allocator; reference = int&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/matmul_integer.ccMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)MatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_GEMM_QUANT_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_GEMM_QUANT_DATA_PARAMS&; size_type = long unsigned int]static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = signed char; int64_t = long int; uint8_t = unsigned char]QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.W_zero_point_data[i] == W_zero_point_valuestatic std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = signed char; int64_t = long int]QLinearConv : input 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(Y_scale)QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = unsigned char; int64_t = long int; uint8_t = unsigned char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = unsigned char; int64_t = long int]N11onnxruntime16DequantizeLinearINS_14Float8E5M2FNUZEEEN11onnxruntime16DequantizeLinearINS_10Float8E5M2EEEN11onnxruntime16DequantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime16DequantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime16DequantizeLinearIiEEN11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIaEEZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime11QLinearConvIaEEN11onnxruntime11QLinearConvIhEEN11onnxruntime17MatMulIntegerBaseEN11onnxruntime13MatMulIntegerEZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime24GetQuantizationParameterIhLb0ELb0ELi0EEEvPKflRfRT_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime21DynamicQuantizeLinearIhEEN11onnxruntime11ConvIntegerE@`@p@QLinearMatMulonnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short int]Unsupported input type.onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short int]DequantizeLinear into BFLOAT16 is not implemented yet.DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]N11onnxruntime13QLinearMatMulEZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdItEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdItEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIsEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIsEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime14QuantizeLinearIsEEN11onnxruntime14QuantizeLinearItEEN11onnxruntime14QuantizeLinearINS_14Float8E5M2FNUZEEEN11onnxruntime14QuantizeLinearINS_10Float8E5M2EEEN11onnxruntime14QuantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime14QuantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearIaEEN11onnxruntime16DequantizeLinearIsEEN11onnxruntime16DequantizeLinearItEE7777::::;;;;@`@p@p@`@p@virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&; size_type = long unsigned int]N11onnxruntime10ReduceMeanIiEEN11onnxruntime10ReduceMeanIfEEN11onnxruntime9ReduceMaxIbEEN11onnxruntime9ReduceMaxIhEEN11onnxruntime9ReduceMaxIaEEN11onnxruntime9ReduceMaxIdEEN11onnxruntime9ReduceMaxIlEEN11onnxruntime9ReduceMaxIiEEN11onnxruntime9ReduceMaxIfEEN11onnxruntime15ReduceLogSumExpIlEEN11onnxruntime15ReduceLogSumExpIdEEN11onnxruntime15ReduceLogSumExpIiEEN11onnxruntime15ReduceLogSumExpIfEEN11onnxruntime12ReduceLogSumIlEEN11onnxruntime12ReduceLogSumIiEEN11onnxruntime12ReduceLogSumIfEEN11onnxruntime8ReduceL2IlEEN11onnxruntime8ReduceL2IiEEN11onnxruntime8ReduceL2IfEEN11onnxruntime8ReduceL1IlEEN11onnxruntime8ReduceL1IiEEN11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime12ReduceKernelILb1EEEN11onnxruntime8ReduceL1IfEE27MLAS_QGEMM_OUTPUT_PROCESSOR35MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR N11onnxruntime6ArgMinIiEEN11onnxruntime6ArgMinIfEEN11onnxruntime6ArgMaxIdEEN11onnxruntime6ArgMaxIhEEN11onnxruntime6ArgMaxIaEEN11onnxruntime6ArgMaxIiEEN11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime12ReduceKernelILb0EEEN11onnxruntime6ArgMaxIfEEN11onnxruntime15ReduceSumSquareIlEEN11onnxruntime15ReduceSumSquareIdEEN11onnxruntime15ReduceSumSquareIiEEN11onnxruntime15ReduceSumSquareIfEEN11onnxruntime9ReduceSumIdEEN11onnxruntime9ReduceSumIlEEN11onnxruntime9ReduceSumIiEEN11onnxruntime9ReduceSumIfEEN11onnxruntime10ReduceProdIlEEN11onnxruntime10ReduceProdIiEEN11onnxruntime10ReduceProdIfEEN11onnxruntime9ReduceMinIbEEN11onnxruntime9ReduceMinIhEEN11onnxruntime9ReduceMinIaEEN11onnxruntime9ReduceMinIdEEN11onnxruntime9ReduceMinIlEEN11onnxruntime9ReduceMinIiEEN11onnxruntime9ReduceMinIfEEN11onnxruntime10ReduceMeanIdEEvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cclast_loop_red_size > 0last_loop_size > 0projected_index.size() > 0void onnxruntime::ValidateMustBeOverloaded()must be overloaded.void onnxruntime::ValidateFastReduceKR(const gsl::span&, const Tensor&)Only works on matrices with two dimensions.fast_shape.size() == 2Output size mismatch.fast_shape[0] == output.Shape().Size()void onnxruntime::ValidateFastReduceRK(const gsl::span&, const Tensor&)fast_shape[1] == output.Shape().Size()void onnxruntime::ValidateFastReduceKRK(const gsl::span&, const Tensor&)Only works on matrices with three dimensions.fast_shape.size() == 3fast_shape[0] * fast_shape[2] == output.Shape().Size()void onnxruntime::ValidateFastReduceRKR(const gsl::span&, const Tensor&)void onnxruntime::ValidateNoTransposeReduce(int64_t)Reduction on all axes, output size should be 1.count == 1void onnxruntime::ValidateKeepDims(const TensorShape&, int64_t)Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:bool onnxruntime::CommonFastReduceCopy(OpKernelContext*, TensorShapeVector&, bool)An axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]Axes input and attribute should not both be present for reduction.axes.empty()bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime6ArgMinIdEEbool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]ZN11onnxruntime16ReduceAggregatorIllE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFlPS7_EESF_IFvRlSG_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIddE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFdPKdEESF_IFvRdSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIiiE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFiPKiEESF_IFvRiSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIffE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFfPKfEESF_IFvRfSH_lEEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRbPKblE0_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKbE_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRbPKblE0_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKbE_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIbEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIbEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_PrePack/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccInput X must have 3 dimensions only. Actual:Input W must have shape {*}. Actual:Input R must have shape {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 initial_c must have shape {Input P must have shape {gsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hCan not get unpacked span from prepacked weights!is_prepacked_const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]cur + size <= endT* onnxruntime::rnn::detail::SafeRawPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]offset + size <= size_t(span.size())T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]forwardbidirectionalcliponnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betainfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.fonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string&)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.sigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.hactivation_func_names.size() == static_cast(num_directions_) * 3onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*)/onnxruntime_src/onnxruntime/core/common/threadpool.ccNested parallelism not supported!current_parallel_section.has_value()!ps_void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = gsl::details::span_iterator; TSpanBIter = gsl::details::span_iterator; TSpanCIter = gsl::details::span_iterator]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_end/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; const_reference = const onnxruntime::rnn::detail::ActivationFuncs::Entry&; size_type = long unsigned int]GRU operator does not support double yetvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constInvalid data type for GRU operator of /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.ccLSTM operator does not support double yetvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) constInvalid data type for LSTM operator of /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.ccFffffEPFffffEN11onnxruntime3RNNIfEEN11onnxruntime8LSTMBaseEN11onnxruntime13DeepCpuLstmOpEZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPiE_N11onnxruntime12DeepCpuGruOpEZN11onnxruntime16ReduceAggregatorIaaE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFaPKaEESF_IFvRaSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIhhE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFhPKhEESF_IFvRhSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIbbE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFbPKbEESF_IFvRbSH_lEEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string&, std::vector::const_iterator&, const std::vector::const_iterator&, std::vector::const_iterator&, const std::vector::const_iterator&, float&, float&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; reference = onnxruntime::rnn::detail::ActivationFuncs::Entry&]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)A + (M * K) <= A_endweights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)reluaffineleakyreluthresholdedreluscaledtanhhardsigmoidsoftsignsoftplusvoid (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string&))(float*, int, float, float)Invalid activation function of void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string&))(const float*, float*, const float*, float*, int, float, float)Invalid LSTM merge activation function of onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.hMust have valid 'axis' attributenew_axisvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccGot nullptr for sequence input.int64_t onnxruntime::GetSeqIdx(const Tensor&)/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) constUnsupported 'dtype' value: zsleOHA:3,% Invalid sequence index () specified for sequence of size (T onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/utils.htensor->Shape().Size() == 1Unsupported input data type of ConcatFromSequenceSequenceLengthSequenceAtSequenceEmptySequenceInsertSequenceEraseSequenceConstructSplitToSequenceonnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = float; U = float]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/dft.ccsignal shape must end in 1 (real) or 2 (real, imaginary).signal_components == 1 || signal_components == 2If both frame_length and window are set, then the size of the window must be equal to the frame_length.frame_length == window_lengthEnsure that the dft size is smaller than the signal.window_size < signal_sizeshort_time_fourier_transformonnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = float; U = std::complex]virtual onnxruntime::common::Status onnxruntime::STFT::Compute(onnxruntime::OpKernelContext*) constUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = double; U = double]onnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = double; U = std::complex]CloneTensoronnxruntime::Tensor onnxruntime::CloneTensor(const Tensor&, OpKernelContext*, const DataTransferManager&)Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.int64_t onnxruntime::GetScalarSplitInput(const Tensor&)Invalid data type for split tensor onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) constSplit should be > 0split_scalar > 0void onnxruntime::GetSplitSizesInput(const Tensor&, InlinedVector&)Invalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })split_size_sum () != split_dim_size (discrete_fourier_transformdft_length must be a scalar value.dft_length must be greater than zero.onnxruntime::common::Status onnxruntime::discrete_fourier_transform(OpKernelContext*, int64_t, bool, bool)Unsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.N11onnxruntime4STFTEN11onnxruntime3DFTEN11onnxruntime15SplitToSequenceEN11onnxruntime17SequenceConstructEN11onnxruntime13SequenceEraseEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEmptyEN11onnxruntime10SequenceAtEN11onnxruntime14SequenceLengthEN11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceEZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPhE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIhEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIfEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;T onnxruntime::bit_reverse(T, unsigned int) [with T = long unsigned int]Unsupported bit size.dft_bluestein_z_chirpT onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = float]onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccAttribute to is not set.Attribute saturate is only used for cast to float 8 types.AffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5.*N11onnxruntime12_GLOBAL__N_14CastEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_ZNK11onnxruntime10AffineGridIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10AffineGridIfE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime10AffineGridIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10AffineGridIdE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime10AffineGridIdEEN11onnxruntime10AffineGridIfEEN11onnxruntime15MelWeightMatrixEN11onnxruntime14BlackmanWindowEN11onnxruntime13HammingWindowEN11onnxruntime26VariableOutputDataTypeBaseEN11onnxruntime10HannWindowE@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?-DT!@-DT!-DT! @onnxruntime::Col2Im::Col2Im(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.hstrides_.empty()dilations_.empty()pads_.empty()onnxruntime::StridedCopy >(concurrency::ThreadPool*, std::__cxx11::basic_string*, const TensorShapeVector&, const TensorShape&, const std::__cxx11::basic_string*, const TensorShapeVector&)::/onnxruntime_src/onnxruntime/core/framework/copy.hcounter.current_offset == lastvoid onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = std::__cxx11::basic_string; TensorShapeVector = absl::lts_20240116::InlinedVector >]src and dst must have same shape and not be rank 0.dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()copy shape must have non-negative sizetotal_num_elements_to_copy >= 0onnxruntime::StridedCopy(concurrency::ThreadPool*, unsigned int*, const TensorShapeVector&, const TensorShape&, const unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, long unsigned int*, const TensorShapeVector&, const TensorShape&, const long unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = long unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, short unsigned int*, const TensorShapeVector&, const TensorShape&, const short unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = short unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, unsigned char*, const TensorShapeVector&, const TensorShape&, const unsigned char*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned char; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::common::Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool*, Tensor&, std::ptrdiff_t, const TensorShapeVector&, const TensorShape&, const Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = TypeList, std::allocator > >; std::ptrdiff_t = long int; TensorShapeVector = absl::lts_20240116::InlinedVector >]src and dst types must matchdst.DataType() == src.DataType()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cclast >= firststoullCol2Imonnxruntime::common::Status onnxruntime::Col2Im::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.ccsize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.dilations_.size() == image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.strides_.size() == image_dim_numbervirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()invalid expand shapetypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long int&; std::size_t = long unsigned int]N11onnxruntime6ExpandINS_9MLFloat16EEEN11onnxruntime6ExpandIbEEN11onnxruntime6ExpandImEEN11onnxruntime6ExpandIjEEN11onnxruntime6ExpandItEEN11onnxruntime6ExpandIhEEN11onnxruntime6ExpandIlEEN11onnxruntime6ExpandIiEEN11onnxruntime6ExpandIsEEN11onnxruntime6ExpandIaEEN11onnxruntime6ExpandIdEEN11onnxruntime6ExpandIfEEZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKSA_SI_EUlllE0_ZN11onnxruntime11StridedCopyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKSA_SI_EUlllE_N11onnxruntime6ConcatEN11onnxruntime8CompressEN11onnxruntime6Col2ImIfEE @@@@?@@? @ @?@@????GatherElementsEyeLike : Input tensor dimension is not 2onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.hMissing/Invalid 'axis' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccindices element out of data bounds, idx= must be within the inclusive range [Gather Tind type not supported in this build.onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = int; int64_t = long int; size_t = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccvoid onnxruntime::core_impl(const Tensor*, const Tensor*, Tensor*, int64_t, concurrency::ThreadPool*) [with Tin = int; int64_t = long int]GatherElements op: Unsupported tensor type, size:GatherElements op: Out of range value in index tensorint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = long int; int64_t = long int; size_t = long unsigned int]void onnxruntime::core_impl(const Tensor*, const Tensor*, Tensor*, int64_t, concurrency::ThreadPool*) [with Tin = long int; int64_t = long int]GatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEKSK_EEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEKSK_EEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_N11onnxruntime14GatherElementsEZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_N11onnxruntime10GatherBaseEN11onnxruntime6GatherEN11onnxruntime7EyeLikeEZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_cubiconnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.h" not supported, expect linear, nearest or cubicbilinearbicubic" not supported, expect bilinear, nearest or bicubicpadding_modereflectionpadding_mode "" not supported, expect zeros, border or reflectiononnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = double]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.ccgrid dimensions must be for input dimension of static_cast(grid_dims.NumDimensions()) == data_dims + 2Last dimension of grid: , expect grid_dims[grid_dims.NumDimensions() - 1] == data_dimsOnly 4-D or 5-D tensor is supportedinput_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5Grid batch size does not match input batch size grid_dims[0] == NOnly support GridSample Cubic mode in 4-D cases.mode_ != CubicOnly support GirdSample in 4-D or 5-D cases.onnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_positiveFailed to obtain detect_negativeNull input ptrvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorindices tensor must has rank larger than 0invalid index found, index = indices tensor data type not supported/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]Unable to get an allocatoronnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]N11onnxruntime25MeanVarianceNormalizationEN11onnxruntime5IsNaNINS_14Float8E5M2FNUZEEEN11onnxruntime5IsNaNINS_10Float8E5M2EEEN11onnxruntime5IsNaNINS_14Float8E4M3FNUZEEEN11onnxruntime5IsNaNINS_12Float8E4M3FNEEEN11onnxruntime5IsNaNINS_9MLFloat16EEEN11onnxruntime5IsNaNIdEEN11onnxruntime5IsNaNIfEEN11onnxruntime5IsInfEN11onnxruntime10IdentityOpILb0EEEN11onnxruntime10IdentityOpILb1EEEZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime10GridSampleIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10GridSampleIdE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime10GridSampleIdEEN11onnxruntime10GridSampleIfEEZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_N11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE  Invalid 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 elements/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.ccNo valid axes are specified. This is not handled now.constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = absl::lts_20240116::InlinedVector >; _Dp = std::_Optional_base >, false, false>]ComputeMeanVarianceNormalization2DX and Y must both have M * N elements.onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccX input is required!failed to get first output!onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = bool]OneHotN11onnxruntime8OneHotOpIlfiEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime8OneHotOpIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlllEEN11onnxruntime7NonZeroIfEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIbEE@std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockScratchAllocator::Allocation; _Alloc = std::allocator::Allocation>; reference = Eigen::internal::TensorBlockScratchAllocator::Allocation&; size_type = long unsigned int]reflectedgewraponnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.hInvalid 'mode' attribute valueInvalid 'pads' attribute valueonnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hAttribute shape is not set.onnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = unsigned int; PadsVector = absl::lts_20240116::InlinedVector >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.ccInput tensor has no dimensionsdata_rank > 0'pads' has wrong number of valuesdata_rank * 2 == pads.size()PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = long unsigned int; PadsVector = absl::lts_20240116::InlinedVector >]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) constPads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Axes tensor should be a 1D tensor axes_tensor_dims.size() == 1Pads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = unsigned char; PadsVector = absl::lts_20240116::InlinedVector >]virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1Resize operatorUpsample operatorScalesValidation/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsamplebase.hScale value should be greater than or equal to 1.Scale value should be greater than 0.'Linear' mode only supports: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the ParseScalesDatascales size should be greater than 0.all values in axes should be less than rank of the dataonnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&)info.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string&)mode attribute is . It can only be (default) or antialiaswhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)UpsampleBasekeep_aspect_ratio_policystretchnot_largernot_smalleronnxruntime::AspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const std::string&)keep_aspect_ratio of [] is not supported!extrapolation_valueasymmetricpytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetriconnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string&)coordinate_transform_mode:[`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attributenearest_moderound_prefer_floorround_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string&)nearest_mode:[cubic_coeff_aexclude_outsideexclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to and anti-aliasing is set to N11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEN11onnxruntime8UpsampleIaEEN11onnxruntime6ResizeIaEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime7ReshapeEN11onnxruntime9Reshape_1EN11onnxruntime7PadBaseEN11onnxruntime3PadE    addmulScatteronnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()info.GetAttr("time_axis", &time_axis).IsOK()Invalid batch_axis of . Must be 0 or 1batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccnoneInvalid sequence length: . Value must be in range [0,gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]sequence_lens shape must be {batch_size}. Got:. batch_size=gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccUnknown tensor type of nJ&j,input tensor and indices tensor must has rank larger than 0. , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: ScatterDataScatterElements op: input tensor must have at least one dimensionCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.data type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=Indices type is not supported.N11onnxruntime9ScatterNDEN11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEEEEN11onnxruntime17ReverseSequenceOpEonnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*)Unexpected element size of onnxruntime::SliceImpl >(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&):: >&)>/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccoutput == output_endonnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>'axes' has an axis outside of the tensor dimension count'axes' has duplicatesCannot slice scalarsFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got 'step' value cannot be 0N11onnxruntime7Slice10EN11onnxruntime9SliceBaseEN11onnxruntime6Slice1EN11onnxruntime4SizeEN11onnxruntime5ShapeEZNK11onnxruntime23ScatterNDDispatchTargetINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIbEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_8BFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_9MLFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIhEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIaEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetItEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIsEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIjEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIiEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetImEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIlEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIdEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIfEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_SpaceDepth ops require a 4-D input. Provided rank: SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_size/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in SpaceToDepth op: DepthToSpace requires input depth to be a multiple of (block_size * blok_size)Unsupported input type in DepthToSpace op: blocksizeonnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&)DepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&, uint32_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })If 'num_outputs' is specified, the 'split' input should not be provided.onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.onnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.hinfo.GetAttr("upper", &temp).IsOK()virtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.cck should be a 1-D or 0-D tensor.IsScalarOr1ElementVector(k)onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>Input count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.ccTile doesn't have an implementation yet for the type: void onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const std::string*, std::string*)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.ccTranspose not implemented for empty tensors.num_axes > 0(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span&, const std::string*, std::string*)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]Transpose of element size not supported in this build. Size=void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span&, const uint8_t*, uint8_t*, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constperm size: does not match input rank: [ perm: does not align with rank of input data: TileUniquevirtual onnxruntime::common::Status onnxruntime::SplitImpl::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccThe split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1Invalid num_outputs value of . Size of dimension being split is Input cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hAxes input is nullaxes_tensor != nullptrstatic onnxruntime::TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShapeVector&)Dimension of input must be 1 instead of . shape=input_shape[i] == 1Mismatched data types between input and output Tensors. N11onnxruntime6UniqueEN11onnxruntime5TriluEN11onnxruntime13TransposeBaseEN11onnxruntime9TransposeEN11onnxruntime4TileEN11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeEN11onnxruntime8Split_18EN11onnxruntime10Split_1_13EN11onnxruntime9SplitBaseEN11onnxruntime9SplitImplEN11onnxruntime12DepthToSpaceEN11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthEstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = long int; _Alloc = std::allocator; const_reference = const long int&]onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()Unsupported tensor type of onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, Prepare&) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensorParseSizesDataResize: input tensor's rank does not match the output tensor's rank.axes should be less than output_dims.size()ParseScalesDataAndAdjustOutputSizeInput dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccInvalid roi input index.Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.N11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEconstexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = float; long unsigned int _Nm = 4; reference = float&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/arrayResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.Resize: unexpected modeUpsample: unexpected modeBaseComputeRank of input and output tensor should be same.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.UpsampleNearest4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IAllocator; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::IAllocator]N11onnxruntime23BilinearParamsAntiAliasIiEEN11onnxruntime24TriLinearParamsAntiAliasIfEEN11onnxruntime21FilterParamsAntiAliasIfEEN11onnxruntime22BiCubicParamsAntiAliasIfEEN11onnxruntime24TriLinearParamsAntiAliasIiEEN11onnxruntime21FilterParamsAntiAliasIiEEN11onnxruntime22BiCubicParamsAntiAliasIiEE@@ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIfEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIiEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIaEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIaEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIhEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_N11onnxruntime23BilinearParamsAntiAliasIfEE5555WCHAR_TInput contains invalid utf8 charsSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedonnxruntime::string_normalizer::Locale::Locale(const std::string&)::/onnxruntime_src/onnxruntime/core/providers/cpu/text/string_normalizer.ccFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesonnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)attribute is_case_sensitive is not setattribute case_change_action is not setLOWERUPPERattribute case_change_action has invalid valueEmpty stopwords not allowed!sw.empty()Duplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_errorGetAttr/onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.hT onnxruntime::OpNodeProtoHelper::GetAttr(const std::string&) const [with T = std::__cxx11::basic_string; Impl_t = onnxruntime::ProtoHelperNodeContext; std::string = std::__cxx11::basic_string]onnxruntime::RegexFullMatch::RegexFullMatch(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/text/regex_full_match.ccInvalid regex pattern: re_.ok()/build/Release/_deps/re2-src/re2/re2.ccInvalid RE2: N11onnxruntime16StringNormalizerEN11onnxruntime12StringConcatEN11onnxruntime14RegexFullMatchEN11onnxruntime5WhereINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5WhereIdEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIhEEZN11onnxruntime28ComputeInterpolationAtLevel1IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIffEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIifEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIaiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIaEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPaE_ZN11onnxruntime19HandleExtrapolationIhiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_unimplemented activation: activation_params count mismatchCropAndResizeDynamicSliceExpandDimsTransposeMatMulSparseToDenseMatMul__pos < this->_M_lenconstexpr const std::basic_string_view<_CharT, _Traits>::value_type& std::basic_string_view<_CharT, _Traits>::operator[](size_type) const [with _CharT = char; _Traits = std::char_traits; const_reference = const char&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/string_viewbasic_string_view::substr%s: __pos (which is %zu) > __size (which is %zu)Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hNull crop_size_ptrNumber of dimensions for crop size should be exactly 1Bias size () does not match the number of channels (onnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearestonnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo&) [with T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.hAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()Axis must be within range [ Axis is axis <= X_NumDims && axis >= -X_NumDims/onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccCreate/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.ccNo attribute with name:''is defined.Attribute name and type don't match for '/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.hgamma/onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.hunknown kernel type/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib19SparseToDenseMatMulEN11onnxruntime7contrib9LayerNormILb1EEEN11onnxruntime7contrib9LayerNormILb0EEE*ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib7InverseEN11onnxruntime7contrib11ImageScalerIfEEN11onnxruntime8functors10ScaledTanhIfEEN11onnxruntime8functors18ParametricSoftplusIfEEN11onnxruntime8functors4SeluIfEEN11onnxruntime8functors15ThresholdedReluIfEEN11onnxruntime8functors4TanhIfEEN11onnxruntime8functors8SoftsignIfEEN11onnxruntime8functors7SigmoidIfEEN11onnxruntime8functors4ReluIfEEN11onnxruntime8functors8SoftplusIfEEN11onnxruntime8functors9LeakyReluIfEEN11onnxruntime8functors11HardSigmoidIfEEN11onnxruntime8functors3EluIfEEN11onnxruntime8functors4CeluIfEEN11onnxruntime7contrib9FusedGemmIfEEN11onnxruntime7contrib14FusedConvFloatEN11onnxruntime7contrib10ExpandDimsEN11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKiPS1_EUllE_EEvSM_lOS8_lEUllE_N11onnxruntime7contrib13CropAndResizeIfEEN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEEN11onnxruntime11StringSplitE<<blk_quant_type/onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/math/sparse_dense_matmul.ccCurrently supporting only 2-D matricesCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: Expecting COO 2-D indices shapeExpecting 2xValues == indicesCOO indices must be 2-D, got: COO k index: is out of bounds of lhs_right: COO m index: is out of bounds of out_left: Expecting the same number NNZ == size of Inner indicesOuter size must be M + 1Currently support only COO and CSR(x64) formatsvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cckeysInvalid assumption of output element sizesizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.hinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0do_rotaryrotary_embedding_dimqkv_hidden_sizesvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulFpQ4::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_fpq4.ccSecond input of MatMulFpQ4 must be a 1D blob!blob_shape.NumDimensions() == 1Right hand side of MatMulFpQ4 must be a 2D matrix!b_shape.NumDimensions() == 2Operator MatMulFpQ4 not yet supported on this hardware platform.buf_size > 0Quantized and packed blob size differ from expected!(size_t)blob_len == buf_sizestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_Q4_GEMM_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_Q4_GEMM_DATA_PARAMS&; size_type = long unsigned int]MatMulFpQ4MaxpoolWithMaskMurmurHash3QAttentionApplyAttention/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.honnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.ccExpect to have present state output when past state input is given4D mask in attention cpu kernel is not supported/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&; size_type = long unsigned int]ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolEEUlllE_ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE0_ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE_N11onnxruntime7contrib13AttentionBaseEN11onnxruntime7contrib16AttentionCPUBaseEN11onnxruntime7contrib10QAttentionIfEEN11onnxruntime7contrib11MurmurHash3EN11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEEN11onnxruntime7contrib15MaxpoolWithMaskEN11onnxruntime7contrib10MatMulFpQ4E/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccInput W_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:W_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight point must be constantRecurrentinput index out of range/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qembed_layer_norm.ccDynamicQuantizeLSTMMatMulBnb4MatMulInteger16MatMulNBitsQEmbedLayerNormalizationQLinearLeakyRelu/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.ccconstexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = {anonymous}::Operations; long unsigned int _Nm = 2; const_reference = const {anonymous}::Operations&; size_type = long unsigned int]ComputeCommon/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.cconnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext*, const uint8_t*, const onnxruntime::TensorShape&, float, uint8_t, bool, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*) constMatmulInteger : b zero point is not validIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) constMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)onnxruntime::contrib::MatMulBnb4::MatMulBnb4(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_bnb4.ccStatus::OK() == info.GetAttr("K", &K_)Status::OK() == info.GetAttr("N", &N_)block_sizeStatus::OK() == info.GetAttr("block_size", &block_size_)quant_typeStatus::OK() == info.GetAttr("quant_type", &quant_type_)Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.quant_type_ == FP4 || quant_type_ == NF4void onnxruntime::contrib::DequantizeBlockwiseBnb4(T*, const uint8_t*, const T*, int32_t, int32_t, int32_t, int32_t, onnxruntime::concurrency::ThreadPool*) [with T = float; uint8_t = unsigned char; int32_t = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dequantize_blockwise_bnb4.hquant_type == FP4 || quant_type == NF4only block size 16, 32, 64, 128, 256 are supported.onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_integer16.ccA != nullptr && B != nullptrT onnxruntime::OpNodeProtoHelper::GetAttr(const std::string&) const [with T = long int; Impl_t = onnxruntime::ProtoHelperNodeContext; std::string = std::__cxx11::basic_string]accuracy_levelonnxruntime::contrib::MatMulNBits::MatMulNBits(const onnxruntime::OpKernelInfo&)Only 4b quantization is supported for MatMulNBits op, additional bits support is planned.nbits_ == 4/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.cconnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]N11onnxruntime7contrib17QLinearLookupBaseIhEEN11onnxruntime7contrib16QLinearLeakyReluIhEEN11onnxruntime7contrib17QLinearLookupBaseIaEEN11onnxruntime7contrib16QLinearLeakyReluIaEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfhEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfaEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib18EmbedLayerNormBaseEN11onnxruntime7contrib15QEmbedLayerNormIfEEN11onnxruntime7contrib11NhwcMaxPoolIhEEN11onnxruntime7contrib11NhwcMaxPoolIaEEZN11onnxruntime10IAllocator13MakeUniquePtrISt4byteEESt10unique_ptrIT_St8functionIFvPS4_EEESt10shared_ptrIS0_EmbPNS_6StreamES5_IFvRSC_RNS_11synchronize12NotificationEEEEUlPS2_E_N11onnxruntime7contrib11MatMulNBitsEN11onnxruntime7contrib15MatMulInteger16IssiEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi256ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi256ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi128ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi128ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi64ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi64ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi32ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi32ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi16ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi16ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_N11onnxruntime7contrib10MatMulBnb4EN11onnxruntime7contrib20MatMulIntegerToFloatEN11onnxruntime7contrib24MatMulIntegerToFloatBaseEN11onnxruntime7contrib21DynamicQuantizeMatMulEN11onnxruntime7contrib19DynamicQuantizeLSTME920k2ʾM?5=qx=$>|>:>>?9??;*??>?*>>**QLinearSigmoidQLinearConcatvoid onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; LookupTableArrayTransformer = std::function]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_lookup_table.ccQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; LookupTableArrayTransformer = std::function]onnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.ccEach input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @tensor_x_scale->IsDataType()Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext*) constinput_count_x3 >= 3 && input_count_x3 % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_global_average_pool.ccInput x_scale must be a scalar or 1D tensor of size 1input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_pool.ccQLinear Pooling unsupported pooling size!onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]onnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_softmax.ccopset must be existed in attributes of QlinearSoftmaxvoid onnxruntime::contrib::{anonymous}::BuildLookupTableIfFixed(const onnxruntime::OpKernelInfo&, std::vector&, size_t, bool)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)N11onnxruntime7contrib14QLinearSoftmaxEZN11onnxruntime7contrib16dequantize_arrayIhEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib16dequantize_arrayIaEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib18QLinearAveragePoolEZN11onnxruntime7contrib23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib24QLinearGlobalAveragePoolE*ZNK11onnxruntime7contrib13QLinearConcat7ComputeEPNS_15OpKernelContextEEUlfE_*ZN11onnxruntime7contrib13QLinearConcatC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib13QLinearConcatE*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_N11onnxruntime7contrib10QLinearMulIhEEN11onnxruntime7contrib10QLinearMulIaEEN11onnxruntime7contrib10QLinearAddIhEEN11onnxruntime7contrib10QLinearAddIaEEZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIhEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIaEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib16QLinearLeakyReluIhEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib16QLinearLeakyReluIaEC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib14QLinearSigmoidIhEEN11onnxruntime7contrib14QLinearSigmoidIaEE???decoder_output_cross_qkskip is expected to have 3 or 2 dimensions, got skip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsinput is expected to have 3 or 2 dimensions, got last two dimensions of skip needs to be same as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not match/onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.ccNode input should be a scalar. Got shape of QLinearSoftmaxQLinearWhereTATBTCTYZTYSampleOpSkipSimplifiedLayerNormalizationTokenizerBeamSearchWhisperBeamSearchonnxruntime::contrib::QLinearWhere::QLinearWhere(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_where.ccThere must be inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)input_def_count == kExpected_input_countWrong input type encountered for zero point input def of x, y, ztensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false]epsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true]void onnxruntime::contrib::transformers::BeamSearch::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search.ccinit_decoderparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperencoderinfo.GetAttr("encoder", &proto).IsOK()decoderinfo.GetAttr("decoder", &proto).IsOK()virtual onnxruntime::common::Status onnxruntime::contrib::QLinearWhere::Compute(onnxruntime::OpKernelContext*) constInput scale is not float for quantized input x @ 2Input scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_z_scale->IsDataType()virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)SetupSubgraphExecutionInfo should only be called once for each subgraph.gpt_subgraph_ == nullptrinit_run_gpt_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrSetupSubgraphExecutionInfoEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrwhisper_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_decoder_subgraph_ == nullptrCheckInputs/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point and scale of input b should have same shape sizeQGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : scale of y must be null or a scalar or 1D tensor of size 1tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedInput string contains invalid utf8 charsstd::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::operator[](size_type) const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&; size_type = long unsigned int]Input string contains invalid utf8 chars: Match contains invalid utf8 chars: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]markonnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute mark is not setpad_valueattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valuemincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Can not digest tokenexp: N11onnxruntime7contrib12transformers22WhisperEncoderSubgraphEN11onnxruntime7contrib12transformers22WhisperDecoderSubgraphEN11onnxruntime7contrib12transformers17WhisperBeamSearchEN11onnxruntime7contrib12transformers27WhisperBeamSearchParametersEN11onnxruntime7contrib12transformers21IGenerationParametersEN11onnxruntime7contrib12transformers20BeamSearchParametersEN11onnxruntime7contrib12transformers10ISequencesEN11onnxruntime7contrib12transformers9SequencesEN11onnxruntime7contrib12transformers20ILogitsProcessorListEN11onnxruntime7contrib12transformers14IConsoleDumperEN11onnxruntime7contrib12transformers19LogitsProcessorListEN11onnxruntime7contrib12transformers22CpuTensorConsoleDumperEN11onnxruntime7contrib12transformers12GenerateBaseEN11onnxruntime11controlflow18IControlFlowKernelEN11onnxruntime7contrib12transformers10BeamSearchEN11onnxruntime7contrib12transformers17T5EncoderSubgraphEN11onnxruntime7contrib12transformers8SubgraphEN11onnxruntime7contrib12transformers17T5DecoderSubgraphEN11onnxruntime7contrib9TokenizerEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib13SkipLayerNormIdLb1EEEN11onnxruntime7contrib13SkipLayerNormIdLb0EEEN11onnxruntime7contrib13SkipLayerNormIfLb1EEEN11onnxruntime7contrib13SkipLayerNormIfLb0EEEN11onnxruntime7contrib8SampleOpIfEEN11onnxruntime7contrib5QGemmE*ZNK11onnxruntime7contrib12QLinearWhere7ComputeEPNS_15OpKernelContextEEUlfE_*ZN11onnxruntime7contrib12QLinearWhereC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib12QLinearWhereE*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIaEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIhEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_base.hGenerateNextTokenmin_lengthmax_lengthnum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.Execute/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_t5.hparameters->sequence_length == 1onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_whisper.hno_speech_token_id is out of range, it is , vocab_size is parameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizedecoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiondecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with past_present_share_bufferdecoder_subgraph_.past_present_share_buffer_Must specify input cross_qk_layer_headinput_tensor_cross_qk_layer_head != nullptronnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::Compute(onnxruntime::OpKernelContext*) constSubgraph SessionState was not found for 'decoder' attribute.decoder_session_stateCreateFeedsFetchesManager must be called prior to execution of graph.decoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::BeamSearchParameters; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::BeamSearchParameters&]Subgraph SessionState was not found for 'encoder' attribute.encoder_session_stateencoder_feeds_fetches_manager_typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::T5EncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::T5EncoderSubgraph&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::WhisperEncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::WhisperEncoderSubgraph&]Model type is not supported.N11onnxruntime7contrib12transformers11IBeamScorerEN11onnxruntime7contrib12transformers16BeamSearchScorerEFN11onnxruntime6common6StatusEPNS_6StreamEiiiiiiPKiiPKfPfiS5_N3gsl4spanIS4_Lm18446744073709551615EEEEPFN11onnxruntime6common6StatusEPNS_6StreamEiiiiiiPKiiPKfPfiS5_N3gsl4spanIS4_Lm18446744073709551615EEEEFN11onnxruntime6common6StatusEiPNS_6StreamEP8OrtValueRSt10unique_ptrIPfSt8functionIFvPS7_EEEiiPKiS7_iSt10shared_ptrINS_10IAllocatorEEEPFN11onnxruntime6common6StatusEiPNS_6StreamEP8OrtValueRSt10unique_ptrIPfSt8functionIFvPS7_EEEiiPKiS7_iSt10shared_ptrINS_10IAllocatorEEEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_EFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEPFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEPFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EPFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EFN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEPFN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEN11onnxruntime7contrib12transformers11GptSubgraphEN11onnxruntime7contrib12transformers14BeamSearchBaseIfEEN11onnxruntime7contrib12transformers14BeamSearchBaseINS_9MLFloat16EEEN11onnxruntime7contrib12transformers13BeamSearchGptIfEEN11onnxruntime7contrib12transformers13BeamSearchGptINS_9MLFloat16EEEN11onnxruntime7contrib12transformers12BeamSearchT5IfEEN11onnxruntime7contrib12transformers12BeamSearchT5INS_9MLFloat16EEEN11onnxruntime7contrib12transformers17BeamSearchWhisperIfEEN11onnxruntime7contrib12transformers17BeamSearchWhisperINS_9MLFloat16EEEmodel_typeearly_stoppingeos_token_idpad_token_iddecoder_start_token_idno_repeat_ngram_sizevocab_sizeCPU beam search current not support output cross QK.virtual void onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_parameters.ccmodel_type == IGenerationParameters::kModelTypeWhispertranslate_token_idtranscribe_token_idstart_of_lm_token_idno_speech_token_idno_timestamps_token_idbeginning_timestamp_token_idvirtual void onnxruntime::contrib::transformers::BeamSearchScorer::Process(onnxruntime::contrib::transformers::ISequences&, gsl::span&, gsl::span&, gsl::span&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_scorer.ccnext_scores.size() == next_tokens.size()next_scores.size() == next_indices.size()Batch can only be done if all beams have been generatedbeam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)beam_idx == num_beams_static_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()void onnxruntime::contrib::transformers::GreedySearch::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search.ccparameters_.model_type == IGenerationParameters::kModelTypeGptvoid onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)input_features shall have 3 dimensions. Got dims.size() == 3decoder_input_ids shall have 2 dimensions. Got decoder_dims.size() == 2extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2extra_decoder_tensor first dim not same as batch_size. Got , expecting extra_decoder_tensor_dims[0] == batch_sizemax_length () shall be greater than input sequence length (max_length > sequence_length) shall be no more than max_length <= kMaxSequenceLengthnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got num_return_sequences >= 1num_return_sequences () shall be be no more than num_beams (num_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got repetition_penalty > 0.0flogits_processor shall be a non-negative integer, got logits_processor >= 0virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(onnxruntime::contrib::transformers::ISequences&, gsl::span&, onnxruntime::Tensor*, onnxruntime::Tensor*)output_sequences != nullptrvoid onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span&, gsl::span&)top_k <= beams_used_BeamSearch op: An implementation for the input type onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/generation_device_helper.ccinput_ids_shape.NumDimensions() == 2onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IBeamSearchState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]logits_shape.NumDimensions() == 3ProcessLogitsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = OrtValue; _Alloc = std::allocator; const_reference = const OrtValue&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]element_type == DataTypeImpl::GetType()onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, gsl::span&, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]is_kv_cacheonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]input_features_shape.NumDimensions() == 3start_token_id >= 0original_decoder_input_ids_shape.NumDimensions() == 2onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IGreedySearchState*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]Sample/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_cpu_helper.hGreedySearchProcessLogitsGreedySearchN11onnxruntime7contrib12transformers22GreedySearchParametersEN11onnxruntime7contrib12transformers12GreedySearchESt4lessIfESt15binary_functionIffbESt7greaterIfEZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers14BeamHypothesesEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_ZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers15HypothesisScoreEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_base.hvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)Not Implementedvoid onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_parameters.ccvoid onnxruntime::contrib::transformers::Sampling::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling.ccvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_gpt.hvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::Compute(onnxruntime::OpKernelContext*) constSamplingN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers18SamplingParametersEN11onnxruntime7contrib12transformers8SamplingEFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEPFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEN11onnxruntime7contrib12transformers16ILogitsProcessorIfEEN11onnxruntime7contrib12transformers24TimestampLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PresencePenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers26TemperatureLogitsProcessorIfEEN11onnxruntime7contrib12transformers24MinLengthLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PrefixVocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers24VocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers28NoRepeatNGramLogitsProcessorIfEEN11onnxruntime7contrib12transformers32RepetitionPenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_22GreedySearchParametersEEE@temperaturetop_pfilter_valuemin_tokens_to_keeppresence_penaltycustomDecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentionvoid onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_parameters.ccSeed must be >= 0seed >= 0onnxruntime::common::Status onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const std::vector&, int, int, gsl::span&, OrtValue&, const OrtValue*, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, onnxruntime::Stream*, int, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_gpt.ccSetup must be called before CreateInitialFeedssession_state_ != nullptrCreateInitialFeedsonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu]past_sequence_lengthbeam_widthonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)Setup/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_base.ccconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::_Optional_base]ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)position_idssubgraph input 1 shall be named as position_ids, got: subgraph input 2 shall be named as attention_mask, got: past_0subgraph input 3 shall be named as past_0, got: subgraph past state is expected to have 5 dimension, got subgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headlogitspresent_0subgraph input 1 shall be named as present_0, got: subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputencoder_hidden_states/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.ccdecoder_masked_attention shall use with past_present_share_buffernumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 0 shall be named as input_ids, got: encoder_attention_maskdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph output 0 shall be named as logits, got: decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph output shall have same data type as that of encoder_hidden_states/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_encoder.ccexpect 3 inputs, got:expect >=6 outputs, got:number of outputs expected to be 2 + 4 * layers, got:encoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder_input_idsencoder subgraph input 2 shall be named as decoder_input_ids, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 1 shall be named encoder_hidden_states, got: present_key_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: present_value_self_0encoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph outputs 1, 2, ... shall have same data type/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_decoder.ccnumber of outputs expected to be first_present_output_index_ + 2 * layers, got:When outputing cross qk, number of outputs expected to be first_present_output_index_ + 3 * layers, got:decoder subgraph input 1 shall be named as encoder_hidden_states, got: decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_states./onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_encoder.ccexpect 2 inputs, got:encoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeInput tensor to Unique op should be 1DConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccUnsupported typeN11onnxruntime8functors7SigmoidIdEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluINS_9MLFloat16EEEEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluINS_9MLFloat16EEEEEN11onnxruntime8functors4ReluIiEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEEN11onnxruntime8functors4ReluIaEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEEN11onnxruntime8functors4ReluIdEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEEN11onnxruntime8functors9LeakyReluINS_9MLFloat16EEEN11onnxruntime8functors26ElementWiseRangedTransformINS_9MLFloat16EEEN11onnxruntime8functors4ReluINS_9MLFloat16EEEN11onnxruntime7contrib17WordConvEmbeddingEN11onnxruntime7contrib6UniqueIfEEFailed to create output tensor for If output CopyTensorAsyncstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtDevice*; _Alloc = std::allocator; reference = const OrtDevice*&]Looponnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Celu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.ccinfo == nullptronnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)'If' node has outputs which doesn't match the subgraph's outputs.num_subgraph_outputs == static_cast(num_outputs)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FeedsFetchesManager&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&]Inconsistent shape in loop output for output. void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccinfo.GetAttr("body", &proto).IsOK()virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)info_ == nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; reference = const onnx::TypeProto*&]onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)Graph in 'body' attribute of Loop should have inputs. Found:static_cast(num_subgraph_inputs) == subgraph_inputs.size() but has 'Loop' node has outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const OrtValue*; _Alloc = std::allocator; const_reference = const OrtValue* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_If nodes condition input must have exactly one elementSubgraph SessionState was not found for 'typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::If::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::If::Info&]Failed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedN11onnxruntime4LoopE*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceR8OrtValueRbE_N11onnxruntime2IfEN11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEEN11onnxruntime8functors4TanhIdEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEEO@O@O@O@OOOO9999-rB-rB-rB-rB-r-r-r-r1111^9^9^9^9giP9giP9giP9giP9C:C:C:C:*=*=*=*=*>*>*>*><<<<CCCCScan<8> spec does not support transpose of output. This should never be called.CreateFeedsFetchesManager/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.ccSubgraph must have the shape set for all outputs but did not.AllocateOutputInvalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was has batch size of Scan inputs have inconsistent sequence lengths. Previous value was has length of onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.ccOutput OrtValue has not been created for loop state variable output p_mlvalueCreateLoopStateVariablesstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::scan::detail::OutputIterator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::scan::detail::OutputIterator&]typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = const OrtValue&]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; reference = onnxruntime::scan::detail::LoopStateVariable&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&]onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]feeds_fetches_manager_ && info_Subgraph SessionState was not found for 'body' attribute.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<8>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<8>::Info&]ValidateInputsequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was Subgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsonnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)The subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::__cxx11::basic_string]onnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::__cxx11::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<9>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<9>::Info&]void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const std::string&, onnxruntime::TensorShapeVector&, size_t)Number of entries in '' was but expected directions.size() == num_entriesInvalid values in ''. 0 == forward. 1 == reverse.void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]scan_input_directionsscan_output_directionsscan_input_axesNumber of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&)output_mlvalueconst OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hAttempt to retrieve final output before it was set.final_output_mlvalue_Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()Outputs from Scan are not optional and should never be null.TransposeOutputonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]Invalid value in scan_input_axes for input . Input tensor rank was but input '' dimension SetupInputsvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) consttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Loop::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Loop::Info&]'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector&, std::vector&)All scan outputs MUST be tensorslast_outputs[j + 1].IsTensor()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; const_reference = const onnx::TypeProto* const&; size_type = long unsigned int]ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_N11onnxruntime4ScanILi9EEE*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEN11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorEN11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorEFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEPFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mE constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = std::unique_ptr >; long unsigned int _Nm = 3; const_reference = const std::unique_ptr >&; size_type = long unsigned int]Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Failed to create output tensor for output #AllocateFinalBufferMismatch between expected shape and shape from first output is not compatible with input_ids and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeInput 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 segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of OrtValue& onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = OrtValue&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; reference = onnxruntime::scan::detail::LoopStateVariable&; size_type = long unsigned int]IterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()Misuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)If shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_AllocateFinalOutputonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus]min_ngram_sizeonnxruntime::contrib::BifurcationDetector::BifurcationDetector(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bifurcation_detector.hinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_onnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::MultiHeadAttention::MultiHeadAttention(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/multihead_attention.ccAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:ValidateInputs/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::contrib::BifurcationDetector::Compute(onnxruntime::OpKernelContext*) constsrc_tokens_len >= prev_suffix_match_idx_datapred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)AttnLSTMBifurcationDetectorMultiHeadAttentiononnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding./onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cconnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false]use_approximationN11onnxruntime7contrib18MultiHeadAttentionIfEEN11onnxruntime7contrib8BiasGeluIfLb1EEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib14EmbedLayerNormIfEEN11onnxruntime7contrib19BifurcationDetectorEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib8BiasGeluIfLb0EEEZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib9AttentionIfEEN11onnxruntime7contrib16AttentionWrapperIfEEN11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEEN11onnxruntime7contrib17DeepCpuAttnLstmOpEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9QuickGeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib9QuickGeluIfEEN11onnxruntime7contrib4GeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceRSD_RbE_y"=y"=y"=y"=*BL?*BL?*BL?*BL?CudaOpenVINO_GPUHipWebGPU_BufferCudaPinnedHipPinnedSpecified device is not supported.Input 'x' is expected to have 3 or 4 dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 Input 'cos_cache' is expected to have 2 dimensions, got Input 'sin_cache' is expected to have 2 dimensions, got Inputs 'cos_cache' and 'sin_cache' are expected to have num_heads must be provided if rotary_embedding_dim is specifiedrotary_embedding_dim must be less than or equal to head_sizeInput 'position_ids' dimension 0 should be of size batch_size, got Input 'position_ids' dimension 1 should be of size sequence_length, got Input 'cos_cache' dimension 0 should be same as max_sequence_length, got Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/rotary_embedding.ccUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supportedThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:cudaMallochipMalloctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::synchronize::Notification; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::synchronize::Notification&]MaybeTransposeToBNSHAndAddBiasAddBiasReshapeAddBiasTransposePacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUInput 'query' is expected to have 3 or 5 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' dimension 0 should be batch_size, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). Input 'past_key' dimension 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall be both present or both absentInput 'query' is expected to have 3 dimensions when key is given, got Input 'key' is expected to have 3, 4, or 5 dimensions, got Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'key' shall have same dim 2 (hidden_size)Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect 'value' be none when 'key' has packed kv format.Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyInput 'query' is expected to have 3 or 5 dimensions when key is empty, got Expect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kvInput 'bias' is expected to have 1 dimension, got 'bias' is not allowed for packed kv. Input 'key_padding_mask' shape shall be 1D, 2D, or 3DInput 'value' is expected to have 3 or 4 dimensions, got Input 'query' and 'value' shall have same dim 0 (batch_size)Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'relative_position_bias' dimension 0 should be batch_size or 1, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/ngram_repeat_block.hinfo.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_size_ > 0virtual onnxruntime::common::Status onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeonnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) const::token_id < vocab_sizeinterleavedonnxruntime::contrib::RotaryEmbedding::RotaryEmbedding(const onnxruntime::OpKernelInfo&) [with T = float]num_heads must be provided if rotary_embedding_dim is specifiedonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()euclideanonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = double]void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.ccReal memory steps is not in (0, mem_steps <= max_memory_steps_ && mem_steps > 0int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const/onnxruntime_src/onnxruntime/core/framework/bfc_arena.hp_int >= base_intp_int < base_int + memory_size_onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(ChunkHandle)/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cch < chunks_.size()void onnxruntime::BFCArena::InsertFreeChunkIntoBin(ChunkHandle)!c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(ChunkHandle)!c->in_use() && (c->bin_num != kInvalidBinNum)Could not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0RotaryEmbeddingConvTransposeWithDynamicPads/onnxruntime_src/onnxruntime/core/framework/allocator.cc nmemb= size= alignment=RegionForCould not find Region for onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::FindChunkPtr(BinNum, size_t, size_t, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)!chunk->in_use()void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(Bin::FreeChunkSet*, const std::set::iterator&)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]void onnxruntime::BFCArena::SplitChunk(ChunkHandle, size_t)void onnxruntime::BFCArena::Merge(ChunkHandle, ChunkHandle)!c1->in_use() && !c2->in_use() && c1->stream == c2->streamc2->prev == h1onnxruntime::BFCArena::ChunkHandle onnxruntime::BFCArena::Coalesce(ChunkHandle)!c->in_use()BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: memory limit: arena_extend_strategy: Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int, int64_t)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)CreateAllocator/onnxruntime_src/onnxruntime/core/framework/allocator_utils.ccReceived invalid value of arena_extend_strategy ReserveReserving memory in BFCArena for virtual void* onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()void onnxruntime::BFCArena::DeallocateRawInternal(void*)h != kInvalidChunkHandlevoid onnxruntime::BFCArena::FreeAndMaybeCoalesce(ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = void*; _Alloc = std::allocator; reference = void*&] BFC Arena shrunk by bytes. The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*)Could not find Region for: entry != regions_.end()St14default_deleteIN11onnxruntime16StreamAwareArenaEESt14default_deleteIN11onnxruntime8BFCArenaEESt14default_deleteIN11onnxruntime10IAllocatorEEN11onnxruntime8BFCArenaEN11onnxruntime16StreamAwareArenaESt19_Sp_counted_deleterIPN11onnxruntime16StreamAwareArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEN11onnxruntime7contrib5CDistIdEEN11onnxruntime7contrib5CDistIfEEZNK11onnxruntime7contrib15RotaryEmbeddingIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib15RotaryEmbeddingIfEEZNK11onnxruntime7contrib16NGramRepeatBlock7ComputeEPNS_15OpKernelContextEEUlllE0_N11onnxruntime7contrib16NGramRepeatBlockEZN11onnxruntime7contrib16AddBiasTransposeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_ZN11onnxruntime7contrib14AddBiasReshapeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_CopyTensorsinvalid index data_transfer registered is nullptr.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Tensor size mismatchThere's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.ccbooldoubleint8uint8int16uint16uint32uint64float16Float8E4M3FNFloat8E4M3FNUZFloat8E5M2Float8E5M2FNUZI"%(+.147:=@CFtensor type is not supported &,28>DJPV\bhnsparse tensor type  &,28>DJPV\bhnMLDataType for: is not currently registered or supportedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Config key is empty or longer than maximum length 128Config value is longer than maximum length: AddConfigEntry/onnxruntime_src/onnxruntime/core/framework/config_options.ccConfig with key [] already exists with value []. It will be overwrittenvirtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&) constsrc.SizeInBytes() == dst.SizeInBytes()static void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto*, onnx::TypeProto&)/onnxruntime_src/include/onnxruntime/core/framework/data_types.hexpected a registered ONNX typevalue_proto != nullptrstatic void onnxruntime::data_types_internal::SequenceTypeHelper::Set(const onnx::TypeProto*, onnx::TypeProto&)elem_proto != nullptrstatic void onnxruntime::data_types_internal::OptionalTypeHelper::Set(const onnx::TypeProto*, onnx::TypeProto&)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)/onnxruntime_src/onnxruntime/core/framework/data_types.ccvoid onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)Only ONNX MLDataType can be registeredproto != nullptrWe do not expect duplicate registration of types for: virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())virtual bool onnxruntime::OptionalTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->optional_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)Invalid DataTypeImpl TypeProto definitiononnxruntime::Stream* onnxruntime::DeviceStreamCollectionImpl::GetStream(size_t) conststream_idx < num_streams_const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const/onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::StreamAwareArena* onnxruntime::AsStreamBasedAllocator(AllocatorPtr)/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccallocator is nullptrallocator.get() != nullptronnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, gsl::span)node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&)OrtValue shape verification failed. Current shape: Requested shape:shape && tensor.Shape() == *shapeshape && sp_tensor.DenseShape() == *shape/onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: Caught exception while destructing CustomOpsLoader with message: N11onnxruntime11synchronize12NotificationEN11onnxruntime17DummyNotificationEN11onnxruntime6StreamEZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_N11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E5M2FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqEjEEN11onnxruntime12OptionalTypeINS_9TensorSeqEdEEN11onnxruntime12OptionalTypeINS_9TensorSeqEsEEN11onnxruntime12OptionalTypeINS_9TensorSeqEtEEN11onnxruntime12OptionalTypeINS_9TensorSeqEhEEN11onnxruntime12OptionalTypeINS_9TensorSeqEaEEN11onnxruntime12OptionalTypeINS_9TensorSeqENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime12OptionalTypeINS_9TensorSeqEbEEN11onnxruntime12OptionalTypeINS_9TensorSeqEfEEN11onnxruntime12OptionalTypeINS_9TensorSeqEiEEN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E5M2FNUZEEEN11onnxruntime12OptionalTypeINS_6TensorENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_6TensorENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_6TensorENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorEmEEN11onnxruntime12OptionalTypeINS_6TensorEjEEN11onnxruntime12OptionalTypeINS_6TensorEdEEN11onnxruntime12OptionalTypeINS_6TensorElEEN11onnxruntime12OptionalTypeINS_6TensorEsEEN11onnxruntime12OptionalTypeINS_6TensorEtEEN11onnxruntime12OptionalTypeINS_6TensorEhEEN11onnxruntime12OptionalTypeINS_6TensorEaEEN11onnxruntime12OptionalTypeINS_6TensorENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime12OptionalTypeINS_6TensorEbEEN11onnxruntime12OptionalTypeINS_6TensorEfEEN11onnxruntime12OptionalTypeINS_6TensorEiEEN11onnxruntime18SequenceTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime18SequenceTensorTypeINS_10Float8E5M2EEEN11onnxruntime18SequenceTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime18SequenceTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime18SequenceTensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEEN11onnxruntime10TensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN4onnx16TypeProto_OpaqueEN4onnx13TypeProto_MapE/onnxruntime_src/onnxruntime/core/framework/func_kernel.hCreate state function failed. Return value:MapNamesToMLValueIdxs/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccError mapping feeds: Error mapping output names: InlineNodesAPI VERSION Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, onnxruntime::HashValue&) const/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccIExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_void onnxruntime::IExecutionFrame::Init(gsl::span, gsl::span, const std::unordered_map&, const std::function&)>&, gsl::span)Get feed size: but expected feed size: feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Could not find OrtValue with idx 'constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Tensor shape cannot contain any negative valuestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; const_reference = const onnxruntime::MemoryPattern&; size_type = long unsigned int]AllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actual size is: , fall back to default allocation behavioronnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtDevice&, const onnxruntime::TensorShape&)Failed to get allocator for alloc && alloc.get() != nullptrTraceAllocateTraceAllocation for ort_value_idx=AllocateAsPerAllocationPlanShape 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.AllocateMLValueTensorPreAllocateBufferInvalid allocation kind: AllocateReusedOrtValueIfNotAllocatedHelperVerifyOutputSizesExpected shape from model of does not match actual shape of for output void onnxruntime::ExecutionFrame::TraceFree(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::OrtValuePatternPlanner; _Dp = std::_Optional_base]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&; size_type = long unsigned int]TraceFreeTraceFree for ort_value_idx=ReleaseMLValueImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.ccCandidate for fallback CPU execution: std::unordered_set onnxruntime::GetCpuPreferredNodes(const GraphViewer&, const IExecutionProvider::IKernelLookup&, gsl::span)kernel_info != nullptrGetCpuPreferredNodesfloat8e4m3fnfloat8e4m3fnuzfloat8e5m2float8e5m2fnuzORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeGetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.std::visit: variant is valuelessonnxruntime::KernelLookup::KernelLookup(onnxruntime::ProviderType, gsl::span >, const onnxruntime::IKernelTypeStrResolver&)/onnxruntime_src/onnxruntime/core/framework/kernel_lookup.hprovider_type must be specified.!provider_type_.empty()typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IndexedSubGraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IndexedSubGraph&]Node '' OpType: with domain: was inserted using the NHWC format as requested by , but was not selected by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP.PartitionOrtFormatModelImpltypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ComputeCapability; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ComputeCapability&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::PartitionOrtFormatModelImpl(const {anonymous}::PartitionParams&, KernelRegistryManager&, IExecutionProvider&)::CompilationEntry; _Alloc = std::allocator; reference = onnxruntime::PartitionOrtFormatModelImpl(const {anonymous}::PartitionParams&, KernelRegistryManager&, IExecutionProvider&)::CompilationEntry&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::GraphViewer&]single_node_compute_func should have 1 element.PartitionOnnxFormatModelImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; reference = const onnxruntime::NodeArg*&; size_type = long unsigned int]onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&)/onnxruntime_src/onnxruntime/core/graph/function.ccInitializer is not found or is not constant initializer.initializer != nullptrThe Function Style fusion is deprecated.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator; reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&] did not return correct number of compiled functionsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::NodeComputeInfo; _Alloc = std::allocator; reference = onnxruntime::NodeComputeInfo&; size_type = long unsigned int]InlineFunctionsAOTImplstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IExecutionProvider; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::IExecutionProvider]No provider specified.PartitionOnnxFormatModelPartitionep.context_enableep.context_file_path_ctx.onnxCreateEpContextModelFailed to generate EP context model since the file exist already.*ZN11onnxruntimeL27PartitionOrtFormatModelImplERKNS_12_GLOBAL__N_115PartitionParamsERNS_21KernelRegistryManagerERNS_18IExecutionProviderEEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISE_EEE_*ZN11onnxruntimeL28PartitionOnnxFormatModelImplERNS_5GraphERNS_11FuncManagerERNS_21KernelRegistryManagerERNS_14KernelRegistryERNS_18IExecutionProviderENS_16GraphPartitioner4ModeERiRKSt8functionIFNS_6common6StatusES1_RbS9_RKSD_IFvRKS0_EEEESM_EUlS3_RKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISV_EEE0_N11onnxruntime18IExecutionProvider13IKernelLookupEN11onnxruntime12KernelLookupEN11onnxruntime14FunctionKernelE*ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKNS_18IExecutionProvider13IKernelLookupEN3gsl4spanIKmLm18446744073709551615EEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime14ExecutionFrameC4EN3gsl4spanIKiLm18446744073709551615EEENS2_IK8OrtValueLm18446744073709551615EEES4_S7_RKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS5_RbEESt4hashImESt8equal_toImESaISt4pairIKmSL_EEEPKNS_22DeviceStreamCollectionERKNS_12SessionStateEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_N11onnxruntime14ExecutionFrameETempSpace allocator not foundKernel found kernel in the supported version range (node_version: However the types are incompatible.Op type ( domain ( kernel is not supported in Encountered following errors: (Kernel not foundkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.found duplicated provider in KernelRegistryManager/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver.ccoqs+uwy{}i kAttribute name and type don't matchNo attribute with this name is defined.MatchKernelDefTypes/onnxruntime_src/onnxruntime/core/framework/kernel_registry.ccbool onnxruntime::{anonymous}::MatchKernelDefTypes(const onnxruntime::Node&, const std::unordered_map, std::vector >&, const onnxruntime::IKernelTypeStrResolver&, std::string&)static_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptrThis op has been implemented only for the following types (), but the node in the model has the following type ( However the types are incompatible. Op with name ( and type (The node is not placed on any Execution Provider. (node:'' ep:''). Failed to find kernel for const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccnullptr != type_protoonnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init > >(const onnxruntime::ValidNodes >&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptrint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_virtual OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&)virtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constInvalid arg_num of . Num args is arg_num < arg_counts.size()virtual OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)Failed to find op_id: ResolveKernelTypeStrFailed to find args for kernel type string '' for node type ''. If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.Kernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string.RegisterNodeOpSchemaOp schema must be available.RegisterGraphNodeOpSchemasktsrLoadKernelTypeStrResolverFromBuffer/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.AddLayoutTransformationRequiredOpsToKernelTypeStrResolverstatic std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_typeinfo.ccOrtValue is TensorSequence type but has no element Tensor DataType.tensor_data_type != nullptrTensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typestatic std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto&)This TypeProto does not have ValueCase setThe type is not tensor, sparse tensor, sequence, map or optional typeZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ktsrL  HDx P``  :Identity:14`$<:DequantizeLinear:10 , NH 80lH:QuantizeLinear:19<  :Identity:19640P com.microsoft:DequantizeLinear:1t8d\@ :Squeeze:11,   :Transpose:130VT :Identity:1x`$com.microsoft:NhwcMaxPool:1@ :Transpose:1B@ :Unsqueeze:11hP(P (pcom.microsoft:QLinearConv:1 T3"$@w_scale8hpX`x T48 :Unsqueeze:13 FD4 :Gather:11lXp :Squeeze:1@ :Unsqueeze:1B@ :Identity:13hP :Identity:16 V8 :Gather:13.,H$8\:QuantizeLinear:10|0XDLL :DequantizeLinear:19 24x$X@ :Squeeze:13axesxhL`:QuantizeLinear:13y_scale(@f8h,Tcom.microsoft:QuantizeLinear:1 T1 T2@ :Gather:1(Tind Pv8t$H:DequantizeLinear:13x_scale y   T std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]KernelUseSharedPrePackedBuffersThe kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; reference = onnxruntime::MemoryPattern&]Received nullptr for nameReceived nullptr for OrtValueReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.PopulateKernelCreateInfoCould not find an implementation for ) node with name 'VerifyEachNodeIsAssignedToAnEpImplconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)/onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.h!using_counters_current <= buffer_size_static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.ccCan't slice a non-tensor OrtValue. Type was ort_value.IsTensor()OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()Insufficient dimensions to slice on . Shape:gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInvalid dim0_offset of . Dimension 0 is dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int]static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int]const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()void onnxruntime::AccumulateAllNestedSubgraphsInfo(const SessionState&, const std::string&, size_t, SubgraphsKernelCreateInfoMaps&)subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()void onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)idx < num_streams_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; reference = onnxruntime::Stream*&; size_type = long unsigned int]void onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, onnxruntime::Stream*)Compute_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]PruneRemovableAttributesfailed at retrieving the removable attributesfor node '' ('').removed removable attributes '), among attributes: onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string&)/onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::SessionState::PrepackConstantInitializedTensors(onnxruntime::InlinedHashMap, long unsigned int>&, const std::unordered_map, const OrtValue*>&)::allocator_for_caching.get() != nullptr doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be empty!op_type.empty()+onnxruntime::HashValue onnxruntime::PrePackedWeights::GetHash() const/onnxruntime_src/onnxruntime/core/framework/prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()Using cached version of pre-packed weight for constant initializer: used in the node: which is of op type: Unable to write the provided PrePackedWeights instance into the containerCreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.CreateSubgraphSessionStatevoid onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string&, std::unique_ptr)Entry exists in node for attribute existing_entries.find(attribute_name) == existing_entries.cend()VerifyEachNodeIsAssignedToAnEpNode placements All nodes placed on []. Number of nodes: Node(s) placed on [Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::common::Status onnxruntime::session_state_utils::ExtDataTensorProtoToTensor(const onnxruntime::Env&, const std::__cxx11::basic_string&, const onnx::TensorProto&, onnxruntime::Tensor&, onnxruntime::OrtCallback&)/onnxruntime_src/onnxruntime/core/framework/session_state_utils.ccutils::HasExternalData(tensor_proto)ExtDataTensorProtoToTensor*ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRNS_14InlinedHashMapIS6_mSaISt4pairIS7_mEEEERKNSI_IS6_9OrtDeviceSaISJ_IS7_SO_EEEEbEUlS8_iRK8OrtValueRKNS_11OrtCallbackEbbE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE9OrtDeviceSaISt4pairIKSI_SJ_EEEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE9OrtDeviceSaISt4pairIKSI_SJ_EEEEEUlRKNS_7NodeArgEmE_N11onnxruntime11DummyStreamE*ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUliE_N11onnxruntime28IStreamCommandHandleRegistryEN11onnxruntime31StreamCommandHandleRegistryImplEInternal error.Mem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedInvalid TensorProtoTrace/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h%FinalizePlanFailed to get allocator for location: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; reference = onnxruntime::MemoryPattern&; size_type = long unsigned int]duplicated locationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; reference = onnxruntime::Tensor&; size_type = long unsigned int]Tensor type is undefinedUnpackTensor: the pre-allocated size does not match the raw data size, expected corrupted protobuf data: tensor shape size() does not match the data size() in protoGraphSaveInputOutputNamesToNodeMapping input with name is not used by any node.ValidateCsrIndicesdense shape must 2-D. Got: Inner and Outer indices must either be both zero or non-zeroExpecting inner index size: the same as values size: Outer index count must be rows + 1 or zero. Got: rows: ReadExternalDataForTensorUnpackTensorWithExternalDataImplnullptr == p_datavoid onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string&, const NodeInfo&)Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()static const onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue&)the ort_value must contain a constructed sparse tensorstatic onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue&)this tensor already has populated sparse_indicesonnxruntime::SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constMust contain Coo format. Got: Format() == SparseFormat::kCooExpecting to contain one index, got: format_data_.size() == 1Ustd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; const_reference = const onnxruntime::Tensor&; size_type = long unsigned int]onnxruntime::SparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constMust contain Csr format. Contains: Format() == SparseFormat::kCsrcExpecting two indices. Got: format_data_.size() == 2Ustatic size_t onnxruntime::Tensor::CalculateTensorStorageSize(onnxruntime::MLDataType, const onnxruntime::TensorShape&)shape.Size() must >=0tensor failed memory size calculationonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr)size_t onnxruntime::Tensor::SizeInBytes() consttensor size overflowDeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires , Got DeserializeTensorProtostring tensor is not supported for copying between allocatorsFailed to copy tensor to CopyDataMust have the same size. Got src_size: dst_size: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; const_reference = const std::reference_wrapper&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; const_reference = const std::reference_wrapper&; size_type = long unsigned int]Support 2-D matrices onlyInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUonnxruntime::common::Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const onnxruntime::DataTransferManager&, const onnxruntime::SparseTensor&, const onnxruntime::AllocatorPtr&, const onnxruntime::AllocatorPtr&, onnxruntime::Tensor&)/onnxruntime_src/onnxruntime/core/framework/sparse_utils.ccExpecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()Outer indices must be M + 1. Got: outer_num == (rows + 1)Unsupported element size: SparseCsrToDenseTensorCurrently do not support dims higher than 2 dimensions: Input must be of COO formatSparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyInvalid index: > dense_size: int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccInvalid dimension of for SizeToDimension. Tensor has dimension <= num_dimsint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constInvalid tensor shape slice argument.dimstart <= dimend && dimend <= values_.size()the ort_value must contain a constructed tensor or sparse tensorOrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.ccArgument is not a tensoronnxruntime::SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constMust contain BlockSparse format. Got: Format() == SparseFormat::kBlockSparseExpecting one index. Got: const onnxruntime::Tensor& {anonymous}::GetIndicesTensor(const OrtValue&, OrtSparseIndicesFormat)Unsupported indices_format passedValues size must be less than total buffer size: onnxruntime::SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)This method should follow a call to constructor that supplies the allocatorallocator_ != nullptrFormat() == SparseFormat::kUndefinedThis instance should not be emptyDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeMust have the same shapeonnxruntime::SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)onnxruntime::SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)Unable to convert strings tensor to a sparse tensor that not on CPUDenseTensorToSparseCsrDenseTensorToSparseCoostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned char; _Alloc = std::allocator; reference = unsigned char&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = short unsigned int; _Alloc = std::allocator; reference = short unsigned int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned int; _Alloc = std::allocator; reference = unsigned int&]N11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternE*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE0_*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE_N11onnxruntime19session_state_utils19ExtDataValueDeleterE  Failed to find input name in the mapping: CalculateStaticCopyInfoForFeedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector >; _Alloc = std::allocator > >; reference = absl::lts_20240116::InlinedVector >&; size_type = long unsigned int]TensorProtoToTensor() tensor shape mismatch!TensorProto type can not be written into Tensor type TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: bDqS5&const OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::SessionState&, std::string_view)exec_plan_ptrFindDeviceForValueconst OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, std::string_view)CalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfobool onnxruntime::utils::FinalizeCopyInfoForFeeds(gsl::span, std::vector&)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(gsl::span&, std::vector&)fetch_alloc_info.size() == copy_info.size()const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap&, NodeIndex)/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)invalid value index: against size n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forstatic size_t onnxruntime::PlannerImpl::GetElementSize(const std::string* const&)nullptr != tensor_type_baseonnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName&)Can not find the node ComputeReuseCountonnxruntime::PlannerImpl::ComputeValueLocation()::There is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_map reused by as inputCopySparseDataSparse Indices raw data size does not match expected.Sparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, const onnxruntime::Path&, gsl::span, std::function)indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid SparseTensor indices. Should be rank 0 or 1. Got:SparseTensorProtoToDenseTensorProtoElement_size of: Unsupported sparse tensor data type of ConstantNodeProtoToTensorProtoConstant node: has no data attributesonnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&, const std::string&)Unsupported attribute value type of in 'Constant' node 'onnxruntime::common::Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool*, Tensor&, std::ptrdiff_t, const TensorShapeVector&, const TensorShape&, const Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = TypeList, std::allocator > >; std::ptrdiff_t = long int; TensorShapeVector = absl::lts_20240116::InlinedVector >]TransposeSingleAxisOutwards/onnxruntime_src/onnxruntime/core/framework/transpose_helper.ccvoid onnxruntime::TransposeSingleAxisOutwards(gsl::span, const Tensor&, Tensor&, size_t, size_t, const TensorShape*, concurrency::ThreadPool*)onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, onnxruntime::Stream*, std::vector*, std::vector*)Failed to find allocator for device Unsupported OrtValue type.BatchOrCopyMLValuestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SparseSrcDstPair; _Alloc = std::allocator; reference = onnxruntime::IDataTransfer::SparseSrcDstPair&]Unsupported OrtValue type to copy between device.void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)id >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeValueLocation()Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defCan not find the execution provider ComputeValueLocationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&, const onnxruntime::KernelCreateInfoMap&)p_providerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtDevice; _Alloc = std::allocator; reference = OrtDevice&]void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const onnxruntime::GraphViewer&, const onnxruntime::InitializedTensorSet&, const onnxruntime::KernelCreateInfoMap&, const std::string&, size_t, std::vector >&)specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_N4onnx17SparseTensorProtoEcannot use operator[] with a string argument with LGBm=rkd]VOHA:3,% yonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)Only tensors are supported for external outputs for now.!IsNonTensor(*node_output)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::PlannerImpl::OrtValueInfo; _Alloc = std::allocator; reference = onnxruntime::PlannerImpl::OrtValueInfo&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = absl::lts_20240116::InlinedVector, 1, std::allocator > >; _Alloc = std::allocator, 1, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 1, std::allocator > >&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 1, std::allocator > >; _Alloc = std::allocator, 1, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 1, std::allocator > >&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::DeviceBasedPartitioner::PartitionGraph(const onnxruntime::GraphViewer&, const onnxruntime::ExecutionProviders&, std::vector > >&, onnxruntime::ExecutionOrder)Failed to find node "" in node-stream mapiter != node_stream_map.end()CreateGraphPartitionerUse DeviceBasedPartition as defaultPartitioner type is not DeviceBasedPartitionerstreamsdevicesstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = signed char; _Alloc = std::allocator; reference = signed char&]void onnxruntime::DeviceBasedPartitioner::Initialize()Number of streams does not equal to number of device types!node_names_by_stream_.size() == device_types_.size()Caught exception when reading partition config file: iterator does not fit current valuecannot use insert() with SaveConfigCaught exception during saving DeviceBasedPartitioner config: OptimizeReusePlanForMultiStreamonnxruntime::common::Status onnxruntime::PlannerImpl::BuildExecutionPlan(const onnxruntime::ExecutionProviders&, const onnxruntime::IStreamCommandHandleRegistry&)BuildExecutionPlanexecution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]HQNZWTifc`]KZN11onnxruntime11PlannerImpl24GenerateDeallocationPlanEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE2_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE1_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlRKNS_7NodeArgEmE0_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE_ZZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvENKUlmE_clEmEUlmE_ZN11onnxruntime11PlannerImpl20ComputeValueLocationEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl17ComputeReuseCountEvEUlRKNS_7NodeArgEmE_N8nlohmann6detail23output_adapter_protocolIcEEN8nlohmann6detail21output_string_adapterIcNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESt23_Sp_counted_ptr_inplaceIN8nlohmann6detail21output_string_adapterIcNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime17IGraphPartitionerEN11onnxruntime22DeviceBasedPartitionerEN11onnxruntime23SequentialExecutionPlan13ExecutionStepEN11onnxruntime11BarrierStepEN11onnxruntime12WaitOnEPStepEN11onnxruntime16LaunchKernelStepEN11onnxruntime24ActivateNotificationStepEN11onnxruntime21TriggerDownstreamStepE0001020304050607080910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989900010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::StreamExecutionContext::CountDownBarrier; _Alloc = std::allocator; reference = onnxruntime::StreamExecutionContext::CountDownBarrier&; size_type = long unsigned int]Set a barrier with id: , count: WaitOnEPStep: wait on notification with id: Launch kernel with node id: ActivateNotificationStep: activate notification with id: TriggerDownstreamStep: trigger downstream of trigger point: func info for node: already exist.Can't use func with null ptrstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = std::unordered_map, onnxruntime::FuncManager::FuncInfo>; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = std::unordered_map, onnxruntime::FuncManager::FuncInfo>]Error parsing node:Error unexpected extra input in node:{"":[]}_kernel_timeop_nameprovidernode_indexactivation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapethread_scheduling_stats_fence_afterAttribute expected to have tensor or sparse tensor type[TypeInferenceError] Inputs 0 shall be 2 dimensionsInvalid bias shapeqkv_hidden_sizes should have 3 elementsInputs 0 (query) shall be 3 dimensionsInputs 2 (value) shall be 3 dimensionsThe past_key input shall be 4 dimensionskey and value cache shall be 4 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensions but has rank expected to have rank expected to have rank >Dimension mismatch in unification between Inputs 0 shall be 3 dimensions in whisper graphFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalar/onnxruntime_src/onnxruntime/core/framework/execution_steps.ccstream activate notification with index not found. has already been loaded.LoadExternalLibCaught exception while loading custom ops with message: GetFuncs/onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.ccA dso with name onnxruntime::Stream* onnxruntime::StreamExecutionContext::GetDeviceStream(size_t)/onnxruntime_src/onnxruntime/core/framework/stream_execution_context.ccidx < device_stream_map_->NumStreams()virtual onnxruntime::common::Status onnxruntime::WaitOnEPStep::Execute(onnxruntime::StreamExecutionContext&, size_t, onnxruntime::SessionScope&, const bool&, bool&)WaitOnEPStep.wait_handle is nullwait_handle_ wait on Notification with id: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = std::atomic; _Dp = std::default_delete []>; typename std::add_lvalue_reference<_Tp>::type = std::atomic&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; const_reference = const onnxruntime::SequentialExecutionPlan::ReleaseAction&; size_type = long unsigned int]void onnxruntime::StreamExecutionContext::RecycleNodeInputs(onnxruntime::NodeIndex)frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&, onnxruntime::Stream*)/onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hAll implicit inputs should have OrtValue instances by now. does not.entry != nullptronnxruntime::common::Status onnxruntime::ExecuteKernel(StreamExecutionContext&, NodeIndex, size_t, const bool&, SessionScope&)/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccAsync Kernel Support is not implemented yet._fence_beforeNon-zero status code returned while running node. Name:'' Status Message: ExecuteKernelExiting due to terminate flag being set to true.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]void onnxruntime::RunSince(size_t, StreamExecutionContext&, SessionScope&, const bool&, size_t)since == endGot null library handleFailed to unload library with error: /onnxruntime_src/onnxruntime/core/framework/library_handles.ccUnloadLibrariesInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsInputs 0 (query) shall be 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Whether to use rotary position embedding. Default value is 0.Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeThe value to be filled in the attention mask. Default value is -10000.0fCustom scale will be used if specified. Default value is 1/sqrt(head_size)pastrelative_position_biasConstrain mask index to integer typescumulative_sequence_lengthPackedAttentionqueryConstrain input and output to float tensors.Constrain mask, offset and sequence length to integer typesPackedMultiHeadAttentioncache_indirectionCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Need output the cross attention MatMul(Q, K)output_qkpast_keypast_valuepresent_keypresent_valueqkConstrain qk output types to float32 tensors.key_padding_maskConstrain mask to integer typesNumber of attention heads for qNumber of attention heads for k and vkv_num_headsleft_window_size for local attention (like Mistral). Default value is -1 meaning unused.local_window_sizeRotate using interleaved pattern. Default value is 0 (False).rotary_interleavedseqlens_ktotal_sequence_lengthcos_cachesin_cacheConstrain mask to int tensor.GroupQueryAttentionOne sided attention windows length W, or half of total window lengthglobal_weightglobal_biasglobalConstrain to integer typesq_weightkv_weightkey_cachevalue_cachestatic_kvuse_pasthas_layer_statehas_key_padding_masknew_key_cachenew_value_cacheConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.DecoderAttentionCustom scale will be used if specified. Default value is 1.0Rotary embedding dimension. Default value is 0.Number of attention heads. Default value is 0. Must use with rotary_embedding_dimConstrain input and output types to integer tensorsThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)mask_index_typesegment_idsword_embeddingposition_embeddingsegment_embeddingembedding_sumConstrain input and output integer tensors typesConstrain input and output float tensors types.Max distancemax_distanceis_bidirectionalbias_tablequery_lengthkey_lengthConstrain sequence_length to int tensors.RelativePositionBiasinv_std_varinput_skip_bias_sumConstrain mean and inv_std_var to float tensors.The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.src_tokenscur_tokensprev_suffix_match_idxpred_tokenstokenssuffix_match_idxsequence_token_countcumulated_seq_lenmax_seq_lenConstrain sequence_token_count and token_offset to integer typesRemovePaddingquery_layerquery_biasrel_poseco_aGatedRelativePositionBiasAlpha value.padding_idxscale_grad_by_freqTorchEmbeddingresidualConstrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.Incompatible dimensions'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplication*ZN11onnxruntime7contrib11GetOpSchemaINS0_40GatedRelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29RestorePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28RemovePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GemmFastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34BifurcationDetector_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31NGramRepeatBlock_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35RelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23FastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30RotaryEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DecoderAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34GroupQueryAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33MultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_46DecoderMaskedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41DecoderMaskedSelfAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39PackedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30PackedAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Attention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_N4onnx26TensorShapeProto_DimensionE*ZN11onnxruntime18ScheduleDownstreamERNS_22StreamExecutionContextEmbRKbRNS_12SessionScopeEEUlvE_N11onnxruntime21SimpleTensorAllocatorE*ZN11onnxruntime14ExecuteThePlanERKNS_12SessionStateEN3gsl4spanIKiLm18446744073709551615EEENS4_IK8OrtValueLm18446744073709551615EEES6_RSt6vectorIS7_SaIS7_EERKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS7_RbEESt4hashImESt8equal_toImESaISt4pairIKmSR_EEERKNS_7logging6LoggerEPKNS_22DeviceStreamCollectionERKbbbEUlvE_N11onnxruntime23OpKernelContextInternalE*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPvE1_*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_̼+̼+Input axis is invalid: If true, check only for Inf, -Inf.isinf_onlyIf true, check only for NaN.isnan_onlyConstrain the output to a boolean tensor.IsAllFiniteThe id of the end-of-sequence tokenThe id of the padding tokenThe id of the token that indicates decoding starts.no repeat ngrams sizeearly stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like T5The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shaperepetition_penaltyvocab_maskprefix_vocab_masklogits_processorsequences_scoresConstrain input type to float or int tensors.The id of the token that indicates decoding starts (i.e. the start of transcription token id)The id of the translate taskThe id of the transcribe taskThe id of the token that indicates LM startsThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates no timestampsThe id of the first timestampMust be 2 for whisperIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.cross_qk_layer_headextra_decoding_idscross_qknon_speech_probsConstrain cross_qk to float32 tensors.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.The value used to module the next token probabilities.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.All filtered values will be set to this float value.Minimumber of tokens we keep per batch example in the output.Presence penalty for custom samplingIf 1 custom sampling logicModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartpresence_maskfiltered_logitsActivation function to use. Choose from relu, gelu, silu and identity. Default is reluactivation_typeNumber of top experts to select from expert poolrouter_probsfc1_experts_weightsfc2_experts_weightsfc1_experts_biasfc2_experts_biasConstrain input and output types to float or float16 tensors.MoEConstrain input0 and output types to float tensorsConstrain input and output types to float tensorsactivation_gammaInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).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.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Quantization typeB_shapeConstrain input matrix data types as single precision float tensorConstrain input B data types as data blobConstrain shape of B must be int64 tensor.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationactivation_axisConstrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.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 arrayThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. crop_sizeWhether A should be transposed. Float 8 only supprted transA=0.Whether B should be transposed. Float 8 only supprted transB=1.Scalar multiplier for the product of input bias C.Output Type. Same definition as attribute 'to' for operator Cast.Activation function, RELU or GELU or NONE (default).TSscaleAscaleBscaleYTRConstrain type to input A.Constrain type to input B.Constrain type to input C.Constrain type to result type.Constrain type for all input scales (scaleA, scaleB, scaleY).GemmFloat8The epsilon value to use to avoid division by zeroThe number of groups of channels. It should be a divisor of the number of channels CgroupsActivation after group normalization: 0 for None, 1 for SiLU1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Constrain gamma and beta to float tensors.GroupNormConstrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNormThree interpolation modes: bilinear (default), nearest and bicubic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.GridConstrain input types to all tensor types.specify the dimension to unfoldspecify the sizespecify the step.stepAllow inputs and outputs to be any kind of tensor.UnfoldTensorConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain indice type to int32 or int64countsInput can be of any tensor type.Input rank must be >= 2.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.inputs are expected to have tensor type and output type should not be null.B input for MatMul must be a 2-D matrix!4b quantization not yet supported on this hardware platform!Input q4 tensors of wrong size!both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput 0 shall be 3 dimensionsbias shall be 1 dimensionOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.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.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.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain seq_lens to integral tensors.initial_hQWMWmemory_seq_lensAWY_hConstrain input and output types.limitdeltaThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varConstrain input X type to float tensors.Constrain output Y, scale and bias type to float tensors.Constrain mean and inv_std_var to be float tensors.Constrain output Y and scale type to float tensors.EfficientNMS_TRTanchorsnum_detectionsdetection_boxesdetection_scoresdetection_classesBackground class ID.background_classEncoding type for the boxes or anchors inputs.box_codingBox IOU threshold value.Max detections to output.max_output_boxesVersion number of the TRT plugin.plugin_versionActivation function to apply to the scores input.score_activationScore threshold value.MultilevelCropAndResize_TRTfeature_map_0feature_map_1feature_map_2feature_map_3patchesImage size.image_sizePooled size.pooled_sizePyramidROIAlign_TRTDisentangledAttention_TRTc2c_attentionc2p_attentionp2c_attentiondisentangled_attentionMaximum relative distance, k.spanScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.factorSnpepayload of the SNPE DLC file.DLC(Optional) SNPE version used to convert the model.snpe_version(Optional) Target device like CPU, DSP, etc.target_device(Optional) Some notes for the modelnotesConstrain input and output types to uint8, uint16, float tensors.EPContextUsually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.main_contextpayload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.ep_cache_context1: indicate ep_cache_context is the context content. 0: indicate ep_cache_context is the file path to the context content.The path is relative to this Onnx file. Default is 1.embed_mode(Optional) SDK version used to convert the model.ep_sdk_version(Optional) Hardware architecture.hardware_architecture(Optional) partitioned graph name.partition_name(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainsourceConstrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.size of each input featuresize of each output featurenumber of bits used for weight quantization (default 4)bitsnumber of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.The minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.zero_pointsConstrain input and output types to float/half_float tensors.Constrain quantized weight types to uint8.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.quantization data type. 0 for FP4, 1 for NF4.Indicate if the ops run in training_mode, by default, False.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.absmaxConstrain input and output types to float/half_float/brain_float tensors. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNCHWSchemaWithNHWCDomainERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_118RegisterNHWCSchemaERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_N4onnx16InferenceContextEN11onnxruntime7contrib20NhwcInferenceContextE*ZN11onnxruntime7contrib11GetOpSchemaINS0_28BiasSplitGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28SkipGroupNorm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GemmFloat8_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28CropAndResize_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_21Unique_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_18Pad_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23GatherND_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26MurmurHash3_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34SparseToDenseMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36FusedMatMulActivation_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26FusedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30TransposeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MatMulFpQ4_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MatMulInteger16_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Tokenizer_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25ExpandDims_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MaxpoolWithMask_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23Sampling_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GreedySearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32WhisperBeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25BeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33DynamicTimeWarping_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27UnfoldTensor_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GridSample_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26IsAllFinite_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33BitmaskBiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26BiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20Trilu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29TorchEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvE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shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}A_scaleA_zero_pointB_scaleB_zero_pointC_scaleC_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.]. Its actual value is: , 'axis' must be in [ or UNDEFINED. Got: expected to have: Output tensor rank too smallinputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Shape of quantized tensor must be 1D.Required attribute axis is missingaxis must be in [-rank, rank)All inputs to Concat must have same rankCan not get shape initializer data!Unsupported non-raw-data data type!Inputs 0 shall be 3 dimensionsThe past input shall be 5 dimensionsReorderInput/onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccConstrain input and output types to signed/unsigned int8 tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Constrain input and output types to 8 bit tensors.w_scalew_zero_pointThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.Constrain 'y', 'x_scale' to float tensors.The type of BFP - must match with the BFPType enumbfp_typeEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.block_dimConstrain the input to float and bfloat.Constrain y to uint8.Constrain shape and strides to uint64.QuantizeBFPThe datatype to dequantize to.Constrain the input to uint8.Constrain y to float and bfloat16.DequantizeBFPConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.MulIntegerb_scaleConstrain input A, b_scale and output Y data type as float tensor.a_scaleConstrain input a_scale, b_scale and output Y data type as float tensor.data_scaledata_zero_pointreduced_scalereduced_zero_pointQLinearReduceMeanX_scaleX_zero_pointY_scaleY_zero_pointapply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.Number of neurons in the hidden layerR_scaleR_zero_pointConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.T8TVConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).z_scalez_zero_pointConstrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)input_scaleweight_scaleinput_zero_pointweight_zero_pointConstrain input and output types to int8 tensors.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantword_embedding_scaleposition_embedding_scalesegment_embedding_scalegamma_scalebeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointbeta_zero_pointlayernorm_outmask_index_outcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_inputcublasLt order of output matrix.order_outputscale_inputConstrain to float typesConstrain Scale to float32 typesQuantizeWithOrdercublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrdercublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_AcublasLt order of matrix Border_BcublasLt order of matrix Y and optional matrix Corder_Yscale_Ascale_Bscale_Yscale_CConstrain bias and scales to float32QOrderedMatMulHidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixorder_weightcublasLt order of global biasscale_Q_gemmscale_K_gemmscale_V_gemmQ_weightK_weightV_weightscale_Q_weightscale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale_QKT_softmaxscale_values_gemmConstrain scales to float32 tensors.QOrderedAttentioncublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.order_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale_XConstrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization scale must be float tensors.quantization tensor must be int8 tensors.QOrderedLayerNormalizationcublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.Constrain scales to float32QOrderedGeluorder_global_weightscale_weightscale_biasscale_qkv_gemmscale_global_weightscale_global_gemmscale_outputBe compatible with float version.QOrderedLongformerAttentionFirst input does not have rank 2Second input does not have rank 2Unsupported type:axis must be in [-rank, rank-1]. input rank was *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35QLinearConvTranspose_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41QOrderedLayerNormalization_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QOrderedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34DequantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QuantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25QAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20QGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27QLinearWhere_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28QLinearConcat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QLinearSoftmax_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QLinearReduceMean_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35MatMulIntegerToFloat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36DynamicQuantizeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MulInteger_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28DequantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QuantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DequantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QuantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_15Crop_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26GivenTensorFill_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GivenTensorFill_Onnx_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28NhwcFusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvEUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvENKUlRN4onnx8OpSchemaEE_clES3_EUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QLinearConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33QLinearAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39QLinearGlobalAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26NhwcMaxPool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNHWCSchemaWithActivationERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_̼+std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator; reference = onnx::TypeProto&; size_type = long unsigned int]Graph attribute inferencing failed: This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&] in node No Graph instance was found for attribute std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Graph*; _Alloc = std::allocator; reference = onnxruntime::Graph*&]Source and target must both be tensors , or optional typed entities , or sparse tensorsMergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.Output:Tensor element type mismatch. void onnxruntime::Node::ToProto(onnx::NodeProto&, bool) constRemovable attributes were removed before the conversion is started.const onnxruntime::ConstPointerContainer::T* onnxruntime::ConstPointerContainer::at(size_t) const [with Container = std::vector; T = onnxruntime::NodeArg; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hindex < data_.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::TypeProto; _Alloc = std::allocator; reference = onnx::TypeProto&]Cannot find a function builder that satisfies the requested opset version: op_type = , opset_version = void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid node indexes specified when adding edge.Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid node indexes specified when removing edge.Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [] op_type [], could not find NodeArg LoadAttributeOrtFormat/onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.ccNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Serialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missing must be either specified in graph inputs or graph initializers.InitInputsInitializersOutputsError: Duplicate definition-site for (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, bool)graph_proto cannot be nullgraph_proto != nullptrDuplicate constant node sparse initializer name: '' Model is invalid.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: 'Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.This is an invalid model. Tensor does not have type information. appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph ctor should have created NodeArg for initializer. Missing:node_argThis is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Subgraph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]This is an invalid model. Error: the graph is not acyclic.This is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types ( in node (InferAndVerifyTypeMatchstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnx::TypeProto; _Alloc = std::allocator; const_reference = const onnx::TypeProto&; size_type = long unsigned int]index=Node () Op () output arg () type inference failed, inferred types: Type Error: Type () of output arg () of node () does not match expected type (Node:VerifyNodeAndOpMatchtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FunctionTemplate; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FunctionTemplate&]Fatal error: ) is not a registered function/opThis is an invalid model. The sum of input arg count is not equal to size of input defs in node () attribute () is required but not specified.This is an invalid model. In Node, (") : ("") -> (, Error PerformTypeAndShapeInferencingSize mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Subgraph input missing type.InferAndVerifySubgraphTypesN4onnx16OpSchemaRegistryEN11onnxruntime20InferenceContextImplEN4onnx15GraphInferencerEN11onnxruntime19GraphInferencerImplEN11onnxruntime5GraphEN4onnx24FunctionBodyBuildContextEN4onnx28FunctionBodyBuildContextImplEForThisAndAllSubgraphs_token_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; reference = const onnxruntime::NodeArg*&]Resolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const ResolveOptions&)Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)AddInitializedTensor already has tensor with name but different TensorProto.existing->second == &tensorvoid onnxruntime::Graph::RemoveInitializedTensor(const std::string&)sparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0graph_proto_ is not in sync with name_to_initial_tensor_.!foundbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)Can't remove node as it still has output edges.node->GetOutputEdgesCount() == 0void onnxruntime::Graph::SetInputs(gsl::span)Input to set must exist.input->Exists()gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]NoOpvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constOuter scope node arg name ''was added but does not exist. onnx::GraphProto onnxruntime::Graph::ToGraphProto() constFailed to convert dense initializer to sparsevoid onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set >*)Cannot find NodeArgs for [initializer_node_arg != nullptrCleanUnusedInitializersAndNodeArgsRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg '_if_Unable to constant fold If node: '' Unable to fetch: InlineIfSubgraphonnxruntime::common::Status onnxruntime::Graph::InlineIfSubgraph(bool, onnxruntime::Node&, const onnxruntime::logging::Logger&)Initializer name: from graph: conflicts with graph initializer. Check name generation above.insert_result.secondNode's: input: is not If node's input or previous node output in this subgraphConstant folded (inlined) for If node: Duplicate initializer (dense or ConstantNode): 'LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: LoadValueInfoOrtFormatNull type info for Node index is out of rangeLoadEdgesFromOrtFormatinput index: is not the same as this node's index:input edgesoutput edgesvoid onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&)nullptr != func_meta_defdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const std::string&)Schema was not found for fused node. Domain: OpType: SinceVersion:SetOpSchemaFromRegistryForNode(fused_node)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::OpSchema; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::OpSchema&]absl::container_internal::raw_hash_map<>::at_inlfunc_onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&)Node has no function body and cannot be inlined.callnode.TryGetFunctionProto(inlined_fp)void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = false]/onnxruntime_src/onnxruntime/core/graph/function_utils.ccNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >&]void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = true]InlineFunctionProtoInlineFunction in inlined subgraph: conflicts with graph initializer. Check Specializing code.*ZN11onnxruntime5Graph49PopulateNodeArgToProducerConsumerLookupsFromNodesEvEUlRKNS_7NodeArgEbE_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.location*/_ORT_MEM_ADDR_/*const std::vector& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const/onnxruntime_src/onnxruntime/core/graph/graph_viewer.ccInvalid ExecutionOrderint onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const std::string&, bool)Attempting to get index by a name which does not exist:for node: itr != node_args.end()const std::string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)Attempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()const std::string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)Attempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()void onnxruntime::GraphViewerToProto(const GraphViewer&, onnx::GraphProto&, bool, bool)/onnxruntime_src/onnxruntime/core/graph/graph_proto_serializer.ccstd::pair, onnx::AttributeProto>, false, true>, bool> onnxruntime::utils::SetNodeAttribute(onnx::AttributeProto, onnxruntime::NodeAttributes&)/onnxruntime_src/onnxruntime/core/graph/node_attr_utils.ccAttributeProto must have a name.utils::HasName(attribute)void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const std::string&, const std::string&)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; reference = onnxruntime::graph_utils::GraphEdge&]bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)Node must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.ALLOW_RELEASED_ONNX_OPSET_ONLYbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()/onnxruntime_src/onnxruntime/core/graph/model_load_utils.h are '0' and '1'. The environment variable contained the value: void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map, int>&, const onnxruntime::logging::Logger&, bool, const std::string&, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain less than 0.Protobuf parsing failed.void onnxruntime::Graph::KahnsTopologicalSort(const std::function&, const std::function&) constSome nodes are not included in the topological sort, graph have a cycle.onnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*)IndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:nodeargMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: onnxruntime::Model::Model(onnx::ModelProto&&, const onnxruntime::PathString&, const onnxruntime::IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&)ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Missing model IR version.Unsupported model IR version: , max supported IR version: ModelONNX 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.No graph was found in the protobuf.Failed to load model with error: SaveModel is less than 0.Saveonnx.ModelProtoProtobuf serialization failed.SaveModelWithExternalInitializers is less than 0.SaveWithExternalInitializersonnx::GraphProto onnxruntime::Graph::ToGraphProtoWithExternalInitializers(const std::string&, const onnxruntime::PathString&, size_t) constexternal_stream.is_open()ToGraphProtoWithExternalInitializersLoadOpIdentifierOrtFormat/onnxruntime_src/onnxruntime/core/graph/op_identifier_utils.ccLoadRuntimeOptimizationRecordFromOrtFormat/onnxruntime_src/onnxruntime/core/graph/runtime_optimization_record_container.ccAttempting to load runtime optimization records for a previously loaded optimizer: *ZN11onnxruntimeL40SaveRuntimeOptimizationRecordToOrtFormatERN11flatbuffers17FlatBufferBuilderERKNS_25RuntimeOptimizationRecordERNS0_6OffsetINS_3fbs25RuntimeOptimizationRecordEEEEUlmE_N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io19ZeroCopyInputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io15FileInputStreamEN11onnxruntime19PriorityNodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_N11onnxruntime11NodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_Received null OrtThreadingOptionsTinReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringSize of affinity string must be between 1 and std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >&; size_type = long unsigned int]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >]OrtThreadPoolParams { thread_pool_size: auto_set_affinity: allow_spinning: dynamic_block_base_: stack_size: affinity_str: set_denormal_as_zero: std::deque<_Tp, _Alloc>::const_reference std::deque<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; const_reference = const std::shared_ptr&; size_type = long unsigned int]Quant GEMM format: AIsSigned(), BIsSigned() is not supported on this deviceonnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto&, std::unique_ptr&, const onnxruntime::InlinedHashMap, int>&, const onnxruntime::InlinedHashMap, int>&)::No opset registered for domain in function opset imports.it != opset_imports.end()domain_version != -1Too many inputs for op static_cast(i) < node_op_schema->inputs().size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::pair, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = std::pair, std::__cxx11::basic_string >&; size_type = long unsigned int]ToutTAggregatedTypesall_tensor_types_ir4std::unique_ptr onnxruntime::function_utils::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, bool)input_arg->Type() != nullptrstd::vector > onnxruntime::concurrency::ReadThreadAffinityConfig(const std::string&)/onnxruntime_src/onnxruntime/core/util/thread_utils.ccProcessor id must consist of only digits: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)stoiProcessor id must start from 1: processor_from > 0 && processor_to > 0Invalid processor interval: processor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]Failed to read affinities from affinity stringReadThreadAffinityConfigFound invalid processor id in affinity string: , skip affinity settingFound out-of-range processor id in affinity string: std::unique_ptr onnxruntime::function_utils::CreateSchema(const std::string&, const std::string&, const std::unordered_map, const onnx::FunctionProto*>&, const std::unordered_map, int>&, const onnxruntime::SchemaRegistryManager&, const onnxruntime::logging::Logger&, bool)The given function name: , domain: is not found in model local functionsonnx_func_protostd::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(onnxruntime::Env*, OrtThreadPoolParams)Number of affinities does not equal to thread_pool_size minus one, affinities: , thread_pool_size: actual_num_affinities == static_cast(options.thread_pool_size) - 1custom join thread function not setunnamed_thread_pool*Z13MlasGemmBatchRK28MLAS_GEMM_QUANT_SHAPE_PARAMSPK27MLAS_GEMM_QUANT_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z17MlasHalfGemmBatchmmmmPK26MLAS_HALF_GEMM_DATA_PARAMSPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN11onnxruntime14function_utils12CreateSchemaERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES8_RKSt13unordered_mapIS6_PKN4onnx13FunctionProtoESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIS7_SD_EEERKS9_IS6_iSF_SH_SaISI_IS7_iEEERKNS_21SchemaRegistryManagerERKNS_7logging6LoggerEbEUlRNSA_16InferenceContextEE_*ZN11onnxruntime14function_utilsL22IOTypeConstraintHelperERKN4onnx13FunctionProtoERSt10unique_ptrINS1_8OpSchemaESt14default_deleteIS6_EERKNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEiSaISt4pairIKSH_iEEEESO_EUlRKNS1_9NodeProtoEE_bad mlas activation kindZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRKSt8functionIFvlEEEEvPS1_lOT_lEUllE_*Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z15MlasSBGemmBatchmmmmPK23MLAS_SBGEMM_DATA_PARAMSPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z21MlasQ4GemmBatchDriverI24MLAS_Q4_GEMM_DATA_PARAMSEv19MLAS_BLK_QUANT_TYPEmmmmPKT_PN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi256ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi256ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi128ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi128ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi64ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi64ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi32ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi32ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi16ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi16ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z19MlasSQNBitGemmBatchmmmmmm24MLAS_SQNBIT_COMPUTE_TYPEPK28MLAS_SQNBIT_GEMM_DATA_PARAMSPvPN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN12_GLOBAL__N_128InitializeWorkspace_CompInt8EmmmmmPK28MLAS_SQNBIT_GEMM_DATA_PARAMSPvmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN12_GLOBAL__N_124SQ4BitGemmPackQuantBDataEmmmPKSt4bytePS0_PN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_AAAAAAAA?@@@N3N3N3N3Q5@.Q5@.Q5@.Q5@.;A8;A8;A8;A8X} 2?~>2?~>2?~>{1{1{1{1++++++++6666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?33{@33{@33{@33{@;;;;A A A A EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>l?l?l?l?fȹfȹfȹfȹ7777~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>¥¥¥mL:mL:mL:mL:e9 *>*>*>DDDDDDDDŽŽŽŽŽŽŽŽbbbbbbbb99999999[[[[[[[[@@@@@@@@ffffffffVVVVVVVV88888888Ȁconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::concurrency::ThreadPoolParallelSection; _Dp = std::_Optional_base]libFailed to load library Failed to get symbol cores_countcpuinfo_get_%s called before cpuinfo is initializedSUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROROKSystemError[ONNXRuntimeError]! std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat; _Alloc = std::allocator; reference = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat&; size_type = long unsigned int]mu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode  is not a valid year_month_dayVIWEF' is required to be non-empty.value_infoname' is required but missing.elem_typekey_typevalue_typeUnrecognized 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_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.) is stored externally but doesn't have a location.) should not be empty.Location of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: ' points outside the directory, but the ') should be file inside the , but it doesn't exist or is not accessible.) should be stored in , but it is not regular file.) 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: Unrecognized data_type (tensor name: sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0Sparse tensor () dimensions are not positive.) must have INT64 type.Sparse tensor indices () has values, but NNZ is ) index value at position [] out of range [0, ] 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.) must have rank 1 or 2.) has no index values.DeleteFolderDeleteFolder(): nftw() failed with error: LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadTensorShapeOrtFormatLoadTensorTypeAndShapeOrtFormatNull sequence type info. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.Null map type info. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Type: is not supported currentlyParsePathRoot/onnxruntime_src/onnxruntime/core/common/path.ccFailed to parse path root: Parsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString&)onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()enabled_onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string&)/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast(common::OK) file "" failed: Invalid fd was supplied: fstatReceived negative size from stat callcloserealpathDistributionEnqueueDistributionWaitRevokeWaitstd::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()Profiler not started yet{"main_thread": {"thread_pool_name": "", std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()LogStart must pair with LogEndpoints_.empty()"thread_id": "thread::id of a non-executing thread", "block_size": [], "core": }, "sub_threads": {": {"num_run": "core": }}void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)!points_.empty()void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >; reference = std::chrono::time_point > >&]void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function&)n >= 0void onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(onnxruntime::concurrency::ThreadPoolParallelSection&, std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]/onnxruntime_src/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.hMore work items than threadsn <= num_threads_ + 1void onnxruntime::concurrency::ThreadPoolTempl::RunInParallel(std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&)Negative thread index is not allowedindex >= 0custom_create_thread_fn returned invalid handle.pthread_attr_init failed, error code: error msg: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: \/onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)UnmapFilemunmap failed. error code: Failed to close file descriptor - error code: ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()lseekReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapnftw_removeremove() failed. Error code: , path: ThreadMaincpu does not exist, skipping it for affinity settingpthread_setaffinity_np succeed for thread: , index: , mask: constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]pthread_setaffinity_np failed for thread: . Specify the number of threads explicitly so the affinity is not set.coreprocessorattrtype field and data field mismatch in attribute ) should not contain more than one value field.Attribute (name: ) should refer to attribute in parent node.N4onnx7checker15ValidationErrorEZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE26ScheduleOnPreferredWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionERN4absl12lts_2024011613InlinedVectorIiLm11ESaIiEEEjjSt8functionIFvjEEEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE21RunInParallelInternalERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjbSt8functionIFvjEEEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjlEUljE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_*ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE0_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_?{Gz?seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(bfloat16))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))then_branch and else_branch produce different number of outputs. but subgraphs produce If node has std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; reference = const onnx::TypeProto*&; size_type = long unsigned int] else= then=Mismatched type for output Mismatched tensor element type for output Invalid data type Invalid tensor data type tensor(seq(optional(map(sparse_tensor(Unsuported type proto value case.DataTypeUtils::FromDataTypeString - Received invalid data type string 'op_type) has zero input and zero output., type: NodeProto (name: No opset import for domain '' appeared multiple times.ai.onnx.mlai.onnx.trainingNo Op registered for with domain_version of Op registered for is deprecated in domain_version of ) has input size not in allowed input sizes.) has output size not in allowed output sizes.has output size ) has more inputs ( is marked single but has an empty string in the graph)'s input ) has more outputs ()'s output for operator Unrecognized attribute: Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'type_proto' has unknown expected type' is missing.Required attribute '' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'initTensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input '' of node: is not output of any previous nodes. OpType: ' has been used as output names multiple times.!empty()void std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::pop_back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator]Warning: Model contains experimental ops:Bad node spec for node. Name: ==> Context: declared= inferred=Mismatched type:Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type.target sequence type missing element type.source optional type missing element type.target optional type missing element type.source map type missing key type.target map type missing key type.Mismatched map tensor key type:source map type missing value type.target map type missing value type.Input tensor must be 2-dimensionalAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorv_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.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv9.All Tensor types up to IRv4.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.All Tensor and Sequence typesLower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Constrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } was not a tensor.Scan input std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; reference = const onnx::TensorProto*&] outputs. Expected Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output ) 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 (Loop 'body' subgraph outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output should specify a shapeAttribute Negative values are not allowed in a shape specification*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ConstantOfShape_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Enable broadcastingbroadcastIf set, defines the broadcast dimensions. Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) TwoOverPi = Constant () TwoOverPiCast = CastLike (TwoOverPi, X) C0 = Constant () C0Cast = CastLike (C0, X) SqrtTwoOverPi = Sqrt (TwoOverPiCast) Three = Constant () ThreeCast = CastLike (Three, X) XCubed = Pow (X, ThreeCast) XCubedC0 = Mul (C0Cast, XCubed) XC0XCubed = Sum (X, XCubedC0) TanhInput = Mul (SqrtTwoOverPi, XC0XCubed) ErfApprox = Tanh (TanhInput) PhiApprox = Sum (OneCast, ErfApprox) MultX = Mul (HalfCast, X) Y = Mul (MultX, PhiApprox) Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) Two = Constant () TwoCast = CastLike (Two, X) SqrtTwo = Sqrt (TwoCast) XSqrt = Div (X, SqrtTwo) ErfXSqrt = Erf(XSqrt) Phi = Sum (OneCast, ErfXSqrt) MultX = Mul (HalfCast, X) Y = Mul (MultX, Phi) output = Identity (input)input_less_than_min = Less (input, min)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)List of tensors for weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)const_zeroconst_oneexpanded_target = Unsqueeze (target, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_ignore_index const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) input_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)const_one_floatconst_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) X_ReduceMax = ReduceMax (input, axes) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorShapeProto*; _Alloc = std::allocator; reference = const onnx::TensorShapeProto*&]X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)output = Cast (X_greater)X_ReduceMax = ReduceMax (input) = Constant() X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels)Shape3D X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) log_prob = Identity (X_Log)All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_float' expect a float.Attribute 'value_string' expect a string.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute '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.Input tensor must be 1-dimensionalinput and zero_point pair is expected to have same type.Invalid rank for ) vs ( broadcasting: (Wrong op_type name for running propagation: Constrain input/output to boolean tensors.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } Value of alpha.Value of beta. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEPFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25BinaryBitwiseDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25BinaryBitwiseDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23ImageDecoder_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. periodicA {name} window with length: size. The output has the shape: [size].Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).size input must be a scalar.size input must be greater than 0.num_mel_bins input must be scalar.dft_length input must be scalar.Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.Rank of input 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. 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. Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).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 maxWhether 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.Number of top elements to retrieveconst_zero_castedconst_one_castedexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64transform_targetsinput_gather_element_transformsqueeze_maskweight_gather_tempweight_gather_temp_1X_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_Logstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::FunctionBodyHelper::AttributeProtoWrapper; _Alloc = std::allocator; reference = onnx::FunctionBodyHelper::AttributeProtoWrapper&]*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15STFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26MelWeightMatrix_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx29CosineSumWindowOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29CosineSumWindowOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.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.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.p value of the Lp norm used to pool over the input data.Constrain input and output types to float and 8 bit tensors.'shape' input must be 1D tensorRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Invalid value for attribute axisInvalid value for attribute kMaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specified) for attribute 'axis'Invalid value(Attribute pads must have an even sizeinput must have rank 3.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. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_meaninput_varrunning_meanrunning_varConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain input to only numeric types. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overScaling parameter.The exponent.Constrain input and output types to float tensors.Input is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } 1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.image_shapeblock_shapeConstrain input and output types to all numeric tensor types.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.Stride along each spatial axis.Stride along each axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that 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.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.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.saved_meansaved_var Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain mean and variance types to float tensors. It allows all float type for U.Attribute pads has incorrect sizeThe pads attribute cannot be used simultaneously with auto_pad attributeNumReducedAxes = Sub (Rank, Axis1D)NumReducedAxes = Neg (Axis1D)FloatEpsilonEpsilon = Cast (FloatEpsilon)XShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)XU = Cast (X2D)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)NormalizedT = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedT, Scale2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)ExponentEpsilonX_RMEX_squaredX_squaredE_XsquaredVarianceSTDX_varianceProcessed_STDSt15_Sp_counted_ptrIPN4onnx13FunctionProtoELN9__gnu_cxx12_Lock_policyE2EE*ZN4onnx11GetOpSchemaINS_29GroupNormalization_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_29LayerNormalization_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Col2Im_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21DeformConv_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_dilation value along each spatial axis of the filter.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::Common::Status::State; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::Common::Status::State&]The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).select_last_indexConstrain 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).{empty_value}The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer.(line: column: [ParseError at position Error context: Expected character infinfinitynanIdentifier expected but not found.Integer value expected, but not found.Expecting primitive type as map key type.Unexpected type in sparse-tensor element type.Unexpected type.Error parsing TensorProto (expected a tensor type).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Unexpected literal type.String value expected, but not found.Unhandled type: %dstolMismatch between expected type and specified value's typeUnexpected attribute type.Empty list attribute value requires type annotation.Singleton attribute value cannot be specified as a list.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } OptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information.OptionalHasElement is expected to have 1 input.Input must be an optional-type value containing an element with type information.'axis' must be in [-rank(indices), rank(indices)-1]Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.*ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25ReduceDocGenerator_opset1EPKcS1_iEUlRNS_8OpSchemaEE_*ZZN4onnx25ReduceDocGenerator_opset1EPKcS1_iENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_*ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_32DynamicQuantizeLinear_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_11EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx26LpPoolOpSchemaGenerator_11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Duplicate type constraint namestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema::TypeConstraintParam; _Alloc = std::allocator; reference = onnx::OpSchema::TypeConstraintParam&]std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnx::FunctionProto; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnx::FunctionProto]ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())Error parsing function body:Extra unparsed input unexpected.Attribute dtype should be of integer type and specify a type.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=Input type for input at index 0 is null. Type info is expected. Value=Invalid value of attribute 'axis'. Rank=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=new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[Constrain input and output types to numeric and Boolean tensors. Computes the {name} of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].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.output_sequenceaxes as an input and attribute cannot be specified at the same time.First input tensor must have rank 3 Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) (Optional) The data type of the tensors in the output sequence. The default type is 'float'.Constrain input types to any tensor type.positionConstrain to any tensor type.Constrain 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).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.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.SequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index Element type of inputs are expected to be the same.Input type was null expected to have tensor or sparse tensor type. Got: unknownElement type of input expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; reference = onnx::OpSchema::FormalParameter&; size_type = long unsigned int]SequenceMap_loop_body_itercount_cond_in_cond_out_inSequenceMap__seqlen_cond_seqempty*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_*ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx17ReduceOpGeneratorEPKcS1_bbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEbEUlRS6_E_*ZZN4onnx17ReduceOpGeneratorEPKcS1_bbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEbENKUlRS6_E_clESD_EUlRNS_16InferenceContextEE_k2axes_input = Constant ()x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)padded_sh = Max(x_shape, shape)pad_amount = Sub(padded_sh, x_shape)pad_amount_left = Div(pad_amount, k2)pad_amount_right = Sub(pad_amount, pad_amount_left)pads = Concat (pad_amount_left, pad_amount_right)padded_input = Pad (input_data, pads, , axes_input)x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)sh_diff = Sub (x_shape2, shape)start_dims = Div (sh_diff, k2)end_dims = Add (start_dims, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)x_shape = Shape (input_data)padded_input = Pad (input_data, pads)x_shape2 = Shape (padded_input)output_data = Slice (padded_input, start_dims, end_dims)Mismatch between number of inferred and declared dimensions. inferred=Unexpected axis value: . Expected range [ is referred to more than once.Axis output = Cast (input) expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input expected to have map typeKey type of map input Value type of map input expected to have type but instead is nullElement type of tensor or sparse tensor input was unknown does not match existing output type of Input element type of Output was expected to have tensor type. Got Element type of sequence input was unknownElement type of optional input was unknownKey type of map input was unknownValue type of map input was unknownInput was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensor. It must be 4 for 2D or 5 for 5D.Length of input 'size' is axis must be in [-rank, rank-1].Input rank for starts and ends should be the same: ('step' cannot be 0 for Sliceindices must be in [-rank, rank-1].Indices tensor must have rank >= 1'input' must have rank >= 2'sequence_lens' must have rank of 1Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..stepsA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedType of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).squeezedexpandedBlocks of [blocksize, blocksize] are moved.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.repeatsConstrain repeat's type to int64 tensors.Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .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".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated. This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.roisizesConstrain roi type to float or double.Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.Constrain input `X` and output `Y` types to all tensor types.(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).(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).depth(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).inverse_indiceslength of each output. Values should be >= 0. is out of bounds.*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21AffineGrid_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_output = Cast (input)) is not equal to the existing dim value (Dimension value inferred (List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The 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". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).constant_valueConstrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.outputs...Which axis to split onList 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`.paddingsOne 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.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.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'.Axes 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 inserted(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. Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. ) and outputs (Mismatch between number of splits (The input is not evenly splittablerank must be greater than axis) 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 lengthAttribute value for pads is requiredAttribute pads has incorrect lengthBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorEither `sizes` or `scales` must be provided, but not both of themUnknown value for `keep_aspect_ratio_policy`: Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".) does not match the number of axes (Number of elements of input 'sizes' () must be same as rank of input 'X' (Number of elements of input 'scales' (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeThe input tensor and grid tensor must have the same rank for GridSample. Got input tensor rank: Got grid tensor rank: The input tensor and grid tensor ranks must be >= 3. Got input tensor and grid tensor ranks: The last dimension of the grid tensor must be the rank of the grid tensor - 2. Got the last dimension of the grid tensor: *ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Qh\p{)kT5 unk__type case unsupported for symbolic shape inference. inferred= is undefined so it cannot be parsed.The type of tensor: Actual:. Expected:ParseData type mismatch for tensor: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::SparseTensorProto*; _Alloc = std::allocator; reference = const onnx::SparseTensorProto*&]Input shape must have either [C] or [1,C] dimensions where C > 0Exactly one of 'cats_*' attributes must be provided.ZipMap input shape should be 1D or 2D.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph. expected to have tensor or sparse tensor type: The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.First set of probability coefficients.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.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the default value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; reference = const onnx::TensorProto*&; size_type = long unsigned int], node name: (op_type:Data for input already exists.Inference error(s): type case mismatch. existing=Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension key type mismatch from MapProto. existing=type case unsupported. existing= optype . No opset import for domain Cannot infer type and shape for node name std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; const_reference = const onnx::OpSchema::FormalParameter&] typestr: , has unsupported type: has inconsistent type has unsupported type Container for generated shape data cannot be nullptr when enable_data_propagation option is set. were provided inputs but Graph has Cannot use the same name as both a subgraph initializer and subgraph input: were provided.The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: in initializers. Cannot find missing input: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::NodeProto; _Alloc = std::allocator; reference = onnx::NodeProto&; size_type = long unsigned int]N4onnx12assert_errorEFvRN4onnx22DataPropagationContextEEPFvRN4onnx22DataPropagationContextEEN4onnx8internal14MutableVisitorEN4onnx8internal15AttributeBinderEN4onnx22DataPropagationContextEN4onnx15shape_inference26DataPropagationContextImplEN4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplE*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22StringSplit_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25RegexFullMatch_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23StringConcat_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE0_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE_*ZN4onnx15PadDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx15PadDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_   onnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.Optionalonnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtog, Hi.OJbX;*8N4onnx18OperatorSetIdProtoEN4onnx19TensorProto_SegmentEN4onnx16TensorAnnotationEN4onnx22StringStringEntryProtoEN4onnx17TrainingInfoProtoEonnx.SequenceProtoonnx.MapProtoonnx.OptionalProto(cannot determine missing fields for lite message)[libprotobuf %s %s:%d] %s /build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl.ccCHECK failed: !is_closed_: /build/Release/_deps/protobuf-src/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): /build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (backup_bytes_) == (0): CHECK 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: (buffer_used_) == (buffer_size_): /build/Release/_deps/protobuf-src/src/google/protobuf/parse_context.hCan't happenclose() failed: Can't message of type "" because it is missing required fields: /build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./build/Release/_deps/re2-src/re2/regexp.ccRegexp not destroyed.RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistency/build/Release/_deps/re2-src/re2/nfa.cccontext does not contain textBad args: nsubmatch=typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Prog::Inst; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Prog::Inst&; std::size_t = long unsigned int]Unexpected opcode in short circuit: std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::NFA::Thread; _Alloc = std::allocator; reference = re2::NFA::Thread&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = const char*; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = const char*&; std::size_t = long unsigned int]SearchBitState inconsistencySearchNFA inconsistencyBad reference count Unknown encoding /build/Release/_deps/re2-src/re2/parse.ccBad call to ParseState::ParsePerlFlags\d\D\s\S\w\W/build/Release/_deps/re2-src/re2/walker-inl.hWalk NULLstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_stack.hvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]Error parsing 'Error compiling 'pattern too large - compile failedtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::InstCond; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::InstCond&; std::size_t = long unsigned int]/build/Release/_deps/re2-src/re2/onepass.ccunhandled opcode: ?ljWN3re216RepetitionWalkerEN3re26Regexp6WalkerIiEE09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az 09N6google8protobuf8internal19ImplicitWeakMessageEN4onnx13OptionalProtoEN4onnx8MapProtoEN4onnx13SequenceProtoEN6google8protobuf8internal16InternalMetadata13ContainerBaseEN6google8protobuf8internal16InternalMetadata9ContainerINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEE[: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:]INFOWARNINGFATAL00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = int&; std::size_t = long unsigned int][]^-\\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\unexpected errorNumCapturesWalker::ShortVisit called/build/Release/_deps/re2-src/re2/compile.ccCompiler::Copy called!Stack not empty.void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]RepetitionWalker::ShortVisit called/build/Release/_deps/re2-src/re2/simplify.ccCoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledUnexpected op in Regexp::Equal: W0 E?Rstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator; reference = re2::Regexp*&; size_type = long unsigned int]/build/Release/_deps/re2-src/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Job; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Job&; std::size_t = long unsigned int]should never happen/build/Release/_deps/re2-src/re2/dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteBad hex digit DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is (?:/build/Release/_deps/re2-src/re2/tostring.cckRegexpCapture cap() == 0?P<[^\x00-\x{10ffff}](?:)Bad final char: ?{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)|ohb*9Etstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::RuneRange; _Alloc = std::allocator; reference = re2::RuneRange&] [truncated]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]Unexpected opcode: No ranges in char classMissing case in Compiler: nqQ#7=\F0<StateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Resettypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]unhandled in AddToThreadqUnhandled in stepAddFoldedRange recurses too much.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Regexp*; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Regexp*&; std::size_t = long unsigned int]Malformed repeat Simplify case not handled: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator; reference = re2::Frame&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Splice; _Alloc = std::allocator; reference = re2::Splice&; size_type = long unsigned int]unknown round: Concat of RE2: unexpected op: AnyCase not handled in ComputeSimple: void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; reference = re2::Prog::Inst&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; reference = re2::Prog::Inst&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = short unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = short unsigned int&; std::size_t = long unsigned int]std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&]std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::front() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&]N3re214ToStringWalkerE  / / _ _ 00) ) ( ( G  ( ) / / _ _ 00Ƥpz|+0-g-o-p---GIlq:@[    ( * 9 < D F H J M U V X Z ] ] ` c f o w  ooppjjjj ª۪ߪ ^`|Pmpt`lnprs JMO`j,P0Y7?ywx  ??0?011111122*2G2P2P2`222223MMƤ(+6799wy@O55OOoo++<>||~~D D R R z | !!@!D!K!K!!!!!!!!!!!!!!!!!!!!" #!#|#|#####%%%%%%o&o&'''''')))))))*0+D+G+L+))bbdf \\^^^^``uu00 ![[jk>>@@ P$$   ?? 88iiŨΨ٨-0>7?ywx  ??^^``||~~uu   p p   O O y y ??446688mm@@ajt|D D R R z | !!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!@!D!J!M!O!O!!!!# #(#+#&$@$J$$$%g''''''))))))s+v++++,,P.Q.....////0000 0 06070>0?00011111122*2G2P2P2`222223MMƤ !(+69wy[[jk))@Obbdfii >>@@\\^^0S__      (([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.U.U.W.W.Y.Y.[.[.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bbooWW  ? ? 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 @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}8`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,/,00,_,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~Z0ZZZZZZZZçħħŧŧZƧƧuǧʧЧѧ֧٧SS`p0h!: AZ'((O(pz'|'''  @   @n_n `nn!""C@B /sys/devices/system/cpu/kernel_maxfailed to parse file %s: file is emptyfailed to parse file %s: "%.*s" is not an unsigned numberaespmullcrc32asimdcpuidjscvtlrcpcevtstrmatomicsasimdhpCPU variantCPU revisionCPU implementerCPU implementorCPU architectureAArch64/proc/cpuinfofailed to read file %s: insufficient buffer of size %zuhi6250hi6210sfthi3751hi3635Unknown/sys/devices/system/cpu/possiblefailed to parse the list of possible processors in %s/sys/devices/system/cpu/presentfailed to parse the list of present processors in %sfailed to parse both lists of possible and present processorsfailed to allocate %zu bytes for descriptions of %u ARM logical processorsfailed to parse processor information from /proc/cpuinfo/sys/devices/system/cpu/cpu%u/cpufreq/cpuinfo_max_freq/sys/devices/system/cpu/cpu%u/cpufreq/cpuinfo_min_freq/sys/devices/system/cpu/cpu%u/topology/physical_package_id/sys/devices/system/cpu/cpu%u/topology/core_siblings_list%s %s%s %s%u%.*sfailed to allocate %zu bytes for descriptions of %u logical processorsfailed to allocate %zu bytes for descriptions of %u coresfailed to allocate %zu bytes for descriptions of %u core clustersfailed to allocate %zu bytes for descriptions of %u microarchitecturesfailed to allocate %zu bytes for %u logical processor mapping entriesfailed to allocate %zu bytes for %u core mapping entriesfailed to allocate %zu bytes for %u uarch index mapping entriesfailed to allocate %zu bytes for descriptions of %u L1I cachesfailed to allocate %zu bytes for descriptions of %u L1D cachesfailed to allocate %zu bytes for descriptions of %u L2 cachesfailed to allocate %zu bytes for descriptions of %u L3 caches! eapanic: checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode Error reverse compiling 'Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.'axis' must be in [-rank(indices)-1, rank(indices)]'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Target shape may not have multiple -1 dimensions.Invalid position of 0.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Input's shape should be 1D or 2DBlocksize must be positiveInput tensor must be 4-dimensionalSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthAt least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set. are different, which is not permitted for LabelEncoders. and the key type The input type was At least one of values_tensor, values_strings, values_int64s, values_floats must be set. must be the same in the LabelEncoder. and the number of values The number of keys and the value type The default tensor type The default tensor must be a singleton 1D tensor.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::TensorShapeProto_Dimension*; _Alloc = std::allocator; reference = onnx::TensorShapeProto_Dimension*&; size_type = long unsigned int]'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. values. Got Input axes has invalid data'step' cannot be 0Both 'split' input and 'num_outputs' attribute were givenAttribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been givenCenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputsNumber of elements of input 'shape' ( does not match type of output: expected to have tensor or sparse tensor type and Output Invalid attribute perm {}, input shape = {Attribute perm for Transpose has repeated value:     "4AA#4AA #3ArA"#2AqA91ApA2ApA"4AS 4AA 4AA2AA2AA"4A 4A4AA AA4AAG 4AAQSDMSMAPQSnapdragon MTExynos K3VHiKirin ATMAMLBCMNuclun LCPXA6ATegra TTegra APTegra SLSurge SMPRKSCTCCOMAPWMQualcommMediaTekSamsungHiSiliconActionsAllwinnerAmlogicBroadcomLGLeadcoreMarvellMStarNovaThorNvidiaPineconeRenesasRockchipSpreadtrumTelechipsTexas InstrumentsUnisocWonderMediaError in cpuinfo: @@@`@   88 v0s0S0u0T0U0C = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (X, NewShape)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)Axes2Mean = ReduceMean (X3D, Axes2)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)ATenConstantFillAlpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) HalfOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) NormalizedV = Cast (Normalized)Scaled = Mul (NormalizedV, Scale2D)X_bias = Add (X, bias)X_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) axis value is invalid for a tensor of rank has rank All inputs to Concat must have same rank. Input Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.data tensor must have rank >= 1axis must be in [-r, r-1]This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.Attribute 'pooled_size' must be >= 1.Attribute 'max_output_boxes' must be >= 1.Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) + bottom_border () needs to be greater than or equal to the top_border () + right_border () needs to be greater than or equal to the left_border () + scale[0] () + scale[1] (size attribute in UnfoldTensor must greater than 0.size attribute in UnfoldTensor not specified!size attribute in UnfoldTensor not positive and less than the dim size! not specifiedValue of attribute should be of integer type and specify a type. does not specify a valid type.Mismatch between the sum of 'split' () and the split dimension of the input (One and only one attribute must be set out of expected to be a 1D tensor but was expected but not providedframe_length input must be scalar.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Output type must be int32 or int64Input tensor must have rank 2is_onesided and inverse attributes cannot be enabled at the same timeinput tensor must have rank >= 2, including the complex dimension.axis attribute value . Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'dft_length input must be a scalar.rank == static_cast(new_shape_proto.dim_size())%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()axis_tensor != nullptr%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointaxis input must be a scalar.K input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input. expected to have tensor or sparse type expected to have tensor typeAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedSecond input tensor has wrong dimensiononnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]not support normalize yet.!normalize_;f?F?&TpxAD?Input 'input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got Word embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1Attention cannot have both past and relative_position_biasInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes: q_hidden_size= k_hidden_size= v_hidden_size=bias_dims[0]=Input 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of past_sequence_length tensor must be of one element when past_present_share_buffer is setInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setvoid onnxruntime::If::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()void onnxruntime::BroadcastLooper(TBroadcastHelper&, const ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = contrib::{anonymous}::QLinearBroadcastHelper]BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()GetParameterssubgraph output present_key_self_0 is expected to have 4 dimension, got onnxruntime::common::Status onnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, int, const std::vector&, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateWhisperEncoderInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)cpu_allocator shouldn't be nullptronnxruntime::common::Status onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, const std::vector&, int, int, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateEncoderInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)onnxruntime::common::Status onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)Invalid hidden_states expension: has_hidden_state_ == falseonnxruntime::common::Status onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_binary_op.ccMatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = unsigned char; _Alloc = std::allocator; reference = unsigned char&; size_type = long unsigned int]???Conversion Erroren_US.UTF-8Conversion Errorstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&]Data type is not supported in this build.onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size != -1A dimension cannot be less than -1, got requested_shape[i] >= -1At most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:size != 0 && (input_shape_size % size) == 0input_shape_size == sizeonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) constinput != nullptrCannot concatenate scalarsRanks of input data are different, cannot concatenate them. expected rank: input_rank == reference_rankNon concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatchvoid onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = const float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = gsl::details::span_iterator; TSpanBIter = gsl::details::span_iterator; TSpanCIter = gsl::details::span_iterator]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long unsigned int; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/window_functions.cclower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned char; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = signed char; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = double; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = float; int64_t = long int]void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string&))(const float*, float*, float*, int, float, float)Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string&))(float*, const float*, const float*, float*, int, float, float)Invalid GRU hidden gate activation function: bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex] #<L>?Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::TensorShapeVector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, onnxruntime::TensorShapeVector*, bool) constinput_shape.Size() > 0 || input_shape[0] == 0void onnxruntime::PoolAttributes::InferOutputSize(gsl::span, onnxruntime::TensorShapeVector*, onnxruntime::TensorShapeVector*, bool) constinput_dims.size() >= 2onnxruntime::LayerNormImpl::LayerNormImpl(const onnxruntime::OpKernelInfo&, bool, bool)op_kernel_info.GetAttr("axis", &axis_).IsOK()GetVectorAttrsOrDefaultonnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, std::vector&) [with TH = double; std::string = std::__cxx11::basic_string]onnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, std::vector&) [with TH = float; std::string = std::__cxx11::basic_string]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hPer-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = float; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()AVERAGEMINtarget_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty()nodes_modes.size() < std::numeric_limits::max()BRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQNode in tree is already there.(coor.node_id >= 0 && coor.node_id < n_nodes_)Unable to find node (truenode).A node cannot point to itself: (falsenode).Number of nodes in nodes_ () is different from n_nodes ( (weights).onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = double; ThresholdType = double; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; reference = onnxruntime::ml::detail::TreeNodeElement*&]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = long int; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = int; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = double; ThresholdType = double; OutputType = float]PThe attributes in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]void onnxruntime::math::Gemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, ptrdiff_t, ptrdiff_t, ptrdiff_t, T, const T*, const T*, T, T*, Provider*) [with T = double; Provider = onnxruntime::concurrency::ThreadPool; ptrdiff_t = long int]CblasNoTrans Unexpected CBLAS_TRANSPOSE for TransB of CblasTrans Unexpected CBLAS_TRANSPOSE for TransB of Unexpected CBLAS_TRANSPOSE for TransA of void onnxruntime::math::Gemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, ptrdiff_t, ptrdiff_t, ptrdiff_t, T, const T*, const T*, T, T*, Provider*) [with T = Eigen::half; Provider = onnxruntime::concurrency::ThreadPool; ptrdiff_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == 2void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = int; ThresholdType = float; OutputType = float]T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = double; std::string = std::__cxx11::basic_string]External data type cannot be STRING.T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = long int; std::string = std::__cxx11::basic_string]left_num_dims and right_num_dims must be >= 1MatMul dimension mismatchonnxruntime::common::Status onnxruntime::MatMulComputeHelper::Compute(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&, bool, bool, bool, bool, bool)Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsonnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::cur1 == end1cur_out == end_outonnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::void onnxruntime::BroadcastLooper(TBroadcastHelper&, const ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = BroadcastHelper]Axis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`Input channels C is not equal to kernel channels * group. C: kernel channels: Output channels M is not divisible by group. M: void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constUnsupported AutoPad Type.void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.ccmin should be a scalar.min->Shape().IsScalar()max should be a scalar.max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long unsigned int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = float]kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape. channels_last: Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.ComputePadAndOutputShapeInferPadsAndOutputShapeDoubleQDQRemoved_ApplyRulesOnNodecom.microsoft.QuantizeLinearcom.microsoft.DequantizeLinearcom.microsoft.QLinearAddcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearConcatcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearMulcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoidstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]ir_versionopset_importproducer_nameproducer_versiondomainmodel_versiondoc_stringmetadata_propsmapoptionalstodstollstoftypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat&]libonnxruntime_providers_cuda.solibonnxruntime_providers_cuda_ut.solibonnxruntime_providers_cann.solibonnxruntime_providers_rocm.solibonnxruntime_providers_dnnl.solibonnxruntime_providers_openvino.solibonnxruntime_providers_tensorrt.solibonnxruntime_providers_migraphx.soFuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32Failed to convert mask to int32Start MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphMatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMaskunidir mask is not constantunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphCheckNodesInPathVStart CheckNodesInPathVFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedCheckDistilBertReshapeShapePass CheckNodesInPathVreshape initializer value is not expectedbool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const onnxruntime::Node&, const GetConstantInitializerFn&, bool&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_util.ccq_or_dq_input_defs.size() >= 2void onnxruntime::IncrementIndexAndComputeOffsetSetup(MultiIndex&, size_t, gsl::span, const gsl::span&, size_t)Method IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator; reference = int&]auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axisQLinearConvTransposecom.microsoft.experimentalorg.pytorch.atenThe input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.A list of strings.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.A default tensor.{"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.inttype_protofloatsintssparse_tensorstype_protoscomplex64complex128Incomplete string literal.Encountered invalid float literal!Value expected but not found.  std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; reference = nlohmann::basic_json<>&; size_type = long unsigned int]Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.Type mismatch. Current= Input=UpdateTypeAndShapeOptional Type mismatch. Expected: . Got: Failed memory size calculationconstexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = unsigned char; long unsigned int _Nm = 400; const_reference = const unsigned char&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = char; long unsigned int _Nm = 512; reference = char&; size_type = long unsigned int]\u%04xinvalid UTF-8 byte at index : 0xstd::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::back() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&]incomplete UTF-8 string; last byte: 0x�{}{ , ":[]"bytes": [], "subtype": {"bytes":[],"subtype":null}constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = nlohmann::detail::dtoa_impl::cached_power; long unsigned int _Nm = 79; const_reference = const nlohmann::detail::dtoa_impl::cached_power&; size_type = long unsigned int]AddConstantProtoAsInitializeronnxruntime::common::Status onnxruntime::Graph::AddConstantProtoAsInitializer(const onnx::NodeProto&, std::optional >)Constant node name: conflicts with graph initializer. Check that the node names have been made unique.LoadFromString/onnxruntime_src/onnxruntime/core/graph/op_identifier.hInvalid OpIdentifier string: Failed to parse since_version from op_kernel_type_str_args is null.op_id is null.kernel_type_str_args is null.kernel_type_str is null.Duplicate entry for kernel type str: Duplicate entry for op id: ReplaceInitializedTensorImplFailed to find existing initializer with name Trying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()onnxruntime::common::Status onnxruntime::utils::GetExtDataFromTensorProto(const onnxruntime::Env&, const char*, const onnx::TensorProto&, void*&, SafeInt&, onnxruntime::OrtCallback&)GetExtDataFromTensorProtoGetFileLength for GetFileContentExternal initializer: offset: size to read: given file_length: are out of bounds or can not be read in full.void onnxruntime::PlannerImpl::PartitionIntoStreams(const onnxruntime::logging::Logger&, const onnxruntime::ExecutionProviders&, const onnxruntime::PathString&)CreatePlanComputeReusePlanGenerateDeallocationPlanstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]SaveInitializedTensorsSaving initialized tensors.onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::__cxx11::basic_string&, const onnxruntime::GraphViewer&, const onnxruntime::AllocatorPtr&, const onnxruntime::OrtValueNameIdxMap&, const std::vector&, onnxruntime::ITensorAllocator&, const SaveTensorFunction&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&, const MemoryProfileFunction&)OrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (OrtValue index: from initializer_allocation_order not found among initialized tensorsentry != initialized_tensors_to_allocate.end()Can not trace string tensorentry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates bytes for Skipping entry for missing optional value at idx Using user supplied initializer with name (session.use_device_allocator_for_initializersDeserialize tensor Done saving initialized tensorsN11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextE   ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = unsigned int; _Alloc = std::allocator; reference = unsigned int&; size_type = long unsigned int]SaveRuntimeOptimizationRecordToOrtFormatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]/onnxruntime_src/onnxruntime/core/framework/endian_utils.ccCopyLittleEndiansource and destination buffer size mismatch Expected: Please fix either the inputs/outputs or the model.Got invalid dimensions for for the following indices Field '' of 'values of data_type '' should be stored in field '' instead of 'Cannot parse data from external tensors. Please load external data into raw data for tensor: Data size mismatch. Tensor: expected size does not match the actual size, max= not in range [min=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred= Dimension=EliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutExpandEliminationCastEliminationPreShapeNodeEliminationNoopEliminationDivMulFusionFuseReluClipGemmSumFusionGemmTransposeFusionNotWhereFusionConvAddFusionConvMulFusionConvBNFusionPad_FusionMatMul_BatchNormalization_FusionClipQuantRewriteReluQuantRewriteLevel_RuleBasedTransformerGenerateRuleBasedGraphTransformerstd::unique_ptr onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&, const onnxruntime::InlinedHashSet >&)static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = std::__cxx11::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]RegisterOpSchemaBuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=) in op definition.) than declared (UnpackInitializerDataUnsupported type: K`s=R(g|8SaveInitializerOrtFormatSerialization of fused function body is not currently supported, Node [Removable attributes were removed before the node is saved.does not have the graph for key SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&)Must have a valid data typeHasDataType(dense_proto)DenseTensorToSparseTensorProto data_type: SaveSparseInitializerOrtFormatSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::fbs::EdgeEnd; _Alloc = std::allocator; reference = onnxruntime::fbs::EdgeEnd&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]GetExternalDataInfoTensor does not have external data to read from.model format error! Need a key for the external data infomodel format error! Need a value for the external data infoparsing checksummodel format error!model format error! Missing 'location'External data type cannot be UNDEFINED or STRING.TensorProto: external data size mismatch. Computed size: , external_data.length: resultstype must be object, but is validatorsCannot find execution provider SetTuningResultsInvalid TuningResults (index= does not support TunableOp.Failed to load TuningResults (index=). Reason: Correctly set TuningResults for , enable TunableOp for usingValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2Expecting index blocks: to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}N11onnxruntime15ReluQuantFusionEN11onnxruntime15ClipQuantFusionEN11onnxruntime14MatmulBNFusionEN11onnxruntime9PadFusionEN11onnxruntime12ConvBNFusionEN11onnxruntime13ConvMulFusionEN11onnxruntime13ConvAddFusionEN11onnxruntime14NotWhereFusionEN11onnxruntime19GemmTransposeFusionEN11onnxruntime13GemmSumFusionEN11onnxruntime12FuseReluClipEN11onnxruntime12DivMulFusionEN11onnxruntime15NoopEliminationEN11onnxruntime23PreShapeNodeEliminationEN11onnxruntime15CastEliminationEN11onnxruntime17ExpandEliminationEN11onnxruntime16EliminateDropoutEN11onnxruntime20UnsqueezeEliminationEN11onnxruntime14EliminateSliceEN11onnxruntime17EliminateIdentityEFLOATINTSTRINGTENSORFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORS/build/Release/_deps/protobuf-src/src/google/protobuf/stubs/stringpiece.ccsize too big: details: string length exceeds max sizeparse_errorparse error at line , column out_of_rangetype_errortype must be number, but is invalid_iteratorL"%(+.147:=@CFI/\std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; reference = onnxruntime::utils::data_types_internal::TypeNode&] Version mismatch. node_version: kernel start version: kernel_end_version: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeAndMoveInfo; _Alloc = std::allocator; reference = onnxruntime::NodeAndMoveInfo&]ArmLinuxInit/onnxruntime_src/onnxruntime/core/common/cpuid_info.ccFailed to init pytorch cpuinfo library, may cause CPU EP performance degradation due to undetected CPU features.vector::_M_fill_insertvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t, gsl::span)Tensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, InstanceType, const std::string*, int)/onnxruntime_src/onnxruntime/core/common/logging/logging.ccISink must be provided.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.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string&)Default logger already set. AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Available memory of is smaller than requested bytes of onnxruntime::BFCArena::Extend(size_t)::Incorrect arena extend strategy.Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t)0 == memory_size % kMinAllocationSizeFailed to find a free memory block despite calling Extend. rounded_bytes=void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)std::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constIndex size: must be equal to or twice the values size: values_count == index_sizestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::profiling::EventRecord; _Alloc = std::allocator; reference = onnxruntime::profiling::EventRecord&]EndTimeAndRecordEventMaximum number of events reached, could not record profile event.const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_.has_value()onnxruntime::ExecutionFrame::ExecutionFrame(gsl::span, gsl::span, gsl::span, gsl::span, const std::unordered_map >&, const onnxruntime::DeviceStreamCollection*, const onnxruntime::SessionState&)buffers_.find(location) == buffers_.end()ExecutionFrameAllocation of memory pattern buffer for returned nullptr failed. Error:ExecuteThePlanSequentialExecutor::Executeai.onnx.preview.training, but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Fatal error in cpuinfo: std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Splice; _Alloc = std::allocator; reference = re2::Splice&]tensor_typesequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SETbasic_string::replacestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::RuntimeOptimizationRecord; _Alloc = std::allocator; reference = onnxruntime::RuntimeOptimizationRecord&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; reference = onnxruntime::graph_utils::GraphEdge&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SrcDstPair; _Alloc = std::allocator; reference = onnxruntime::IDataTransfer::SrcDstPair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper >; _Alloc = std::allocator > >; reference = std::reference_wrapper >&] [seqno= rows[ row[std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::StringPiece; _Alloc = std::allocator; reference = re2::StringPiece&]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = true]/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_kernel_base.hinfo.GetAttr("keepdims", &keepdims).IsOK()const onnx::TensorShapeProto& onnxruntime::utils::GetShape(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.hTypeProto must have shape for this to runshape != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, std::shared_ptr, ptrdiff_t, gsl::span)static std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_sequence_type_info.cctype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypestatic std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_map_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypebool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto&, const onnx::TypeProto&)onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = false]Failed to analyze start state.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::FunctionBodyHelper::NodeDef; _Alloc = std::allocator; reference = onnx::FunctionBodyHelper::NodeDef&]opaquevoid onnxruntime::utils::UpdateWithParentStream(onnxruntime::DeviceStreamCollection&, onnxruntime::Stream*)Subgraph has nodes running on device: while parent graph node running on device: , this is not supported yet.ExecuteGraphImplonnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, gsl::span, std::vector&, gsl::span, gsl::span)copy_info.size() == num_feedsfeed_streams.size() == num_feedsCopyInputsAcrossDevicesstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; const_reference = const onnxruntime::MLValueCopyInfo&; size_type = long unsigned int]CopyOutputsAcrossDevicessession.node_partition_config_fileFinalizeSessionStateImplsession.disable_prepackingconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]CreateKernelsonnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::__cxx11::basic_string&, const onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, onnxruntime::InlinedHashMap, long unsigned int>&, const onnxruntime::InlinedHashMap, OrtDevice>&, bool)Missing session state for subgraph. Node:' Index: Attribute:entry != node_to_subgraph_ss.second.cend()OuterScopeNodeArgLocationAccumulatorp_op_kernelstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&]constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = long int; long unsigned int _Nm = 6; const_reference = const long int&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockIO::BlockIteratorState; long unsigned int _Nm = 5; reference = Eigen::internal::TensorBlockIO::BlockIteratorState&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockIO::BlockIteratorState; long unsigned int _Nm = 5; reference = Eigen::internal::TensorBlockIO::BlockIteratorState&; size_type = long unsigned int]Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; reference = onnxruntime::ml::detail::TreeNodeElement*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&]SoftmaxCPU inputs N, D and N * D must be < std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::{anonymous}::ScaleMergeInfo; _Alloc = std::allocator; reference = onnxruntime::{anonymous}::ScaleMergeInfo&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const std::vector*; _Alloc = std::allocator*>; reference = const std::vector*&]vector::_M_insert_auxstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::FreeDimensionOverride; _Alloc = std::allocator; reference = onnxruntime::FreeDimensionOverride&] execution provider is not supported in this build. CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when 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