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C;QH$8dH+%(HHL[]A\A]A^A_AQr:D$6 H|$7D$0ErrofD$4DE1sHMHHcL6] 1L$~Qp@QfG AWAVAUIATUSH( LxAG01I_8H$D#IIG@LLD$ IADD$Dd$=MDDy L%P#1D$I$I9G@@u\L IMt?MyLAG0tLLL$m LL$IIAH@@I9$M뼃@"l$@ DD$D$  tL$  uL HHt@HkHLI LLHIE H}(HuH([]A\A]A^A_DDy uL%(#LLLL$I$I9G@@D$jH{LILL$HCIG8M1AL/ IMMqLAF0tLLL$ LL$I$LHD$IALH9P@T$AD׉T$LL$H{ILgLL$HCIF8LLLL$H{IL5D$MHCIG8fAWAVIAUATULSHHXHT$0L$Ht$HL$LL$ǀ(IL(HǀXHǀ`HǀhHxI9L;5Ӥ#0L;5!##ILJ@ILJHHD$ 1ɺH`H{AIHHD$HA!HCIPHD$8APD$DD3H|$HD$D$@D$,E1E1H\$DfA$t/A$A$tDdAA$uH|$ tI@L+D$@?H|$HD$0ЋD$,HX[]A\A]A^A_HD$I@H0Ҩ H#HIHHD$HD$ DHT$LHLÅ/fDH|$1ɾA$uHD$@HD$8;D$DuHD$Ht$HT$,HxHt$T$,HL$8HFfp1uHD$T$,HxT$,AA$Љ\$,EExH|$ IIPHHD$HHƺ#HL$81HIHH{IAAHCAPD$DD3Lt$L Lt$ D$@H|$ IHLLH=V HD$HD$ H= V f.@uDAW1ɺAVIAUATUSHI~CŅt-1E1LA-H[]A\A]A^A_fDI~I~IHIT$IFIB0uKL1IAE0tH5K#ID$H6H9p@tMI~A‰؃EEiIT$BHHJHu@HL[]A\A]A^A_ I~GIHD$ Bu8D$I~LHIFH$@0ubLLl$ l$IMt[IEI~LMH$IH$@0tH5d#ID$H6H9p@ul$l$ @IL$AHxyHu늋l$ uDAW1ɺAVAUATAU1SHH6H{=IHuHLHMt}H{LM~M:IEt#E1AG0tIUH#E1HH9B@AH{LHCAG0tIUBHHJHuzf.HtH[]A\A]A^A_HDH[]A\A]A^A_/f.DHIH APIHHHT E1HPHHHfHnHwflf.1HHH9t$HHVfHnflHQH fHWH9HEf.@HHVH7H~HHPfDATIHt Ht LA\fLfLA\fDHHtSHH6H[f.DHtH1f.HH9tH1f.AWIAVAUAATUHSHHD!HHHdž@DHdžXHdž`DEHF1E1HdžhD$ H$,fMDD U!NjAt֨uDD €ɀ U !uALJ(M(ID$ H{urHCfƒuA}tH<$ A}uD$ D$ A@DDAOH[]A\A]A^A_ÐHCD1u f1AuJЃÐ1ufDATUHHAuPu H]A\ÐH1IHt#LHHL]A\f1ufDATUHHauu H]A\@H1IHܲ#LHHL]A\fHt7UHSHHtHHHGPHt HvH;FPtH[]LGXLNXM9tHG`HtLLЅtH{HuHHhHh[]f.AUATUSHHHnL&IHSBHHJHuLwu)HCHHhHPhH9t&HppfHnflHqH@hHL[]A\A]L4H9tLHfAWAVAUATIUSH1H8C@q uK0ҁw?t/tNfD3 NjAL$AAu1΃ tAT$)!uH8[]A\A]A^A_Ð) u1ELcHD$(E1HCD$$D$ HD$fDL0CLL$$IDžH#HD$HL9MƒuMHD$HL$$L$`DH=L IOHAPHyXЅt|M H#IOHH9A@D$ MHD$MHL9LLaIOIHAPHtT$uHtHyXЅuIGH@pL@II9I;wLIMuD$$@LLt$(ILLH}LLHZLcHCHD$(\@LLH|$LI%HD$IGLh@LXD$$d$ d1ɺH D$$CH7H#HD$H1M;d$ D$$HD$(H|$HCDt$ H{EAAĊ AƀDEAD EDEA)D D!t@) T$ D!uH|$;HHLt$HHLELHH7HD$HE@ H}H'HumH v#IWH H9J@tHzPtƒD$$D$$f1ufDPu+ t tH#H0IftH=I MDH=I <f.f1ufDƒutEH=I fƒ⌁t tHP#H0H=I @AWAVAUATIUHSHHxdH%(HD$h1HHI IHI HLL0LxLLA}-HD$hdH+%(sHxD[]A\A]A^A_HCH\$8D$0+mrHt$0HHD$ HD$XIE0D$PHHD$D$`AETMMHI LLMLL$(o)D$HT$HL$LT$[A}qH|$D\$D\$#HD$hdH+%(HxH[]A\A]A^A_DAUHH LLHTI}hHLT$HLLL$(IEhLLLT$H|$LT$LT$H|$ LLAÃnuL A}H|$D\$oAETD\$trHG LLHHG LLD\$HPHHH D\$I}hHGD\$IEhfMU MM(VAEtMU MM(fDHaG LLHqDH|$&H|$ HLA (QfDSHtPu[H1ɺE1jE1H޿ QZYt} Q tt% @AUATIUHSH8H4$HT$dH%(HD$(1Et9PUuE1HD$(dH+%(H8D[]A\A]f.H)F Ht$LHPHHHoE1tfo$LD$)D$HE11jH1QHXZ]QD(AA tAnu=4An*Ht$LHH D% 'Qf.@HHDPuH1jE1E1QZYx H% H& .HHHHH?HH)Hi6eHHQHH1H9)u1H9)fATH5nD UHHL%#L&QHt"Hj QHLH%Q!QI$HxLw )QSHw@D$ D$ 9tD$ D$ D$ 9uHC[@H[AUAATAUSHHD1fDsDAuDDD D!uHD[]A\A]fƒt% S1HH=#SH=|#HHi#W#[f.ATUSH=s#\(QH(HtI܉ƒ1ɺH=#H=#IH#H#HHIA$#Il$ADŽ$+mrHI$0I$I$d1A$(ADŽ$8I$pLADŽ$P21ADŽ$HtNA$L[]A\@H=!#L)H#ID$L#HuH5LA$H=Pr#&QL(A$x+$QIu% DSHt[1ɺH=M# H=I#HH6#$#[f.H`AVAUATIUSGTHt3HT@ H8HpHPHHH7[]A\A]A^fDW H&@ H8HpH@ fDCT(H{hHhHChE H}(H{hFHHuH{`5HHt4LmH{`HMMu0LHAEPtVLnHu1H{0H5HdM$I|$`HID$`HCX[]A\A]A^LHL HgfH Hs(H? f.fAUATUHo0SHHHWCTtpH> H8HpHPHHH ~5LcXCPMt_Ml$0Lt~HLLkkPHH[]A\A]UDCt)H{ Hs(HJ> 1H@H9> H8HpH> TfDHLHxeAWAVAUATUSHGTt'H= L LhHLL[]A\A]A^A_@Lw0HL!ETtbL== M'MoLtM7MLLLL~LLHHHMMPrEu!H:= L=#= L Lhf.Le Lm(L== pf.fUHHH< HHPHHH4]fDH]f.DB#tmH1ɺtI@"#u;#uHH= #Q##tfD Q#uHUHHp=#H]"QG QAWAVAUATUHSHhHt$ HT$(HL$@LD$HLL$HHÝ#L0H\$ HH|$(Ӆ HAE1ЃD$THDIMfDHD$ L繀MHI(IXHD$(ILJ@I`HD$@IhIPALJ(HD$8APT$TD$PKI|$ AIHD$XWIHHI|$HID$A@A$AN)u @+A$u؅)HD$HD$0E11D$4HD$HDIAEttdAEt%AEuD$0H|$(HD$ 1ЅD$4 J1M IxHT$H$LHt$D$ vAEhA,$1@A,$CuUA$@"uӉ A$@ƒA$LLIHHaI|$HID$Wf.LLLAEt HD$89D$PtYA,$D$0l$ rLuD$4Hh[]A\A]A^A_D$4H=8 H#L0I|$Ht$X3ID$AEAnA,$D$0D$ D$4HX#L0hHH8 jLLHZYu HH=A8 H`AWAVAUATLgXUSHLHQHLk#1Hǃ0Hǃ@HǃHHHCX1f8Ho#HǃPHhLhHǃXH}H+HL/QHCH{@LsH~#HCHPH@HC HCXHs#HHHC(HCHC0HC8QH#LLCHHHCHk#HHCPQH[]A\A]A^A_IILempty(), true)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string&]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_vector.hinternal errorindex out of range__builtin_expect(__n < this->size(), true)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::basic_string&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]out of indexbasic_string::_S_construct null not validRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsmax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkInvalid key found: initial_growth_chunk_size_bytes was false.Stacktrace: Integer overflowstatic void SafeIntExceptionHandler::SafeIntOnOverflow()/onnxruntime_src/onnxruntime/core/common/safeint.hStrings can only reside in CPU memoryonnxruntime::SparseTensor& {anonymous}::ValidateFillInputArgs(OrtValue*, const onnxruntime::TensorShape&, const OrtMemoryInfo*)/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shapeNot able to find appropriate IDataTransfer to copy sparse datastd::unique_ptr {anonymous}::GetDataTransfer(const OrtDevice&, const OrtDevice&)tried creating tensor with negative value in shapenot enough space: expected , got vector::reserveget() != pointer()typename 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&]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/unique_ptr.hCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copybasic_string::appendopaque(ml_type != nullptrnon_tensor_base != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)Opaque type is not a non_tensor type!!!OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)ORT_LOAD_CONFIG_FROM_MODELonnxruntime_profile_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtCustomOp*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtCustomOp*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtCustomOpDomain*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtCustomOpDomain*&]Map is missing type entry for its valuestatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = std::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Sequence is missing type entry for its elementstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with T = std::map; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]basic_string::replace%s: __pos (which is %zu) > this->size() (which is %zu)UseCooIndicesOrtStatus* OrtApis::UseCooIndices(OrtValue*, int64_t*, size_t)UseCsrIndicesOrtStatus* OrtApis::UseCsrIndices(OrtValue*, int64_t*, size_t, int64_t*, size_t)UseBlockSparseIndicesOrtStatus* OrtApis::UseBlockSparseIndices(OrtValue*, const int64_t*, size_t, int32_t*)FillSparseTensorCooOrtStatus* OrtApis::FillSparseTensorCoo(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t)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&]FillSparseTensorCsrOrtStatus* OrtApis::FillSparseTensorCsr(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int64_t*, size_t)tried Filling sparse tensor with negative value in block sparse indices shapeOrtStatus* OrtApis::FillSparseTensorBlockSparse(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int32_t*)FillSparseTensorBlockSparseTrying to get a SparseTensor, but got: const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]/onnxruntime_src/include/onnxruntime/core/framework/ort_value.hIsSparseTensor()Trying to get a Tensor, but got: T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]this API does not support stringslocation dimensions do not match shape sizestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]cannot create std::vector larger than max_size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]invalid location rangeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]Trying to get a TensorSeq, but got: const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]IsTensorSequence()the ort_value must contain a constructed tensorbasic_string::assigninput name cannot be emptyoutput name cannot be emptyCPUExecutionProviderstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]input array is too short!=T Tensor type mismatch. T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]/onnxruntime_src/include/onnxruntime/core/framework/tensor.hutils::IsPrimitiveDataType(dtype_)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string]OrtValue should contain a Tensor or a Sparse TensorSparse Tensor does not contain sparse dataThis API supports Tensors or SparseTensorsshape is invalidbuffer size is too small for string elementelement index is out of boundsoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.index is out of boundsinput array doesn't equal tensor sizeconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]len >= 0 && static_cast(len) < std::numeric_limits::max()const T* onnxruntime::Tensor::Data() const [with T = float]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]const T* onnxruntime::Tensor::Data() const [with T = double]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]const T* onnxruntime::Tensor::Data() const [with T = long int]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]const T& OrtValue::Get() const [with T = std::vector, float> >]onnxruntime::DataTypeImpl::GetType() == type_const T& OrtValue::Get() const [with T = std::vector >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with T = std::map, float>; 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.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.Tensor must always contain primitive types. Found: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]Value type is not supported yet: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]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]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.Expecting all elements to be tensors. Got: OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Tensor&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Tensor sequence must contain only primitive typesvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/TensorSeq.hconst T& OrtValue::Get() const [with T = std::map, float>]const T& OrtValue::Get() const [with T = std::map]Input is not of one of the supported map types.value_type != nullptrvector::_M_default_appendin[idx]->IsTensor()elem_type_ != nullptrUnsupported input typeconst T& OrtValue::Get() const [with T = std::map >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long int&]Invalid index requested for map type.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = OrtValue; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = OrtValue&]const T& OrtValue::Get() const [with T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = std::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, std::basic_string >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, long int>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = float&]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; std::vector<_Tp, _Alloc>::reference = double&]const T& OrtValue::Get() const [with T = std::map]const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) constvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)i < tensors_.size()St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagPFvPvESt14default_deleteIN11onnxruntime18IExecutionProviderEESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionE*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_*ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_St19_Sp_counted_deleterIPN11onnxruntime18IExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPvPFvS0_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE?onnxruntime_profCPUExecutionProv/\basic_string::substrstatic void OrtEnv::Release(OrtEnv*)/onnxruntime_src/onnxruntime/core/session/ort_env.ccenv_ptr == p_instance_N11onnxruntime7logging5ISinkE14LoggingWrapper_Map_base::atstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]TensorRT execution provider is not enabled in this build.__pos <= size()std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/basic_string.hbasic_string::insert is not present.Required output at index output_ptronnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&)/onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.hT* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]Please fetch output tensor with specified shape.T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]p_ml_valueAttempt to use DefaultLogger but none has been registered.static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger()/onnxruntime_src/include/onnxruntime/core/common/logging/logging.h/onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.ccEnsureProvider_SetHostGetGetProviderOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA execution provider is either not enabled or not available.const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor]Missing Input: Required input at index input_ptrconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::SparseTensor]Tensor type mismatch.type == dtype_const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)Tensor size () != new size (void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)shape_.Size() == new_shape.Size()TensorSeq: tensor to be added has a different data type.void onnxruntime::TensorSeq::Add(onnxruntime::Tensor&&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Tensor&]IsSameDataType(tensor)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = bool]libonnxruntime_providers_cuda.solibonnxruntime_providers_dnnl.solibonnxruntime_providers_openvino.solibonnxruntime_providers_tensorrt.sotypename 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&]N11onnxruntime23NodeAttributes_IteratorEN11onnxruntime35TensorShapeProto_Dimension_IteratorEN11onnxruntime18Node__NodeIteratorEN11onnxruntime18Node__EdgeIteratorEN11onnxruntime12ProviderHostEN11onnxruntime40TensorShapeProto_Dimension_Iterator_ImplEN11onnxruntime28NodeAttributes_Iterator_ImplEN11onnxruntime23Node__NodeIterator_ImplEN11onnxruntime23Node__EdgeIterator_ImplEN11onnxruntime16ProviderHostImplESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899libonnxruntime_providers_shared. execution provider is not enabled in this build. std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::shared_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]/onnxruntime_src/onnxruntime/core/session/IOBinding.ccBindInputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtDevice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]execution_mode is not validgraph_optimization_level is not validstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::FreeDimensionOverride; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::FreeDimensionOverride&]Env is nullOrtMemoryInfo 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.Provided OrtMemoryInfo is nullNo requested allocator available12OrtAllocatorN11onnxruntime10IAllocatorEN11onnxruntime16OrtAllocatorImplEN11onnxruntime34OrtAllocatorImplWrappingIAllocatorEN11onnxruntime34IAllocatorImplWrappingOrtAllocatorEResult buffer is not large enough is not implementedComputeAsyncUnsupported version '' in custom op 'onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&)/onnxruntime_src/onnxruntime/core/session/custom_ops.cctype_id_counter == 1CreateCustomRegistryThere 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.onnxruntime::common::Status onnxruntime::CreateCustomRegistry(const std::vector&, std::shared_ptr&)N11onnxruntime8OpKernelEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_E*ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899custom op registN3Ort9ExceptionE22OrtDefaultCpuAllocatorCpuvector::_M_range_insertSchema error: No allocator for this device has been registered for sharing.An allocator for this device has already been registered for sharing.Only CPU devices are supported for now.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.[TypeInferenceError] Input type was nullInput unknownElement type of input expected to have tensor or sparse tensor type. Got: Target=[ShapeInferenceError] Mismatch between source and target type. Source=Unsupported Source/Target type= expected to have sequence type expected to have sequence type. Got: expected to have type but instead is null (domain: ) from file , but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange tensor(uint8)tensor(uint16)tensor(int8)tensor(int16)tensor(bfloat16)tensor(float16)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)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))seq(tensor(bfloat16))MemcpyFromHostMemcpyToHostException caught: /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.FvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEE*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver1EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver2EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver3EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver4EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver5EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver6EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver7EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver8EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver9EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver10EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver11EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver12EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver13EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver14EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver15EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver1EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver2EEEviEUlONS_8OpSchemaEE_Only CPU allocators can be shared between multiple sessions for com.microsoft.excom.microsoft.nccom.microsoft.mltypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::ModelProto; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::ModelProto&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::shared_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]The provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instance/onnxruntime_src/onnxruntime/core/session/inference_session.ccGetModelMetadataModel was not loadedModel was not loaded.GetModelInputsGetOverridableInitializersGetModelOutputsEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.offoperator()Flush-to-zero and denormal-as-zero are Unexpected input data type. Actual: ()) , expected: (ShrinkMemoryArenasUnable to shrink arena: OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:Device:[DeviceType: MemoryType: DeviceId: error message: Output vector pointer is NULLp_fetches->size(): Invalid Output Name:At least one output should be requested.Output vector incorrectly sized: output_names.size(): NewIOBindingSession was not initializedSession not initialized.typename 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&]Invalid rank for input: Expected: for the following indices Please fix either the inputs or the model.Got invalid dimensions for input: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Received nullptr for custom registryAddCustomOpDomainsLoadOrtModelBytesLoad model from failed. Only bytes were able to be read.session.use_ort_model_bytes_directly[json.exception.%i&iH&ip&i&i%i%i&i&icpugpuUnsupported 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: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::shared_ptr&]Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr&)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)FeedsFetchesInfo/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector >&, const std::vector >&, const onnxruntime::OrtValueNameIdxMap&)Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.elements, but feeds has elements.Invalid Feed Input Name:Input with name: ValidateInputssparse_tensorseqRunRunning with tag: model_runmemory.enable_memory_arena_shrinkagestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtDevice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IExecutionProvider*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IExecutionProvider*&]Encountered unknown exception in Run()AssignNodesToEpsFromHashesImpltypename 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&]Validating no unexpected access using an invalid node_index. Got:onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const/onnxruntime_src/include/onnxruntime/core/graph/graph.hFailed to find kernel def hash ( Max:node_index < nodes_.size()) in kernel registries for ) node with name ''.%Y-%m-%d_%H-%M-%S.jsonProvider has already been registered./onnxruntime_src/onnxruntime/core/framework/execution_providers.hReceived nullptr for exec providerExecution 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.Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.RegisterExecutionProviderDmlExecutionProviderCUDAExecutionProviderTensorrtExecutionProviderCould not find an implementation for VerifyEachNodeIsAssignedToAnEpAll nodes have been placed on [Node placements]. Provider: [TransformGraphMemcpyTransformerLoad_M_get() != nullptrException during loading: Unknown exception in Load()This session already contains a loaded model.std::__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; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::Model]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/shared_ptr_base.hEncountered unknown exception in Load()model_loading_uriModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>&] are '0' and '1'. FinalizeSessionOptionsis_model_proto_parsedstatus.IsOK()session.set_denormal_as_zeroConstructorCommonsession--intra-op-inter-opThe only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT configModelProto needs to be parsed to check for ORT config within itonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&)Could not finalize session options while constructing the inference session. Error Message: void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&)graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinningsession.inter_op.allow_spinningFailed 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()CastFloat16TransformerGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&)Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, std::istream&)onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int)Error during EndProfiling(): ~InferenceSessionUnknown error during EndProfiling()Failed to load model because protobuf parsing failed.LoadOrtModelORTMThis session has already been initialized.ORT model verification failed.Serialized version info is null. Invalid ORT format model.The ORT format model version [] is not supported this build Missing Model. 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&]SessionState is null. Invalid ORT format model.session.load_model_formatORTmodel_loading_arraySaveToOrtFormatInitializeInitializing session.session.use_env_allocatorsCompute_Create_State_Release_State_session.save_model_formatPartitionOrtFormatModelAssignNodesToEpsFromHashesNchwcTransformersession_initializationSession has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.onnxruntime::common::Status onnxruntime::InferenceSession::Initialize()loading_ort_format && serialized_session_state != nullptrUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED 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 successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()St14default_deleteIN11onnxruntime5ModelEESt14default_deleteIN11onnxruntime20CPUExecutionProviderEEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN8nlohmann6detail9exceptionEZN11onnxruntime20CPUExecutionProviderC4ERKNS_24CPUExecutionProviderInfoEEUliE_*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_*ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_St19_Sp_counted_deleterIPN11onnxruntime20CPUExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE__/onnxruntime_src/onnxruntime/core/session/infere@@Bvector::_M_insert_auxcannot get valuenumberbooleanbinarydiscardedobjectmap::atcannot use at() with key '' not foundbjbjhcjGcj\cjRbjRbjRbjbjbj at line , column parse_errorparse error'[', '{', or a literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of inputtrue literalsyntax error while parsing - ; last read: 'unexpected ; expected jjjjhjjjjXjHj8j(jjjjjxjjjjĎjj::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>*&]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>]iterator does not fit current valuestd::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator) [with _Key = std::basic_string; _Val = std::pair, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st, nlohmann::basic_json<> > >; _Compare = std::less; _Alloc = std::allocator, nlohmann::basic_json<> > >; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree, std::pair, nlohmann::basic_json<> >, std::_Select1st, nlohmann::basic_json<> > >, std::less, std::allocator, nlohmann::basic_json<> > > >::iterator]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_tree.hiterator out of range__position != end()cannot use erase() with 1j%jjjjjjjjjjTjjjjjj4jjvoid 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 = char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = char&]invalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator]invalid string: ill-formed UTF-8 byteinvalid 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 \u001Fjj>jhjSjOj:j%jjjjjjjjyjdj)jFj1jjjjjjjjj}j[jxjcjjxjxjjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjxjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjpj(j(j(jjjjjjjjyjjjjgjjjjjjjjjjjjjjUjjinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literalGjyj=jyjyjyjyjyjyjyjyjjyjyjyjyjyjyjyjjyjyjyjyjyjjyjyjyjyjyjyjjyjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj~jjj~j~j~j~j~j~j~j~j~j~j5jobject keyobject separatornumber overflow parsing 'j jj::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Unsupported levelstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::unordered_set >&)/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc_RuleBasedTransformersession.disable_quant_qdqMatMulAddFusionReshapeFusionROCMExecutionProviderACLExecutionProviderArmNNExecutionProviderQDQS8ToU8TransformerQDQPropagationTransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionConvActivationFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionBiasDropoutFusionMatmulTransposeFusionBiasGeluFusionBiasSoftmaxFusionSkipLayerNormFusionFastGeluFusionMatMulScaleFusionGeluApproximationNhwcTransformerUnsupported level optimization.enable_gelu_approximationCommonSubexpressionEliminationstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const std::unordered_set >&)N11onnxruntime11RewriteRuleEEliminateIdentitUnsqueezeEliminaEliminateDropoutExpandEliminatioGemmTransposeFusReluQuantRewriteYieldOpstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime17EliminateIdentityERecurseApplyImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&]/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.ccInsertedCast_InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constdtype_attribute->second.has_i()N11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerERemoveDuplicateCcast node to cast from float16 to float32 on cpu?l?l?l?l?l?l@l@l?l?l?l @l?l?l?l?l?lsize is differentsize_ == sizeunsupported data type: onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&)/onnxruntime_src/onnxruntime/core/optimizer/initializer.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = short unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = short unsigned int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = signed char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = signed char&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned char&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&]ReadExternalRawData() failed: Pil9dlfl?elPilPilelIflPilPilcldlPilPilPilPilclai.onnxDivSqrtPowfused LayerNorm subgraphs epsilon/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.ccCast_Scalecast scale of layer normSimplifiedLayerNormalizationlayer_norm_outcast output of layer normN11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionE'7    fused Matmul and Add /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&]N11onnxruntime15MatMulAddFusionE /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.ccN11onnxruntime26MatMulIntegerToFloatFusionEMatMulIntegerToFstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::{anonymous}::ScaleMergeInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::ScaleMergeInfo&]Constant initializer NodeArg shape should not be null. NodeArg: std::optional onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.ccvoid onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int]Unsupported data type: GetScalarConstantInitializerdiv_inputs.size() == 2mul_inputs.size() == 2std::optional > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const std::unordered_set >&)_FusedMatMulAndScalecom.microsoftFusedMatMulstd::vector onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set >&)input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2std::vector onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set >&)output_node.OutputDefs().size() == 1N11onnxruntime17MatMulScaleFusionEtensor(bfloat16)CUDAExecutionProROCMExecutionProFused MatMul andcannot 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; std::deque<_Tp, _Alloc>::reference = long unsigned int&]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_deque.hbool onnxruntime::GetTransposePerms(const onnxruntime::Node&, std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_transpose_fusion.cctranspose_node.InputDefs().size() == 1!node_consumers.empty()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node*&]size_t onnxruntime::UpdateConsumerCount(onnxruntime::Graph&, onnxruntime::NodeArg*, std::unordered_map&)_transformedcast != nullptronnxruntime::Node* onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph&, onnxruntime::Node*, std::unordered_map&, std::deque&)N11onnxruntime21MatmulTransposeFusionECreated a new Cast node to interchange Cast and Created a new Transpose node to interchange Cast and Transpose nfused MatMul andMatMul_With_TranmmmmmmmmmmmЅmmmmmmcom.microsoft.nchwcReorderOutputchannels_lastkernel_shapeauto_padNOTSETSAME_UPPERSAME_LOWERVALIDreorderReorderInputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NchwcTransformerImpl::NchwcArgument*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NchwcTransformerImpl::NchwcArgument*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]activationnearestasymmetricalign_cornersnearest_modescalescoordinate_transformation_modeReshapereshapestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]spatialbn_scalebn_B_bn_nchwcmnLnmnmnmnmnnnmnmnNnnmnmnmnmnWnnnnnnnnnnnnTnnnnnnt nnt nt nt nt nnnt nt nVnnt nt nt nt nnFusedConvBatchNormalizationGlobalMaxPool/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.ccN11onnxruntime16NchwcTransformerE     _nhwcDequantizeLinearnQnDn7nnn*nnnnnnnnnnnQLinearConvQLinearAddQLinearMulQLinearLeakyReluQLinearSigmoidQLinearGlobalAveragePoolQLinearAveragePoolQLinearConcatPad/onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.ccN11onnxruntime15NhwcTransformerEnhwc_permutated_  (nXn(n(n(n(nn8n(n(nnn;QϠnŠnn;Q;Qnn;Q;Qn֠n;Q;Q;Q;Qnh;Qnn۠nh;Qh;QΠnnh;Qh;Qnnh;Qh;Qh;Qh;Qn:;Q!nnn:;Q:;Qnn:;Q:;Qn+n:;Q:;Q:;Q:;Qn nZnMn@n n n3n&n n nnn n n n nn:Q|nonbn:Q:QUnHn:Q:Q>nn:Q:Q:Q:Q>nN11onnxruntime15NoopEliminationEWhereNotstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long unsigned int&]N11onnxruntime14NotWhereFusionEup_node should have only one Edge that points to down_node and its output is not graph outputvoid onnxruntime::SwapAdjacentNodes(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::Node&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccoptimizer_utils::CheckOutputEdges(graph, up_node, 1)up_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0).edge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index()N11onnxruntime25QDQPropagationTransformerE SwapAdjacentNodennnnnnnnnnnnnnnnnqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quant/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccN11onnxruntime20QDQS8ToU8TransformerEnnnnnnnnnnnnnnnnnN11onnxruntime15ReluQuantFusionEN11onnxruntime28QDQSelectorActionTransformerEQDQSelectorActiostd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Node* const&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Node* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]A target node must be set.target_node != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Node*&]std::unique_ptr onnxruntime::NodesToOptimizeBuilder::Build()/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.hN11onnxruntime12NodeSelectorEN11onnxruntime3QDQ12BaseSelectorEN11onnxruntime3QDQ20DropDQDNodesSelectorEN11onnxruntime3QDQ13UnarySelectorEN11onnxruntime3QDQ14BinarySelectorEN11onnxruntime3QDQ16VariadicSelectorEN11onnxruntime3QDQ12ConvSelectorEN11onnxruntime3QDQ14MatMulSelectorEDequantizeLinearClipstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = short unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = short unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = signed char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = signed char&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned char&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]data type is not supportedToProto ?o:oM;o:o ?o ?o:o9o ?o ?o69o;o ?o ?o ?o ?o69oonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector&)Do@o@oAAoDoDoAoAoDoDoAoBoDoDoDoDo@oUnexpected data type for Clip 'min' input of virtual 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.ccFuseReluClip__min_zero_constantBQYOoDOo/OoBQBQOo OoBQBQNonOoBQBQBQBQNoOoOoOoOoOoOoOoOoOoOoOoOoOoOoOoOoOoJBQJPo5Po PoJBQJBQ PoOoJBQJBQOo_PoJBQJBQJBQJBQOoN11onnxruntime12FuseReluClipE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node::EdgeEnd*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node::EdgeEnd*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const int&]Cannot replace concat node with initializer:Fused reshape node: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccTotal fused reshape node count: N11onnxruntime13ReshapeFusionE e/optimizer/reshApplyRulesOnNode/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::RewriteRule; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::RewriteRule&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&]N11onnxruntime25RuleBasedGraphTransformerEonnxruntime::Node* onnxruntime::NodesToOptimize::GetNode(int, bool) conststatic_cast(index) < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/actions.ccN11onnxruntime6ActionEN11onnxruntime11RemoveNodesEN11onnxruntime15MergeIntoTargetEN11onnxruntime14ReplaceWithNewE/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.ccMoveInputOutputImplMoveInputOutputIndex out of ranMatched TODO: Save the selected nodes into the Graph.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodesToOptimize; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodesToOptimize&]/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SelectorAndAction; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SelectorAndAction&]Multiple entries for operator is not supported. OpType=onnxruntime::SelectorActionTransformer::SelectorActionTransformer(const string&, onnxruntime::SelectorsAndActions&&, bool)insertedExisting registration with name void onnxruntime::SelectorsAndActions::RegisterSelectorAndAction(const string&, const OpVersionsMap&, std::unique_ptr, std::unique_ptr)selectors_and_actions_map_.find(name) == selectors_and_actions_map_.cend()N11onnxruntime25SelectorActionTransformerEe/optimizer/selectors_actions/selector_action_tr/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccN11onnxruntime19SkipLayerNormFusionE̼+fused SkipLayerNSkipLayerNormaliLpMp4Mp'MpLpLpMpLpLpLpLpMpLpLpLpLpLpDLpLpLpLpDLpDLpLpgLpDLpDLpSLptLpDLpDLpDLpDLpSLpN11onnxruntime14EliminateSliceE onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()ForEachWithIndexVitisAIExecutionProvider' doesn't support memcpy Execution type 'void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)N11onnxruntime17MemcpyTransformerE*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_Copy from/to hosUnsqueezeElimination cannot remove node UnsqueezeElimination_N11onnxruntime20UnsqueezeEliminationEe/optimizer/unsqueeze_eliminatio^Qppp^Q^Qpp^Q^Qpp^Q^Q^Q^Qp^QhpSp>p^Q^Q)pp^Q^Q pzp^Q^Q^Q^Q ppppppppppppppppwppppppppppwp7^Qppp7^Q7^Qpp7^Q7^Qpp7^Q7^Q7^Q7^Qp]Qppp]Q]Qp)p]Q]QFpp]Q]Q]Q]QFppppppppppppppppppp&pp ppppppppppppppRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomial'7w+2Start ValidateGemmInitializerPass ValidateGemmInitializer/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.hGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedValidateUnidirMaskunidir 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 floatStart CheckNodesInPathKk_reshape const not matchedPass CheckNodesInPathKStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.permPass CheckNodesInPathQq_transpose perm attribute not matchedStart MatchInputMaskSubgraphFailed to find Softmax nodeFailed to find path for maskUnsqueezePass MatchInputMaskSubgraphOutput edge count not expected for SoftmaxOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedStart MatchInputMaskSubgraphDistilBertFailed to find reshape shape path 1Failed to find reshape shape path 2Pass MatchInputMaskSubgraphDistilBertExpandFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Gathergather indices not matched.CheckDistilBertReshapeShapeStart CheckNodesInPathVPass CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedreshape initializer value is not expectedstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]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() > 0Slice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:CheckSliceParametersExpected value:Start MatchUnidirMaskSubgraphSqueezePass 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_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional 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 slice2Start MatchGemmSubgraphFaild to match gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedPass MatchGemmSubgraphInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathCheckSliceParameters 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 expectedStart MatchPastSubgraphFailed to match v_concatpast_v_gather indices != 1past_k_gather indices != 0Pass MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vpast_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 and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphStart FuseGptAttentionFaild to find path v to SplitFailed to find path for qFailed to find path for kFused Attention subgraphs num_headsunidirectionalFaild to find path to qkv_matmulCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLq and v are not from same Split nodeCheckNodesInPathQ returns falsek and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT.qkv_weightsqkv_biasX r3r&rrX rX rrrX rX r"rrX rX rX rX r"rD rrrrD rD rmr`rD rD rrzrD rD rD rD rr0 rrrr0 r0 rrr0 r0 rrr0 r0 r0 r0 rr4rrrr4r4rrr4r4rprtr4r4r4r4rprrrrrrrrrrrr~rrrrrrrrrrrrr-rrrrGrrrrrrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&]/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccq root should be layer normalizationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]q_matmul and q_add shape not matchedk_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.Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32Failed to convert mask to int32FuseSubGraphQKImplk root is not layer normConvertMaskToInt32FuseSubGraphFaild to find path vFuseSubGraphQKFused an attention node.FuseSubGraphQKDistilBertOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphshape of layer norm bias tensor not expectedTotal fused Attention node count: N11onnxruntime15AttentionFusionE@  Cast mask from iConvertMaskToIntBiasDropoutfused Add and Dropout/onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.ccN11onnxruntime17BiasDropoutFusionE FastGelu/onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccN11onnxruntime14BiasGeluFusionEfused Add and Ge is not in valid range [-axis fused and into softmax(input + bias)/onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.ccint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)/onnxruntime_src/onnxruntime/core/providers/common.haxis >= -tensor_rank && axis <= tensor_rank - 1N11onnxruntime17BiasSoftmaxFusionEN11onnxruntime15CastEliminationEwrdrfr$rwrwrrtrr^rrrrrrErrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Not eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.N11onnxruntime30CommonSubexpressionEliminationEe/optimizer/common_subexpressionstartcan't constant fold Unsupported output type of . Can't constant fold /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccCould not find OrtValue with name 'Could not find a CPU kernel and hence virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constfetches.size() == node->OutputDefs().size()N11onnxruntime15ConstantFoldingE*ZNK11onnxruntime15ConstantFolding9ApplyImplERNS_5GraphERbiRKNS_7logging6LoggerEEUlRKSsE_e/optimizer/consUnexpected data type for Clip input of onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccQaHsLHs7HsQQ"HsHsQQGsGsQQQQGsGsHssHsfHsGsGsYHs?HsGsGs2HsLHsGsGsGsGs2HsminHardSigmoidfused Conv with activation N11onnxruntime20ConvActivationFusionEL>? activation_paramzsstszsszsszsszsstsuszsszss@vsrszsszsszsszssssvsvsvsvsvsvsvsvsvsvsvsvsvsvsvsvsvsvswsvsvsvsvsvsvsvsvsvsvsvsvsvsvsvsvs-ws!wswsvsvs wsvsvsvsvsvsvsvsvsvsvswsYwsMwsAwswsws5ws wswswsws,wswswswswsws)/onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of Invalid free dimension override.The model has input '' with a fixed dimension size N11onnxruntime32FreeDimensionOverrideTransformerEFreeDimensionOverrideTransformerInvalid free dimension override.e/optimizer/free_dim_override_trBiasGelu/onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: N11onnxruntime17GeluApproximationEGelu approximatie/optimizer/gelu_approximation.cErffused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.ccN11onnxruntime10GeluFusionE??SoftsignScaledTanhParametricSoftplusThresholdedRelufused Gemm activation_/onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.ccN11onnxruntime20GemmActivationFusionE  N11onnxruntime19GemmTransposeFusionE Fused Gemm with External data type must not be UNDEFINED or STRING.model_path must not be empty. Ensure that a path is provided when the model is created or loaded./onnxruntime_src/onnxruntime/core/optimizer/initializer.ccTensorProto external data size mismatch. ReadExternalRawDataComputed size: , external_data.length: e/optimizer/initReadExternalRawDtypename 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&]Tried to allocate without valid type information, ort_value index=Failed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.ccallocator_ptr_N11onnxruntime23OptimizerExecutionFrameE*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSK_EEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISs8OrtValueSt4hashISsESt8equal_toISsESaISt4pairIKSsSB_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSH_EEEUlRKNS_7NodeArgEmE_QLinearb33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8b33f88f7-c464-43e3-8692-97ac832bb14aN11onnxruntime3QDQ17QDQReplaceWithNewEN11onnxruntime3QDQ18ReplaceWithQLinearEN11onnxruntime3QDQ23UnaryReplaceWithQLinearEN11onnxruntime3QDQ24BinaryReplaceWithQLinearEN11onnxruntime3QDQ26VariadicReplaceWithQLinearEN11onnxruntime3QDQ22ConvReplaceWithQLinearEN11onnxruntime3QDQ24MatMulReplaceWithQLinearEgenerated at runMIGraphXExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderNupharExecutionProviderNnapiExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderRegisterOnnxOperatorKernels/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxMLOperatorKernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constN11onnxruntime20CPUExecutionProviderEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::shared_ptr&]N11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime15ProviderHostCPUEN11onnxruntime19ProviderHostCPUImplET1T2void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList]/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.hONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: utils::HasDataType(t_proto)SetValueFromTensorProtov|"v!v vvv vvv$v4vLvdv|vMust have a valid input shape./onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: Must have a single dimensiont_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList]Must have a single dimension of 1FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEN11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJlNS_9MLFloat16EfdasihtjmbEEEEE*N11onnxruntime12_GLOBAL__N_115ConstantOfShapeEPFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEe/providers/cpu/generator/constant_of_shape_baseonnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)info.GetAttr("high", &high_).IsOK()info.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::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)Output type not supported in this build: std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/random.hstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]_M_a <= _M_binput count mismatchCould not infer data type from input tensor with data type _M_stddev > _RealType(0)std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]Empty dimensions for input tensor/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccbatch_size is < 1num_classes is < 1num_samples is < 1MultinomialComputeInvalid data type of N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialEAC???0???Integer overflow"~+@"~+@W6}+W6}++eG?+eG?????@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?@@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 should be scalar like tensor, yet got shape:N11onnxruntime5RangeEdelta in Range operator can not onnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hmin_ <= max_void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long 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*) const [with T = long int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = float]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = unsigned char]void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1N11onnxruntime13clip_internal10Clip_6BaseIfEEN11onnxruntime4ClipEN11onnxruntime6Clip_6IfEE  CumSumonnxruntime::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()void onnxruntime::SliceIteratorBase::Init(const std::vector&, gsl::span, gsl::span)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()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`starts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = float]dims.size() == extents_.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 = float]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = double]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector&, gsl::span, gsl::span) [with T = long int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = long int]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEExxxxxxxxxxxxxxxxTxxxxxxxx4xxxxxxxxxx4xxxx4xxxxxxxxxxԶxxxxxxxxxxԳxtxxTxvxTx4xTxxxܵxxxxxxxxxxxxxxxxxxxxxxxxxx4xxxxxxxxxxxxxxxxԳxxxxԳxtxxxxxxxԳxtxxԳxxxTxxxxDetonnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedX != nullptrN11onnxruntime3DetIfEEEinsumbasic_string::basic_stringDeviceCompute is not supported yettypename 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&]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Tensor* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Einsum op: An implementation for the input type Missing 'equation' attributeonnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.hinfo.GetAttr("equation", &equation_).IsOK()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Tensor*&]FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_RKSt6vectorIlSaIlEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvEFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEN11onnxruntime6EinsumEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEPFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvEPFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_RKSt6vectorIlSaIlEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEPFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEEinsum op: There must be atleastThere was a problem acquiring temporary memory allocator in EinsEinsum op: The candidate output does not match the actual output's shapeonnxruntime::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.ccoutput.SizeInBytes() == input.SizeInBytes()The rank of the input must match permutation size for Transposebool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector&)input_rank == permutation.size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Length of permutation must match the rank of the input to be permutatedstd::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector&, const std::vector&, onnxruntime::AllocatorPtr, void*, const Transpose&)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&]Einsum op: Transpose failed: The innermost dims should have the same dim value to parse the diagonal elementsstd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]Einsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Data types of the inputs must match for MatMulstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector&, const onnxruntime::Tensor&, const std::vector&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]input_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: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector&, const onnxruntime::Tensor&, const std::vector&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector&, const onnxruntime::Tensor&, const std::vector&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector&, const onnxruntime::Tensor&, const std::vector&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector&, const onnxruntime::Tensor&, const std::vector&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]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 == rankvector::_M_fill_insertFound a '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Another operand has a dim value of Einsum 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 std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]. The shape is: in the same dimensionstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::TensorShape&]Inputs have ellipses in them but the provided output subscript does not contain Found a '.' not part of an ellipsis in the output subscript provFound '.' not part of an ellipsis in the output subscript providThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased Output subscript contains repeat contains letters not seen in thNumber of subscripts in the input equation does not match number of input tensorEinsum subscripts string contains too many subscript labels when compared to the rank of the inpRank of the input must match number of subscript labels corresponding to the inpEinsum op: The candidate output cannot be reshaped into the op's outputvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const std::vector&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cccandidate_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: Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rankleft_dim == right_dimThe override dims are not compatible with given tensor's shape. std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector&, bool) [with T = float]left.Shape().Size() == left_shape_override.Size()right.Shape().Size() == right_shape_override.Size()Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsEinsum op: Input shapes do not alignstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::TensorShape&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const std::vector&) [with T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector&, bool) [with T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const std::vector&) [with T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector&, bool) [with T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const std::vector&) [with T = long int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector&, bool) [with T = long int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long int&]fmod must have value either 0 or 1onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc(fmod == 0) || (fmod == 1)Invalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8::Compute >::FloorSinReciprocalNegAbsBitShiftAsinhAsinCosCeilAcoshAtanAtanhSinhTanCoshPReluAcosLessAndGreaterOrEqualLessOrEqualGreaterXorOrModBroadcastLooper requires two tensors as input.void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper]/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::BitShift::Compute::cur1 == end1cur_out == end_outonnxruntime::BitShift::Compute::onnxruntime::BitShift::Compute::onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]'. Valid values are 'LEFT' or 'RIGHT'.RIGHTInvalid direction value of 'LEFTonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::Broadcaster(const std::vector&, const std::vector&)) for tensor of length:Invalid start/ending offset [Broadcast Output range [onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len) are not at boundary of span with size:start_offset % span_size == 0 && real_end % span_size == 0InputBroadcaster can only start at span boundary!void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&)fmod attribute must be true for floating point typesvoid onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if::value, void>::type = void]void 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*) constfmodUnsupported Y type: Unsupported X type: onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]/onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hinput_size < std::numeric_limits::max()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::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::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::Abs]Tensor with shape information must be 1 dimensional.void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&)shape_data_tensor.Shape().GetDims().size() == 1ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&)!helper.HaveTwoTensorInputs()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::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]Must have 1 or more inputsinputCount >= 1data_n.Shape() == shapeonnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]All inputs must have the same shapeonnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]input_count >= 1data_1.Shape() == shapeonnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]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::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::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::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = double]ElementWiseKernelonnxruntime::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::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]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]N11onnxruntime3PowEN11onnxruntime5Min_8EN11onnxruntime5Max_8EN11onnxruntime3NotEN11onnxruntime3AndEN11onnxruntime2OrEN11onnxruntime3XorEN11onnxruntime8functors26ElementWiseRangedTransformIfEEN11onnxruntime8functors3ExpIfEEN11onnxruntime3AddIfEEN11onnxruntime3AddIdEEN11onnxruntime3AddIiEEN11onnxruntime3AddIlEEN11onnxruntime3SubIfEEN11onnxruntime3SubIdEEN11onnxruntime3SubIiEEN11onnxruntime3SubIlEEN11onnxruntime3MulIfEEN11onnxruntime3MulIdEEN11onnxruntime3MulIiEEN11onnxruntime3MulIlEEN11onnxruntime3DivIfEEN11onnxruntime3DivIdEEN11onnxruntime3DivIiEEN11onnxruntime3DivIlEEN11onnxruntime8functors3AbsIfEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEEN11onnxruntime8functors26ElementWiseRangedTransformIdEEN11onnxruntime8functors3AbsIdEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEEN11onnxruntime8functors26ElementWiseRangedTransformIaEEN11onnxruntime8functors3AbsIaEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEEN11onnxruntime8functors26ElementWiseRangedTransformIsEEN11onnxruntime8functors3AbsIsEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEEN11onnxruntime8functors26ElementWiseRangedTransformIiEEN11onnxruntime8functors3AbsIiEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEEN11onnxruntime8functors26ElementWiseRangedTransformIlEEN11onnxruntime8functors3AbsIlEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEEN11onnxruntime8functors26ElementWiseRangedTransformIhEEN11onnxruntime8functors3AbsIhEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEEN11onnxruntime8functors26ElementWiseRangedTransformItEEN11onnxruntime8functors3AbsItEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEEN11onnxruntime8functors26ElementWiseRangedTransformIjEEN11onnxruntime8functors3AbsIjEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEEN11onnxruntime8functors26ElementWiseRangedTransformImEEN11onnxruntime8functors3AbsImEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEEN11onnxruntime8functors3NegIfEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEEN11onnxruntime8functors3NegIdEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEEN11onnxruntime8functors3NegIaEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEEN11onnxruntime8functors3NegIiEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEEN11onnxruntime8functors3NegIlEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEEN11onnxruntime8functors5FloorIfEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEEN11onnxruntime8functors4CeilIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEEN11onnxruntime8functors10ReciprocalIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEEN11onnxruntime8functors10ReciprocalIdEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEEN11onnxruntime8functors4SqrtIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEEN11onnxruntime8functors4SqrtIdEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEEN11onnxruntime8functors3ExpIdEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEEN11onnxruntime8functors3LogIfEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEEN11onnxruntime8functors3LogIdEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEEN11onnxruntime5Sum_6IfEEN11onnxruntime5Sum_6IdEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Sum_8IdEEN11onnxruntime5Max_6IfEEN11onnxruntime5Min_6IfEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIdEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime5EqualIdEEN11onnxruntime11LessOrEqualIfEEN11onnxruntime11LessOrEqualIdEEN11onnxruntime11LessOrEqualIiEEN11onnxruntime11LessOrEqualIlEEN11onnxruntime14GreaterOrEqualIfEEN11onnxruntime14GreaterOrEqualIdEEN11onnxruntime14GreaterOrEqualIiEEN11onnxruntime14GreaterOrEqualIlEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8ISsEEN11onnxruntime3ModE*ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOpA@K8F,F????????????5?5?5?5?!=!=!=!=뽸뽸뽸%=%=%=%=O]O]O]O]>>>>P*P*P*P*L>L>L>L>>>>>^^^^1?1?1?1?@@"?"?"?"?@K@K@K@K@@@@ɿɿɿɿ.4.4.4.4t.t.t.t.FFFF s7 s7 s7 s7nnnn*=*=*=*=pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<****,F,F,F,F @ @C@@       .@-DT!::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::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()c_shape is required if c_data is providedvoid onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = float; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hc_shape != nullptronnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)left.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcastright.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = double; int64_t = long int]N11onnxruntime8GemmBaseEZNK11onnxruntime4GemmIdE17ComputeActivationEPdmPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime4GemmIfEEN11onnxruntime4GemmIdEEZNK11onnxruntime4GemmIfE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_ Hardmax/onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=N11onnxruntime7HardmaxIfEEleft_num_dims and right_num_dims must be >= 1/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsMatMul dimension mismatchM_ == 1 && N_ == 1 was false/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_SGEMM_DATA_PARAMS; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_SGEMM_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime6MatMulIfEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIlEEmath/matmul_helpT3MatMulIntegerPer-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.MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.ccIsScalarOr1ElementVector(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[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_GEMM_U8X8_DATA_PARAMS; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_GEMM_U8X8_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime17MatMulIntegerBaseEN11onnxruntime13MatMulIntegerEQLinearMatMulstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&]QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccIsScalarOr1ElementVector(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>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]27MLAS_QGEMM_OUTPUT_PROCESSOR35MLAS_QGEMM_REQUANT_OUTPUT_PROCESSORN11onnxruntime13QLinearMatMulERoundN11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundIdEE0CSignN11onnxruntime4SignELogSoftmax/onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13N11onnxruntime7SoftmaxIfEEN11onnxruntime7SoftmaxIdEESoftmaxCPU inputs N, D and N * D must be < ;ITopKvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, unsigned int&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()op_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo&, int&)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&)op_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorvalue of k must not be negativeN11onnxruntime4TopKILi9EfEEN11onnxruntime4TopKILi9EdEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi10EdEEN11onnxruntime4TopKILi11EfEEN11onnxruntime4TopKILi11EdEEN11onnxruntime4TopKILi11EiEEN11onnxruntime4TopKILi11ElEE*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ 333333?input count mismatch, expected 1 input - the tensor to be procesatch, expected 2 inputs - the tensor to be processed and a tensor containing k vk tensor should be a 1D tensor oArrayFeatureExtractorai.onnx.mlInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[) >=N11onnxruntime2ml23ArrayFeatureExtractorOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpISsEEBinarizerInput data with index: is NaNN11onnxruntime2ml11BinarizerOpIfEE%fCastMaponnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hinfo.GetAttr("map_form", &attr).IsOK()onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&)info.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_STRINGTO_INT64Invalid CAST_TO value of DENSE Expected DENSE or SPARSEInvalid PACK_MAP value of TO_FLOATconst T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]Negative index values are not permitted. First entry in map has index value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cccur_input == end_input || cur_input->first >= 0const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = std::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]stofonnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = float]stollonnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = long int] Expected std::map or std::mapInvalid input type of value: Unexpected CAST_TO value of N11onnxruntime2ml7CastMapECategoryMapperInput of string must have output of int64Input of int64 must have output of string onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_strings", string_categories).IsOK()info.GetAttrs("cats_int64s", int_categories).IsOK()info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()N11onnxruntime2ml14CategoryMapperEDictVectorizeronnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::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::basic_string; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::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, long int>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]N11onnxruntime2ml16DictVectorizerOpISslEEN11onnxruntime2ml16DictVectorizerOpISsfEEN11onnxruntime2ml16DictVectorizerOpISsdEEN11onnxruntime2ml16DictVectorizerOpIlSsEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIldEEstring_vocabularint64_vocabularyFeatureVectorizerinputdimensions attribute must be providedonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hstatus.IsOK() && !input_dimensions_.empty()Number of inputs (input_tensor_ptr != nullptrInvalid input type:) does not match number of inputdimensions values (virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()N11onnxruntime2ml17FeatureVectorizerEImputerExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected '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()Empty value of imputed values.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Empty input dimensions.virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constInvalid typeN11onnxruntime2ml9ImputerOpEimputed_value_flimputed_value_inreplaced_value_freplaced_value_iLabelEncoderkeys_int64svalues_stringskeys_floatsvalues_int64svalues_floatskeys_stringsInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.hinfo.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()However, the number of key is attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_valuesonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string]classes_stringsdefault_stringonnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::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 = long int; TValue = long int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]N11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml14LabelEncoder_2IfSsEEN11onnxruntime2ml14LabelEncoder_2ISsfEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2IlSsEEN11onnxruntime2ml14LabelEncoder_2ISslEEN11onnxruntime2ml14LabelEncoder_2IllEELinearClassifiervoid onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::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 SOFTMAXSOFTMAX_ZERO!coefficients_.empty()LOGISTICNONEonnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.ccScores output is incorrect size. Expected:void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span, int64_t, int64_t, int64_t, const std::vector&, const std::vector&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) constscores_output_data.length() >= scores_output_sizeInput shape needs to be at least a single dimension.void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = int]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 shape_size == out.length()N11onnxruntime2ml16LinearClassifierEZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_@@3ֳclasslabels_striclasslabels_intsLinearRegressoronnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.ccinfo.GetAttr("targets", &num_targets_).IsOK()info.GetAttrs("coefficients", coefficients_).IsOK()Input shape had more than 2 dimension. Dims=Unsupported data type of N11onnxruntime2ml15LinearRegressorEonnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of N11onnxruntime2ml10NormalizerEOneHotEncoderUnknown Category and zeros = 0.One and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cctmp_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::basic_string]N11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpISsEEEmpty scale in attributes!scale_.empty()Scale size: () != (onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccscale_.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]Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_N11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIiEESVMClassifierLINEARRBFPOLYonnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.hinfo.GetAttrs("kernel_params", kernel_params).IsOK()coefficients_.size() > 0onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.ccinfo.GetAttrs("rho", rho_).IsOK()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() > 0N11onnxruntime2ml9SVMCommonEN11onnxruntime2ml13SVMClassifierE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_4 ף;vectors_per_clasSVMRegressoronnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext*) const [with T = float]num_features == feature_count_Unexpected mode:ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbEUlPfE_N11onnxruntime2ml12SVMRegressorIfEEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::ScoreValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]TreeEnsembleClassifieronnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const string&, 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 string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with ITYPE = float; OTYPE = float; std::string = std::basic_string; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.hnodes_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()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::NODE_MODE; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::NODE_MODE&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]One falsenode is pointing either to itself, either to another tree.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement*&]n_targets_or_classes > 0AVERAGESUMMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQ (truenode).Unable to find node (falsenode). (weights). is already there. in tree Node classlabels_int64sclasslabels_stringsclass_weightsclass_treeidsclass_nodeidsclass_idspost_transformnodes_valuesnodes_truenodeidsnodes_treeidsnodes_nodeidsnodes_modesnodes_hitratesnodes_featureidsnodes_falsenodeidsbase_valuesaggregate_functionnodes_missing_value_tracks_trueonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const string&, 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 string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with ITYPE = double; OTYPE = float; std::string = std::basic_string; int64_t = long int]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const string&, 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 string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with ITYPE = long int; OTYPE = float; std::string = std::basic_string; int64_t = long int]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const string&, 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 string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with ITYPE = int; OTYPE = float; std::string = std::basic_string; int64_t = long int]ࡌȡ ӢOQstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::SparseValue; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::ml::detail::SparseValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int], 䩌<0l0n ܱ,² \"nlL\, |Gӻpstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector, std::allocator > >; _Alloc = std::allocator, std::allocator > > >; std::vector<_Tp, _Alloc>::reference = std::vector, std::allocator > >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector >&, const onnxruntime::ml::detail::TreeNodeElement&) const [with ITYPE = double; OTYPE = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hit->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector >&, const onnxruntime::ml::detail::TreeNodeElement&) const [with ITYPE = int; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector >&, const onnxruntime::ml::detail::TreeNodeElement&) const [with ITYPE = float; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector >&, const onnxruntime::ml::detail::TreeNodeElement&) const [with ITYPE = long int; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int&, const std::vector >&) const [with ITYPE = float; OTYPE = float; int64_t = long int]classes.size() == 2 || classes.size() == 1std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::ml::detail::ScoreValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]predictions.size() == 2void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = float; OTYPE = float]predictions.size() == predictions2.size()computevoid onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float]X dims is empty.void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int&, const std::vector >&) const [with ITYPE = double; OTYPE = float; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = double; OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = long int; OTYPE = float; int64_t = long int]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int&, const std::vector >&) const [with ITYPE = long int; OTYPE = float; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = long int; OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = long int; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = int; OTYPE = float; int64_t = long int]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int&, const std::vector >&) const [with ITYPE = int; OTYPE = float; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = int; OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = int; OTYPE = float]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_N11onnxruntime2ml22TreeEnsembleClassifierIfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml22TreeEnsembleClassifierIiEETreeEnsembleRegressortarget_weightstarget_treeidstarget_nodeidstarget_idsn_targetsvoid onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = float; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = float; OTYPE = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = double; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector >&, const std::vector >&) const [with ITYPE = double; OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int]Unknown aggregation function in TreeEnsemble.void onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_N11onnxruntime2ml21TreeEnsembleRegressorIfEEN11onnxruntime2ml21TreeEnsembleRegressorIdEEZipMapMust provide classlabels_strings or classlabels_int64s but not both.onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector, float> >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map, float>; _Alloc = std::allocator, float> >; std::vector<_Tp, _Alloc>::reference = std::map, float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]T* OrtValue::GetMutable() [with T = std::vector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::map&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Input features_per_batch[] != number of classlabels[N11onnxruntime2ml8ZipMapOpEclasslabels_int6MaxUnpoolNo kernel shape is set.kernel_shape_[dim] > 0onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.hinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()Pad should be smaller than kernel.pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Input dimension cannot be less than 3.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 shape/onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccoutput_shape is smaller than minimum required. output_shape:Unsupported pooling size. inferred output shape:N11onnxruntime9MaxUnpoolETraining mode only supports spatial BNonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.his_spatial_onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double]Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: Invalid number of outputs for BN trainingonnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = float]!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = double]N11onnxruntime9BatchNormIfEEN11onnxruntime9BatchNormIdEEfff?Unknown AutoPadType Stringonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.hauto_pad == AutoPadType::NOTSETX num_dims does not match W num_dims.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long int&]Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group. X: C: kernel channels: group: M: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. kernel_shape: 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.ComputePadAndOutputShapeInferOutputShapeInvalid input shape: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.ccN11onnxruntime4ConvIfEEConvIntegerMust be a scalar or 1D tensor or size 1.virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.ccIsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)N11onnxruntime11ConvIntegerE*out_size >= 0dim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, int64_t, std::vector*, std::vector*) constfilter number not equal to input channel number.Input channels is not divisible by group.group count is <= 0 filter_number: num_input_channels: PrepareForComputeDoConvTranspose/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccN11onnxruntime13ConvTransposeIfEEratio input should have a single value.float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.hratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0ffloat onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]X and mask should have the same shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double]!mask || mask->Shape() == X_shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float]N11onnxruntime7DropoutIffEEN11onnxruntime7DropoutIfdEEN11onnxruntime7DropoutIdfEEN11onnxruntime7DropoutIddEEFlattenonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hinfo.GetAttr("axis", &axis_).IsOK()The rank of input tensor must be >= axisvirtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisN11onnxruntime7FlattenEInstanceNormalizationonnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid 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 Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.ccN11onnxruntime12InstanceNormIfEELpNormalizationonnxruntime::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]N11onnxruntime6LpNormIfEEN11onnxruntime6LpNormIdEELRNonnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr("size", &size).IsOK()info.GetAttr("beta", &beta_).IsOK()size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fonnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4N11onnxruntime3LRNIfEEN11onnxruntime8functors4PowxIfEE t@GlobalLpPoolvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()void onnxruntime::PoolAttributes::InferOutputSize(const std::vector&, std::vector*, std::vector*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hvoid onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constinput_dims.size() >= 2Unsupported AutoPad Type.Invalid input shape. Only N can be zero. Got:std::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector*) constinput_shape.Size() > 0 || input_shape[0] == 0padsstridesceil_modedilationscount_include_padstorage_orderkernel_shape[dim] > 0onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const string&, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()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_src/onnxruntime/core/providers/cpu/nn/pool.cckernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size : N11onnxruntime8PoolBaseEN11onnxruntime10Pool1DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool2DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool3DTaskIfNS_6LpPoolEEEN11onnxruntime9MaxPoolV8EN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime4PoolIfNS_6LpPoolEEEN11onnxruntime13MaxPool1DTaskIfEEN11onnxruntime13MaxPool2DTaskIfEEN11onnxruntime13MaxPool3DTaskIfEEN11onnxruntime13MaxPool1DTaskIdEEN11onnxruntime13MaxPool2DTaskIdEEN11onnxruntime13MaxPool3DTaskIdEEN11onnxruntime13MaxPool1DTaskIaEEN11onnxruntime13MaxPool2DTaskIaEEN11onnxruntime13MaxPool3DTaskIaEEN11onnxruntime13MaxPool1DTaskIhEEN11onnxruntime13MaxPool2DTaskIhEEN11onnxruntime13MaxPool3DTaskIhEE T4virtual onnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.ccprepacked_buffers[0].get() == nullptrQLinearConv : input zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)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)N11onnxruntime11QLinearConvE*ZNK11onnxruntime11QLinearConv7ComputeEPNS_15OpKernelContextEEUllE_@>QLinearConv : filter zero point must be constantlter scale shapeMaxRoiPoolonnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.hinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scale_ > 0onnxruntime::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_sizeN11onnxruntime7RoiPoolIfEEShrinkonnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()N11onnxruntime6ShrinkEStringNormalizer:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::Locale(const string&)::/onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.ccInput contains invalid utf8 charsWCHAR_TSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper >; _Alloc = std::allocator > >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper >&]attribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)attribute case_change_action is not setattribute case_change_action has invalid valueDuplicate stopwords not allowedStopword contains invalid utf8 charsEmpty stopwords not allowed!sw.empty()p.secondwstr != wconv_errorConversion Erroren_US.UTF-8Conversion ErrorN11onnxruntime16StringNormalizerEis_case_sensitivcase_change_actiTfIdfVectorizertypename 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&]Input shape must have either [C] or [B,C] dimensions with B > 0.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] id: p.first->second->id_ == 0Duplicate ngram detected, size: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = const long int*; Map = std::unordered_map > >; size_t = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.ccsize_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::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]mode is requiredTFIDFmode: min_gram_length is requiredimpl_->min_gram_length_ > 0 >= max_skip_count is requiredimpl_->max_skip_count_ >= 0 <= must be of equal size but ngram_indexes size: Got weights of size: !pool_strings.empty()-grams(items % ngram_size == 0)TFonnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: Non-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()Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings must not be empty if specifiednon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole N11onnxruntime15TfIdfVectorizerE*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_GGGGstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr&]NonMaxSuppressioncenter_point_box only support 0 or 1onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h0 == center_point_box_ || 1 == center_point_box_iou_threshold must be in range [0, 1].static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccboxes 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.boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.output != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SelectedIndex; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::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/devtoolset-10/root/usr/include/c++/10/bits/stl_queue.hvirtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionEcenter_point_boxobject_detection/non_max_suppresGetThresholdsFrostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]RoiAlignavgInvalid mode of value sampling_ratio_ >= 0 specified. It should be either avg or maxonnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was 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 matchN11onnxruntime12RoiAlignBaseE*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEEReduceMinReduceProdReduceL2ArgMaxReduceLogSumArgMinReduceSumSquareReduceL1ReduceLogSumExpCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape&, int64_t)/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cckeepdimsvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0must be overloaded.void onnxruntime::ValidateMustBeOverloaded()Reduction on all axes, output size should be 1.void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1Axes input is nullaxes_tensor != nullptrvoid onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor*)An axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1Only works on matrices with two dimensions.void onnxruntime::ValidateFastReduceKRK(const std::vector&, const onnxruntime::Tensor&)fast_shape[0] * fast_shape[2] == output.Shape().Size()fast_shape.size() == 3Output size mismatch.void onnxruntime::ValidateFastReduceRK(const std::vector&, const onnxruntime::Tensor&)fast_shape[1] == output.Shape().Size()fast_shape.size() == 2void onnxruntime::ValidateFastReduceKR(const std::vector&, const onnxruntime::Tensor&)fast_shape[0] == output.Shape().Size()onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = true]/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = false]N11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime16ReduceKernelBaseILb0EEEZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhhE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhhE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhhE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaaE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaaE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaaE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIddE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIddE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIddE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntim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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 > 0'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast(num_directions_) * 2Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.clip_ > 0.fforwardreversebidirectionallayout_ == 0void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [with T = float; typename gsl::span::const_iterator = const float*; 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 = 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 = float*; TSpanBIter = const float*; TSpanCIter = float*]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::rnn::detail::ActivationFuncs::Entry&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccComputeImplGRU operator does not support double yetInvalid data type for GRU operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbEUlPiE_linear_before_reactivation_alphaonnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.hactivation_func_names.size() == static_cast(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccPrePackN11onnxruntime8LSTMBaseEN11onnxruntime13DeepCpuLstmOpEInput X must have 3 dimensions only. Actual: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 B must have shape {}. Actual:Input P must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.ccRNN/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cclayoutdirectiononnxruntime::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()info.GetAttrs("activations", activations_).IsOK()info.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()FffffEN11onnxruntime3RNNIfEEPFffffEaffineleakyreluthresholdedreluscaledtanhhardsigmoidsoftplussoftsignInvalid activation function of void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccInvalid LSTM merge activation function of void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)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)weights.quant_para_Input W must have shape {Input R must have shape {Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector::const_iterator&, const const_iterator&, std::vector::const_iterator&, const const_iterator&, float&, float&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::rnn::detail::ActivationFuncs::Entry&]ZN11onnxruntime24GetQuantizationParameterIhLb0ELb0ELi0EEEvPKflRfRT_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime17ParQuantizeLinearIhEEvPKfPT_mfS3_PNS_11concurrency10ThreadPoolEEUlllE_%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;@`@ A%~3a*7.A\3JCy7 ':Y;Ӡ5֕8;Y;ACp@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]*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIfEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIhEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbEUlPhE_ConcatFromSequenceMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concatbase.hGot nullptr for sequence input.virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccN11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceESplitToSequenceSequenceEmptySequenceConstructSequenceLengthSequenceInsertSequenceEraseSequenceAtSequenceEmpty: Got nullptr for output sequencevirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccY != nullptrUnsupported 'dtype' value: SS}}}} ~~,~<~L~\~l~|~~SSSS|}split_size_sum () != split_dim_size (CreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constSequenceConstruct: Got nullptr for output sequencenum_inputs >= 1virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) constSequenceLength: Got nullptr for output tensorint64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&)virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) constGot nullptr input for index tensor) specified for sequence of size (SequenceAt: Got nullptr for output tensorI != nullptrInvalid sequence index (virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) constData type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceS != nullptrGot nullptr for input tensor.), input tensor data type (virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) constSequenceErase: Got nullptr for output sequenceInvalid data type for split tensor int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector&)Split should be > 0split_scalar > 0onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string]Invalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float]SplitToSequence operator does not support N11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEViolation of the requirment that all input tensors must have the same data type.Attribute to is not set.onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc-INF%.8gstoullstod*N11onnxruntime12_GLOBAL__N_14CastEAAAAOOOOCompressvirtual 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()N11onnxruntime8CompressE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.honnxruntime::StridedCopy >::/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/copy.hlast >= firstonnxruntime::StridedCopy::onnxruntime::StridedCopy::onnxruntime::StridedCopy::onnxruntime::StridedCopy::src and dst must have same shape and not be rank 0.void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const std::vector&, const onnxruntime::TensorShape&, const T*, const std::vector&) [with T = std::basic_string]dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()counter.current_offset == lastinput != nullptrCannot concatenate scalars got: input_rank == reference_rankData type mismatchonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector&, onnxruntime::Prepare&) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.ccRanks of input data are different, cannot concatenate them. expected rank: Non concat axis dimensions must match: Axis has mismatched dimensions of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Prepare::InputInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Prepare::InputInfo&]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const std::vector&, const onnxruntime::TensorShape&, const T*, const std::vector&) [with T = unsigned int]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const std::vector&, const onnxruntime::TensorShape&, const T*, const std::vector&) [with T = long unsigned int]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const std::vector&, const onnxruntime::TensorShape&, const T*, const std::vector&) [with T = short unsigned int]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const std::vector&, const onnxruntime::TensorShape&, const T*, const std::vector&) [with T = unsigned char]src and dst types must matchonnxruntime::common::Status onnxruntime::DispatchStridedCopy(onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, std::ptrdiff_t, const std::vector&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >; std::ptrdiff_t = long int]dst.DataType() == src.DataType()Unsupported input data type of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Prepare::InputInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Prepare::InputInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime6ConcatEZN11onnxruntime11StridedCopyISsEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE_ZN11onnxruntime11StridedCopyISsEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE0_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE0_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE0_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE0_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKSt6vectorIlSaIlEERKNS_11TensorShapeEPKS4_SA_EUlllE0_ tensor/concat.ccPrepareForComputonnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccx_ptr != nullptrN11onnxruntime21DynamicQuantizeLinearIhEEtypename 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]ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_N11onnxruntime6ExpandIfEEN11onnxruntime6ExpandIdEEN11onnxruntime6ExpandIaEEN11onnxruntime6ExpandIsEEN11onnxruntime6ExpandIiEEN11onnxruntime6ExpandIlEEN11onnxruntime6ExpandIhEEN11onnxruntime6ExpandItEEN11onnxruntime6ExpandIjEEN11onnxruntime6ExpandImEEN11onnxruntime6ExpandIbEEN11onnxruntime6ExpandINS_9MLFloat16EEEinvalid expand sEyeLikeN11onnxruntime7EyeLikeEEyeLike : Input tensor dimensionTindMissing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.hindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccN11onnxruntime10GatherBaseEN11onnxruntime6GatherEZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_Gather Tind type not supported iGatherElementsGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h]. Actual value is , GatherElements op: Value in indices must be within bounds [onnxruntime::core_impl, long int>::/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cconnxruntime::core_impl, int>::onnxruntime::core_impl::onnxruntime::core_impl::N11onnxruntime14GatherElementsE*ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_*ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_*ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_*ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_*ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_*ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_GatherElements op: Cannot operate on scalar inpup: Rank of input 'data' needs to be equal to rank of input 'indip: Data type of input 'data' should match the data type of the oGatherNDinvalid index found, index = GatherNDBase PrepareForCompute: Input count mismatchvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE*ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_indices tensor must has rank larlast dimension of indices must not be larger than rank of input indices tensor data type not supIdentityonnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.hUnable to get an allocatoronnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEIsInfFailed to obtain detect_positiveonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeN11onnxruntime5IsInfEIsNaNNull input ptrN11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNINS_9MLFloat16EEE||||||||MeanVarianceNormalizationonnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.hinfo.GetAttr("across_channels", &across_channels_).IsOK()info.GetAttr("normalize_variance", &normalize_variance_).IsOK()Input is expected to have four dimensions corresponding to [N,C,H,W]N11onnxruntime27MeanVarianceNormalization_0IfEEN11onnxruntime27MeanVarianceNormalization_1IfEEnormalize_varianonnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/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 = bool]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEInvalid 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 elementsOneHotDepth is negative./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = Eigen::internal::TensorBlockScratchAllocator::Allocation; _Alloc = std::allocator::Allocation>; std::vector<_Tp, _Alloc>::reference = Eigen::internal::TensorBlockScratchAllocator::Allocation&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlfiEE>Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of reflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.hInvalid 'pads' attribute valueInput tensor has no dimensionsonnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc'pads' has wrong number of valuesdata_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = long unsigned int]Pads tensor should be an INT64 tensorvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) constpads_tensor.IsDataType()Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const onnxruntime::Mode&, T) [with T = unsigned char]pads_size == 2 * data_rankN11onnxruntime7PadBaseEN11onnxruntime3PadEx_zero_point must be null or a scalar or 1D tensor or size 1.void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cczero_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)DequantizeLinear with type int32 should have no zero point or all zero points should be 0onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = int]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })ZN11onnxruntime17ParQuantizeLinearIaEEvPKfPT_mfS3_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime16DequantizeLinearIaEEN11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIiEEN11onnxruntime14QuantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEEAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hA dimension cannot be less than -1, got onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hThe input tensor cannot be reshaped to the requested shape. Input shape:At most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizerequested_shape[i] >= -1, requested shape:!allow_zerounknown_dim == -1A shape tensor must be a vector tensor.virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constshapeTensor->Shape().NumDimensions() == 1N11onnxruntime7ReshapeEN11onnxruntime9Reshape_1E Resize operatorUpsample operatorscale >= 1scale > 0Scale value should be greater than or equal to 1.void onnxruntime::UpsampleBase::ScalesValidation(const std::vector&, onnxruntime::UpsampleMode) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.hScale value should be greater than 0.'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)mode attribute is . It can only be (default) or onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)onnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&)info.GetAttr("mode", &mode).IsOK()info.GetAttrs("scales", scales_).IsOK()onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&)`tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attributeonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&)exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0.void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector&) constpytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizecoordinate_transform_mode:[] is not supportted!yet this opset round_prefer_floorround_prefer_ceilnearest_mode:[] is not supported!scales_size > 0ߩYܩN11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEE@?extrapolation_vacoordinate_transtensor/upsample.round_prefer_floReverseSequenceonnxruntime::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()time_axis and batch_axis must have different values but both are . Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axisInvalid 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]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = 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]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]sequence_lens shape must be {batch_size}. Got:virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc. batch_size=Unknown tensor type of T,㜪 AݖKTTPN11onnxruntime17ReverseSequenceOpEScatterElementsScatteronnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank= vs . Input rank=Indices dim= at pos= is greater than input dim=N11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbSsEEEEEdata type is different from updaIndices and updates must have thIndices type is ScatterNDinput tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = N11onnxruntime13ScatterNDBaseEN11onnxruntime9ScatterNDE*ZNK11onnxruntime9ScatterND13ScatterNumberERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime9ScatterND13ScatterStringERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_N11onnxruntime5ShapeESizeN11onnxruntime4SizeEMissing or invalid starts and ends attributeonnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_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()Unexpected element size of void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*)onnxruntime::SliceImpl >:: >&)>/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccoutput == output_endonnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>Starts must be a 1-D arrayEnds must be a 1-D arrayFillVectorsFromInputStarts 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 'axes' has duplicates'axes' has an axis outside of the tensor dimension count'step' value cannot be 0Cannot slice scalarsN11onnxruntime9SliceBaseEN11onnxruntime6Slice1EN11onnxruntime7Slice10EFillVectorsFromIDepthToSpaceSpaceToDepthSpaceDepth ops require a 4-D input. Provided rank: Attribute blocksize is not set.onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()DepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&)CRDDCR/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in DepthToSpace op: Unsupported input type in SpaceToDepth op: N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthEN11onnxruntime12DepthToSpaceEe@SpaceToDepth requires input height to be a multiple of block_sizuires input width to be a multiple of block_sizeDepthToSpace requires input deptle of (block_siz*>Invalid value in 'split' attribute. All values must be > 0onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Axis= NumOutputs= Input shape=Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was An split tensor must be a vector tensor.onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccsplit_tensor->Shape().NumDimensions() == 1onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = signed char]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = int]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = float]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = std::basic_string]Split operator does not support virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime9SplitBaseEN11onnxruntime5SplitEstatic std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const std::vector&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h. shape= must be 1 instead of Dimension of input input_shape[i] == 1virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeETileInput 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' tensorTile doesn't support string type yetvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc!input_tensor.IsDataType()Tile doesn't have an implementation yet for the type: N11onnxruntime4TileEMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, const std::vector&, const std::vector&, size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.ccnaxes > 0bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector&, size_t, const std::vector&, 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, const std::vector&, size_t, const std::vector&, 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, const std::vector&, size_t, const std::vector&, 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, const std::vector&, size_t, const std::vector&, 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=[ perm: does not align with rank of input data: 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.Transpose not implemented for empty tensors.void onnxruntime::DoTransposeEltWise(int64_t, const std::vector&, size_t, const std::vector&, const string*, std::string*)(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::DoTransposeImpl(int64_t, const std::vector&, size_t, size_t, const std::vector&, const string*, std::string*)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, const std::vector&, size_t, size_t, const std::vector&, const uint8_t*, uint8_t*, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)num_axes > 0Mismatched data types between input and output Tensors. virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime13TransposeBaseEN11onnxruntime9TransposeETriluonnxruntime::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.ccIsScalarOr1ElementVector(k)N11onnxruntime5TriluEk should be a 1-D or 0-D tensor.Uniquestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Unsupported tensor type of N11onnxruntime6UniqueEMissing/Invalid 'axes' attribute valueonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::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_tensorN11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEResize: 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. Output dim value: Input dim value: Dimension: output_dims[i] == 0Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector&, const std::vector&, std::vector&) constResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const std::vector&) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: size of roi array should be 2 * N where N is the rank of input tensor X.: '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.Resize: unexpected modeUpsample: unexpected modeUpsampleNearestBaseComputeonnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = float]Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()Invalid roi input index.roi_input_idx_ > 0sizes == nullptronnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const std::vector&) const [with T = int]onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext*, const std::vector&, const std::vector&, const std::vector&) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]ZN11onnxruntime16UpsampleBilinearIhEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime16UpsampleBilinearIiEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime16UpsampleBilinearIfEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_@A@ N11onnxruntime5WhereIhEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereIdEEN11onnxruntime5WhereISsEERegisterNchwcKernels/onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterQuantizationKernelsRegisterCpuContribKernelsCropAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder ( input dimensions insteadInput's height () + bottomBorder (Input's width () + rightBorder () + scale_[0] () + scale_[1] (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEECropAndResizebilinear specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1Null crop_size_ptrZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKSsPKiPS1_EUllE_EEvSG_lOS8_lEUllE_N11onnxruntime7contrib13CropAndResizeIfEEDynamicSliceAffineonnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo&) [with T = float]info.GetAttr("scale", &scale_).IsOK()N11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEExpandDimsAn axis tensor must be a scalar tensor.virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.haxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDims Axis is Axis must be within range [N11onnxruntime7contrib10ExpandDimsEonnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()N11onnxruntime7contrib14FusedConvFloatEFusedGemm/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib9FusedGemmIfEETransposeMatMul!(__hi < __lo)constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = long int]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_algo.hGridSamplebicubiczerosborderreflectionpadding_mode "" not supported, expect bilinear, nearest or bicubiconnxruntime::contrib::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/grid_sample.ccmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"" not supported, expect zeros, border or reflectionpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection" does not match input batch size onnxruntime::common::Status onnxruntime::contrib::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = float]Only 4-D tensor is supportedGrid batch size grid_dims[0] == N, expect 2Last dimension of grid: grid_dims[3] == 2constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = float]ZNK11onnxruntime7contrib10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib10GridSampleIfEEp@@?@ImageScaleronnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()) does not match the number of channels (Bias size (N11onnxruntime7contrib11ImageScalerIfEEInverseN11onnxruntime7contrib7InverseE*ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false]/onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.ccop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true]ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib9LayerNormIfLb0EEEN11onnxruntime7contrib9LayerNormIfLb1EEEN11onnxruntime7contrib9LayerNormIdLb0EEEN11onnxruntime7contrib9LayerNormIdLb1EEESparseToDenseMatMulCOO indices must be 2-D, got: is out of bounds of lhs_right: is out of bounds of out_left: COO k index: COO m index: Currently supporting only 2-D matricesCan not multiply A and B as inner dimension does not match. inner_A: Expecting COO 2-D indices shape/onnxruntime_src/onnxruntime/contrib_ops/cpu/math/sparse_dense_matmul.ccExpecting the same number NNZ == size of Inner indices vs inner_B: Expecting 2xValues == indicesOuter size must be M + 1N11onnxruntime7contrib19SparseToDenseMatMulE/onnxruntime/contrib_ops/cpu/math/sparse_dense_mCurrently support only COO and CMatMulInteger16onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.ccA != nullptr && B != nullptrN11onnxruntime7contrib15MatMulInteger16IssiEEMaxpoolWithMaskN11onnxruntime7contrib15MaxpoolWithMaskEN11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEEcount_include_patrib_ops/cpu/maxpool_with_mask.hMurmurHash3virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_byteskeysinput_num_bytes % 4 == 0N11onnxruntime7contrib11MurmurHash3Eonnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.hinfo.GetAttr("channels_last", &channels_last_).IsOK()invalid channel countchannels_ > 0onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&)info.GetAttr("channels", &channels_).IsOK()scales_.size() == 4' for NCHWc UpsampleUnsupported mode 'onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&)info.GetAttrs("scales", scales_).IsOK()scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRIConnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)pool_attrs_.kernel_shape.size() == 2virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.ccX_rank == 4(channels % 4) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) constchannels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4X_shape.NumDimensions() == 4Unsupported convolution size.virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)output and sum shape must matchonnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constN11onnxruntime7contrib13NchwcPoolBaseEN11onnxruntime7contrib12ReorderInputEN11onnxruntime7contrib13ReorderOutputEN11onnxruntime7contrib9NchwcConvEN11onnxruntime7contrib12NchwcMaxPoolEN11onnxruntime7contrib16NchwcAveragePoolEN11onnxruntime7contrib13NchwcUpsampleE*ZNK11onnxruntime7contrib12ReorderInput7ComputeEPNS_15OpKernelContextEEUllE_*ZNK11onnxruntime7contrib13NchwcUpsample7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_N11onnxruntime7contrib17QLinearLookupBaseIaEEN11onnxruntime7contrib16QLinearLeakyReluIaEEN11onnxruntime7contrib17QLinearLookupBaseIhEEN11onnxruntime7contrib16QLinearLeakyReluIhEEN11onnxruntime7contrib14QLinearSigmoidIaEEN11onnxruntime7contrib14QLinearSigmoidIhEEZN11onnxruntime7contrib16QLinearLeakyReluIaEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib16QLinearLeakyReluIhEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib14QLinearSigmoidIaEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIhEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ #<void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper]MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_binary_op.ccIsScalarOr1ElementVector(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 = unsigned char]*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_N11onnxruntime7contrib10QLinearAddIaEEN11onnxruntime7contrib10QLinearAddIhEEN11onnxruntime7contrib10QLinearMulIaEEN11onnxruntime7contrib10QLinearMulIhEEAt least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!onnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_concat.ccinput_def_count >= 8 && (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[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext*) constinput_count_x3 >= 6 && input_count_x3 % 3 == 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Tensor*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Input 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[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime7contrib13QLinearConcatE*ZN11onnxruntime7contrib13QLinearConcatC4ERKNS_12OpKernelInfoEEUlfE_*ZNK11onnxruntime7contrib13QLinearConcat7ComputeEPNS_15OpKernelContextEEUlfE_Input x_scale must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_scale)input 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 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)N11onnxruntime7contrib24QLinearGlobalAveragePoolE*ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE0_*ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE1_ @P@trib_ops/cpu/qlinear_global_averQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1void 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; onnxruntime::contrib::LookupTableArrayTransformer = std::function]/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.ccQlinearBuildLookupTable : 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 1QlinearBuildLookupTable : 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; onnxruntime::contrib::LookupTableArrayTransformer = std::function]ZN11onnxruntime7contrib23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = long int; _Alloc = std::allocator]virtual onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_pool.ccUnsupported 'dtype' in QLinear Pooling:QLinear Pooling unsupported pooling size!N11onnxruntime7contrib18QLinearAveragePoolEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEEZN11onnxruntime7contrib16dequantize_arrayIhEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_QAttentiononnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.hinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&]ApplyAttention/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h/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>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime7contrib13AttentionBaseEN11onnxruntime7contrib16AttentionCPUBaseEZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiPKSt6vectorIlSaIlEES4_biiiiS6_S4_PNS_11concurrency10ThreadPoolES6_EUlllE_ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib10QAttentionIfEE@Fqkv_hidden_sizes@@@@4D mask in attention cpu kernel is not supportedtrib_ops/cpu/quantization/attentDynamicQuantizeLSTM} for per-tensor/layer quantization or shape {} for per-channel quantization. Actual:Weight zero point must be zeroW_zero_point, 4*R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : Weight point must be constantRecurrent/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccN11onnxruntime7contrib19DynamicQuantizeLSTMEDynamicQuantizeMatMulComputeCommon/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validonnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext*, const uint8_t*, const onnxruntime::TensorShape&, float, uint8_t, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) constIsScalarOr1ElementVector(a_zero_point_tensor)N11onnxruntime7contrib24MatMulIntegerToFloatBaseEN11onnxruntime7contrib21DynamicQuantizeMatMulEN11onnxruntime7contrib20MatMulIntegerToFloatENhwcMaxPoolvirtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0N11onnxruntime7contrib11NhwcMaxPoolEntization/nhwc_mTATBTCTYZTYQGemmQGemm : scale of input a must be a scalar or 1D tensor of size 1static void onnxruntime::contrib::QGemm::CheckInputs(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::GemmHelper&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccQGemm : zero point of input a must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zp)QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QGemm : scale of input b must be a scalar or 1D tensor of size 1 or Nb_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : zero point and scale of input b should have same shape sizeb_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)QGemm : scale of y must be null or a scalar or 1D tensor of size 1y_scale == nullptr || IsScalarOr1ElementVector(y_scale)void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = int; int64_t = long int]N11onnxruntime7contrib5QGemmESampleOpN11onnxruntime7contrib8SampleOpIfEESkipLayerNormalizationonnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double]input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib13SkipLayerNormIfEEN11onnxruntime7contrib13SkipLayerNormIdEEskip is expected to have same shLast dimension of gamma and input does not matchf beta and inputf bias and inputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::StringPiece; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::StringPiece&]Tokenizerattribute mark is not setmincharnum_ > 0!tokenexp.empty()separators must not be empty!separators.empty()Can not digest separators: Can not digest tokenexp: onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute pad_value is not setattribute mincharnum is not setattribute mincharnum must have a positive valueEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Input string contains invalid utf8 charsconst value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]Input string contains invalid utf8 chars: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedN11onnxruntime7contrib9TokenizerEN11onnxruntime7contrib6UniqueIfEEInput tensor to Unique op shouldtypename 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&]WordConvEmbeddingConv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute .Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccN11onnxruntime7contrib17WordConvEmbeddingEconv_window_sizechar_embedding_shӥhӥhӥhSeluCeluEluNo attribute with name:''is defined.Attribute name and type don't match for 'Init/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.hunknown kernel typeonnxruntime::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::Selu]onnxruntime::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::Elu]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::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::Selu]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::Softplus]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::Tanh]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::ThresholdedRelu]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::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]N11onnxruntime8functors7SigmoidIfEEN11onnxruntime8functors4TanhIfEEN11onnxruntime8functors3EluIfEEN11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEEN11onnxruntime8functors11HardSigmoidIfEEN11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEEN11onnxruntime8functors9LeakyReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEEN11onnxruntime8functors4ReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEEN11onnxruntime8functors4ReluIdEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEEN11onnxruntime8functors4ReluIaEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEEN11onnxruntime8functors4ReluIiEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEEN11onnxruntime8functors4SeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEEN11onnxruntime8functors7SigmoidIdEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEEN11onnxruntime8functors8SoftplusIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEEN11onnxruntime8functors8SoftsignIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEEN11onnxruntime8functors4TanhIdEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEEN11onnxruntime8functors4CeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEEN11onnxruntime8functors15ThresholdedReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEEN11onnxruntime8functors18ParametricSoftplusIfEEN11onnxruntime8functors10ScaledTanhIfEEAAO@O9BBBB¥¥¥;?;?;?;?BBBBgiP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>O@O@O@O@OOOO9999>@9@@IfFailed to create output tensor for If output outputs.'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.ccnum_subgraph_outputs == static_cast(num_outputs)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]then_branchinfo == nullptrSetupSubgraphExecutionInfoSetupSubgraphExecutionInfo should only be called once for each subgraph.virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)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>::back() [with _Tp = const OrtMemoryInfo*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtMemoryInfo*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]Failed to create output tensor for std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const OrtValue*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtValue* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]else_branch' attribute.session_stateCreateFeedsFetchesManager must be called prior to execution of graph.virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph 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&]void onnxruntime::If::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()N11onnxruntime11controlflow18IControlFlowKernelEN11onnxruntime2IfE*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_Only tensors or sequence of tensors are suppportLoop inputs. Found: but has 'Loop' node has Graph in 'body' attribute of Loop should have onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccstatic_cast(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]All scan outputs MUST be tensorsvoid onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector&, std::vector&)last_outputs[j + 1].IsTensor()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&]Inconsistent shape in loop output for output. Expected:onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager&)::virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)'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: ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Subgraph SessionState was not found for 'body' attribute.virtual 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&]feeds_fetches_manager_void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("body", &proto).IsOK()FN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEN11onnxruntime4LoopEPFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEcontrolflow/loopScan<8> spec does not support transpose of output. This should never be called.Scanonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cctypename 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&] dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid scan input: has batch size of has length of sequence_lens length of did not match batch size of ValidateInputInvalid entries in sequence_lens. Max sequence length was std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&]Output OrtValue has not been created for loop state variable output onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::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; std::vector<_Tp, _Alloc>::reference = onnxruntime::scan::detail::LoopStateVariable&]p_mlvalueCreateLoopStateVariablesvoid onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()Subgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]feeds_fetches_manager_ && info_FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEN11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorEonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::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&] but input '' dimension SetupInputsAttempt to retrieve final output before it was set.const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()final_output_mlvalue_. Output tensor rank was TransposeOutputInvalid value in scan_input_axes for input . Input tensor rank was output_mlvalueonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&)void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputs but expected onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]N11onnxruntime4ScanILi9EEE*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_scan_input_direcscan_output_direscan_output_axesMismatch between expected shape and shape from first output is not compatible with Misuse of LoopStateVariable. Attempt to move beyond end of sequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cciteration_num_ < sequence_len_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.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&]Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()' was Number of entries in 'Invalid values in 'void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector&, size_t)directions.size() == num_entries'. 0 == forward. 1 == reverse. inputs but Scan was only given The subgraph in 'body' requires onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)num_variadic_inputs == num_subgraph_inputsCreateFeedsFetchesManagerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&]Failed to create output tensor for output #AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocatoronnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)!is_concrete_shape_AllocateFinalOutputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::scan::detail::LoopStateVariable&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&]IterateSequenceSubgraph must have the shape set for all outputs but did not.*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_onnxruntime::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::ParametricSoftplus]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh]ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEEN11onnxruntime7contrib4GeluIfEE5?@AttnLSTMonnxruntime::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_ * 3Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{Attention query layer weight shape error! Expected:{Attention v weight shape error! Expected:{Attention layer weight shape error! Expected: {/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc}, actural: ], while found!, am_attn_size}, Got:}, Got: }. Got: , aw_attn_size}. Got:virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib17DeepCpuAttnLstmOpEN11onnxruntime7contrib16AttentionWrapperIfEE row[ rows[ [seqno=Expect to have present state output when past state input is givenonnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 'bias' is expected to have 1 dimension, got hidden_size should be divisiable by num_heads:qkv_sizes doesn't match the wights dimensionInput '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 Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional_ set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'extra_add_qk' is expected to have 4 dimensions, got Input 'extra_add_qk' dimension 0 should be same as batch_size, got Input 'extra_add_qk' dimension 1 should be same as number of heads, got Input 'extra_add_qk' dimension 2 should be same as sequence_length, got Input 'extra_add_qk' dimension 3 should be same as sequence_length, got ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib9AttentionIfEEAttention cannot have past sequence and extra adInput 1 dimension 0 should have same length as dimension 2 of inn 1 should be 3 times of hidden hidden_size should be divisiable attribute should have 3 element first element should be same asInput 'bias' dimension 0 should have same length as dimension 1 of input 'weightuse_approximation/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]N11onnxruntime7contrib8BiasGeluIfLb0EEEN11onnxruntime7contrib8BiasGeluIfLb1EEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_y"=y"=y"=y"=*BL?*BL?*BL?*BL?y"=Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got same length as the last dimensioonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.ccZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib18EmbedLayerNormBaseEN11onnxruntime7contrib14EmbedLayerNormIfEEinput index out input_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 gamma 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 Input 0 and 1 shall have same shInput 0 and 7 (mask) shall have word_embedding and segment_embedding shall have same dimension 1nd position_embedding shall have same dimension TidNGramRepeatBlockonnxruntime::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_ > 0onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) const::token_id < vocab_sizevirtual onnxruntime::common::Status onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeN11onnxruntime7contrib16NGramRepeatBlockEZNK11onnxruntime7contrib16NGramRepeatBlock7ComputeEPNS_15OpKernelContextEEUlllE0_QEmbedLayerNormalization/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/qembed_layer_norm.cc*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfaEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfhEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib15QEmbedLayerNormIfEEWord embedding scale must be a scalar or 1D tensPosition embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tGamma scale must be a scalar or 1D tensor of sizBeta scale must be a scalar or 1D tensor of sizeWord embedding zero point must be a scalar or 1D tensor of size ng zero point must be a scalar or 1D tensor of sg zero point must be a scalar or 1D tensor of siGamma zero point must be a scalar or 1D tensor oBeta zero point must be a scalar or 1D tensor ofCDistonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()sqeuclideanonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = double]The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:N11onnxruntime7contrib5CDistIfEEN11onnxruntime7contrib5CDistIdEEConvTransposeWithDynamicPadsN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEunimplemented activation: activation_params count mismatchnot support normalize yet.!normalize_onnxruntime::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]/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc is not in (0, Real memory steps void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]mem_steps <= max_memory_steps_ && mem_steps > 0N11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEEbool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*)/onnxruntime_src/onnxruntime/core/util/math_cpu.ccdims[d_i] < d_maxGemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = float; Provider = onnxruntime::CPUMathUtil]void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil]?Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1CudaCudaPinnedSpecified device is not supported./onnxruntime_src/onnxruntime/core/framework/allocator.ccN11onnxruntime12CPUAllocatorEReceived invalid value of arena_extend_strategy duplicated allocatorvoid onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr)/onnxruntime_src/onnxruntime/core/framework/allocatormgr.ccSt14default_deleteIN11onnxruntime10IAllocatorEESt14default_deleteIN11onnxruntime8BFCArenaEESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEe/framework/allocudaMallochipMallocCould not find Region for onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cch < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const/onnxruntime_src/onnxruntime/core/framework/bfc_arena.hp_int < base_int + memory_size_p_int >= base_intstd::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator) [with _Key = long unsigned int; _Val = long unsigned int; _KeyOfValue = std::_Identity; _Compare = onnxruntime::BFCArena::Bin::ChunkComparator; _Alloc = std::allocator; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree, onnxruntime::BFCArena::Bin::ChunkComparator, std::allocator >::iterator; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator = std::_Rb_tree, onnxruntime::BFCArena::Bin::ChunkComparator, std::allocator >::const_iterator]void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binvoid onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle)!c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use()c2->prev == h1void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::DeallocateRawInternal(void*)h != kInvalidChunkHandlevoid onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t)void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)!chunk->in_use()BFCArenaCreating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy: Creating bins of max chunk size with following configs: initial_chunk_size_bytes: initial_growth_chunk_size_bytes: onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b) BFC Arena shrunk by bytes. Could not find Region for: entry != regions_.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = void*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = void*&] The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*)std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_num Chunk Free DumpMemoryLogAllocator:: Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_useDiff between in-use and requested bytes is Summary of in-use chunks by size: Available memory of ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at is smaller than requested bytes of Incorrect arena extend strategy.onnxruntime::BFCArena::Extend(size_t)::onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t)0 == memory_size % kMinAllocationSizeFailed to allocate memory for requested buffer of size AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool)ReserveReserving memory in BFCArena for virtual void* onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()N11onnxruntime8BFCArenaEe/framework/bfc_Config with key [] already exists with value []. It will be overwrittenConfig key is empty or longer than maximum lengtConfig value is longer than maxie/framework/confCopyTensors/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccCopySparseTensorsvirtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) constsrc.SizeInBytes() == dst.SizeInBytes()N11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEdata_transfer registered is nullptr.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Tensor size mismatchThere's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::utils::data_types_internal::TypeNode&](null)doubleuint8uint16uint32uint64bfloat16,???????@@D@??4@$@,?,?t?GetElementTypebool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&)/onnxruntime_src/onnxruntime/core/framework/data_types.cc)VZ)V)VYY)VLY Ybool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)Y*V[Y*VY*V|[4[Y*VZZvirtual 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())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constInvalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)Only ONNX MLDataType can be registeredvoid onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)We do not expect duplicate registration of types for: 2VPx`xpxxxxxxxxy(y@y2V2V xsparse tensor type F5V@HXhxȑؑF5VF5V expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = float]/onnxruntime_src/include/onnxruntime/core/framework/data_types.helem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = short int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = short unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = long int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = long unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = bool]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = std::basic_string]value_proto != nullptrstatic void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = long int]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = std::basic_string; V = double]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = long int]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto&) [with K = long int; V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::map, float>]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto&) [with T = std::map] is not currently registered or supportedMLDataType for: N11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeISsEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeIbEEN11onnxruntime17PrimitiveDataTypeISsEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_SsN11onnxruntime9MLFloat16EN11onnxruntime8BFloat16ESt3mapISsfSt4lessISsESaISt4pairIKSsfEEESt3mapIlfSt4lessIlESaISt4pairIKlfEEEUnloading DSO Failed to unload DSO: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while destructing CustomOpsLoader with message: A dso with name has already been loaded.LoadExternalLibCaught exception while loading custom ops with message: N11onnxruntime11ExLibLoaderEe/framework/ex_lMemory pattern planner is not enabled on this execution framework. does not match actual shape of Expected shape from model of 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_int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast(offset) < node_values_size_const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int)/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocation for ort_value_idx= size=void onnxruntime::ExecutionFrame::TraceFree(int)TraceFree for ort_value_idx=Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]AllocateMLValueTensorSelfOwnBufferHelper, block in memory pattern size is: , fall back to default allocation behaviorort_value.Fence() == nullptrFor ort_value with index: but the actually size is: node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, const std::vector&)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()void onnxruntime::IExecutionFrame::Init(const std::vector&, const std::vector&, const std::unordered_map&, const std::function&)>&, const std::vector&)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Could not find OrtValue with idx 'T* OrtValue::GetMutable() [with T = onnxruntime::SparseTensor]invalid index ReleaseMLValueImplFetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.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.onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, bool, const onnxruntime::SessionState&)AllocateAsPerAllocationPlanInvalid allocation kind: mlvalue.Fence() == nullptr Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&)shape && tensor.Shape() == *shapeshape && sp_tensor.DenseShape() == *shapeExecutionFrame returned nullptr failed. Error:std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&]onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::unordered_map >&, const onnxruntime::SessionState&)buffers_.find(location) == buffers_.end()Allocation of memory pattern buffer for N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameE*ZN11onnxruntime14ExecutionFrameC4ERKSt6vectorIiSaIiEERKS1_I8OrtValueSaIS6_EES5_SA_RKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS6_RbEESt4hashImESt8equal_toImESaISt4pairIKmSO_EEERKNS_12SessionStateEEUlRKSsE_e/framework/execVerifyOutputSizeAllocateMLValueTensorPreAllocateIExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccduplicated allocator: IExecutionProvider constructor must be called with true for use_metadef_id_creatorvirtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, uint64_t&) constmetadef_id_generator_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]N11onnxruntime18IExecutionProviderEution_provider.cTryInsertAllocatstd::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer&, const string&, const std::vector&, const std::vector&)/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.ccvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = long unsigned int; _Alloc = std::allocator]ORT 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 nodekernel_info != nullptrGetCpuPreferredNodesCandidate for fallback CPU execution: *ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKSsRKSt6vectorIPKNS_14KernelRegistryESaIS8_EERKS5_ImSaImEEEUlRKNS_7NodeArgEmE0_GetCpuPreferredNe/framework/fallback_cpu_capabilMapNamesToMLValueIdxs/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/framework/func_kernel.hcompute_info_->create_state_func(&context, &func_state_) == 0InlineNodes/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccPartitionOnnxFormatModelImpl1 == capability.nodes.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::KernelRegistry*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::KernelRegistry*&]onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::KernelRegistryManager&, const string&, onnxruntime::IExecutionProvider::FusionStyle, onnxruntime::GraphPartitioner::Mode, int&)Must use Function based fusion when exporting compiled nodes to dll.onnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, onnxruntime::GraphPartitioner::Mode, int&)fusion_style == IExecutionProvider::FusionStyle::Function did not return correct number of compiled functionsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeComputeInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeComputeInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename 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&]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&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]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&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&]PartitionOnnxFormatModelstd::__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; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::IExecutionProvider]PartitionOrtFormatModelImplEP: has Compile error: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]single_node_compute_func should have 1 elements. Execution Provider must generate unique names across the entire model.Existing entry in compiled kernel hashes for onnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph&, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, std::unordered_map, long unsigned int>&, int&)No provider specified.PartitionCompiled kernel hashes must be providedonnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::GraphPartitioner::Mode, std::unordered_map, long unsigned int>*) constcompiled_kernel_hashes != nullptrN11onnxruntime14FunctionKernelEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]onnxruntime::KernelDefBuilder& onnxruntime::KernelDefBuilder::VariadicAlias(int, int)/onnxruntime_src/onnxruntime/core/framework/kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: (node_version: ),void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::]/onnxruntime_src/onnxruntime/core/framework/kernel_registry.cclen <= op_schema.inputs().size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::]Found kernel for Op with name ( in the supported version range However the types are incompatible. This op has been implemented only for the following types ( but the node in the model has the following type ( kernel is not supported in Kernel not foundtypename 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&] Encountered following errors: (TryCreateKernelkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.: Conflict with existing kernel def hash.e/framework/kern (node ). found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution ProviderFailed to find kconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccnullptr != type_protoS.5/::/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init > >::ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_type_proto is not of type map!type_proto is not of type sequence!\\[D\[[\[]]]]^<^]]]OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadydedddddeeTempSpace allocator not foundExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&). Num args is Invalid arg_num of arg_num < arg_counts.size()int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constOrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)N11onnxruntime15OpKernelContextEcannot find allocatorconst OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const/onnxruntime_src/onnxruntime/core/framework/op_kernel_info.ccNo attribute with this name is defined.Attibute name and type don't matchNo attribute with name: is defined.Attribute: expected to be of type: but is of type: Requested attribute: is expected to have type: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)/onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&]!using_counters_current <= buffer_size_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemoryPattern&]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int]Can't slice a non-tensor OrtValue. Type was static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]OrtValue has not been allocated so can't be sliced.Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsTensor()ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizestatic onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]Received nullptrReceived OrtValue is not a tensor. Only tensors Buffer containing the initializer must be owned An OrtValue for this name has already been addedSetGraphAndCreateKernels must be called prior to GetExecutionInfo.const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const/onnxruntime_src/onnxruntime/core/framework/session_state.ccnode_index_info_KernelUseSharedPrePackedBuffersThe kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsFailed to find input name in the mapping: const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::OpKernel*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::OpKernel*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector&)CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.Allocator already registered for . Ignoring allocator from onnxruntime::SessionState::PrepackConstantInitializedTensors(std::unordered_map, 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 emptyUsing cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container!op_type.empty()+ used in the node: which is of op type: Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)output_names_to_nodeinfo.empty()LoadFromOrtFormatCan't find node with index . Invalid ORT format model.Unable to find compiled kernel hash for node 'PopulateKernelCreateInfoduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?void onnxruntime::AccumulateAllNestedSubgraphsInfo(const onnxruntime::SessionState&, const string&, size_t, onnxruntime::SubgraphsKernelCreateInfoMaps&)subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()FinalizeSessionStateImplsession.disable_prepacking Attribute:' OpType:p_op_kerneltypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SequentialExecutionPlan&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ITensorAllocator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ITensorAllocator&]Missing session state for subgraph. Node:'onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, std::unordered_map, long unsigned int>&, const std::unordered_map, OrtMemoryInfo>&, bool)entry != node_to_subgraph_ss.second.cend()OuterScopeNodeArgLocationAccumulator for attribute Entry exists in node void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()CreateSubgraphSessionStateMain Graph instance should have populated all subgraphs when being resolved.onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState()FinalizeSessionStatestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned int&]GetSubGraphSessionStatesOrtFormatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextE*ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERSt13unordered_mapISs13OrtMemoryInfoSt4hashISsESt8equal_toISsESaISt4pairIKSsSD_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERSt13unordered_mapISs13OrtMemoryInfoSt4hashISsESt8equal_toISsESaISt4pairIKSsSD_EEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRSt13unordered_mapISsmSt4hashISsESt8equal_toISsESaISt4pairIS1_mEEERKSB_ISs13OrtMemoryInfoSD_SF_SaISG_IS1_SL_EEEbEUliRK8OrtValueRKNS_11OrtCallbackEbbE0_*ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_e/framework/sessSize mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model. is null. Invalid ORT format model.Subgraph SessionState for SessionState for subgraphs is null. Invalid ORT Kernel create info is null. Invalid ORT format mfo node indices are null. Invalid ORT format modfo hashes are nu input with name is not used by any node.SaveInputOutputNamesToNodeMapping/onnxruntime_src/onnxruntime/core/framework/session_state_utils.ccDeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires , Got DeserializeTensorProtoFailed to copy tensor to Saving initialized tensors.SaveInitializedTensors bytes for Deserialize tensor OrtValue indexes should have been populated.onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string&, const onnxruntime::GraphViewer&, const AllocatorPtr&, const onnxruntime::OrtValueNameIdxMap&, const std::vector&, onnxruntime::ITensorAllocator&, const SaveTensorFunction&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&)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 (entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name (Done saving initialized tensors*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE_*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE0_shape.Size() musFailed memory siion_state_utils.SaveInputOutputNamesToNodeMappinstring tensor is not supported for copying betweSaveInitializedTsession.use_device_allocator_forValidateBlockSparseShapesExpecting index blocks: Expecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2 to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}AllocateBufferValues size must be less than total buffer size: SparseTensor Allocation failed for size: Divide by zerostatic void SafeIntExceptionHandler::SafeIntOnDivZero()the ort_value must contain a constructed sparse tensorstatic const onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/sparse_tensor.ccstatic onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue&)this tensor already has populated sparse_indicesMust contain Coo format. Got: onnxruntime::SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constFormat() == SparseFormat::kCooExpecting to contain one index, got: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Tensor&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]format_data_.size() == 1UMust contain Csr format. Contains: onnxruntime::SparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constFormat() == SparseFormat::kCsrcExpecting two indices. Got: format_data_.size() == 2UMust contain BlockSparse format. Got: onnxruntime::SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constFormat() == SparseFormat::kBlockSparseExpecting one index. Got: This method should follow a call to constructor that supplies the allocatoronnxruntime::SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)Sparse format must not be set. Already contains format: Format() == SparseFormat::kUndefinedallocator_ != nullptrMakeBlockSparseDataValidateCsrIndicesdense shape must 2-D. Got: Expecting inner index size: the same as values size: rows: Inner and Outer indices must either be both zero or non-zeroOuter index count must be rows + 1 or zero. Got: This method does not expect allocator to be setonnxruntime::SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)MakeCsrDataNot expecting an allocator setCopyDestination should be emptyMust have the same shapeThis instance should not be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeUnable to find a data transfer for copying from device type: to device type: CopyData dst_size: Must have the same size. Got src_size: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Use MakeCsrStringsUse MakeBlockSparseStringsExpecting data type to be set as string must be equal to or twice the values size: std::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constIndex size: values_count == index_sizeonnxruntime::SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)MakeCooDataUse MakeCooStringsZN11onnxruntime10IAllocator13MakeUniquePtrIvEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbEUlPvE_e/framework/sparCurrently do not support dims higher than 2 dimensions: Expecting indices to be equal the number of values or be twice as many/onnxruntime_src/onnxruntime/core/framework/sparse_utils.ccSparseCooToDenseTensorUnsupported element size: Invalid index: > dense_size: Expecting inner indices to be same as nnz. Got: onnxruntime::common::Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const onnxruntime::DataTransferManager&, const onnxruntime::SparseTensor&, const AllocatorPtr&, const AllocatorPtr&, onnxruntime::Tensor&)inner_num == src.Values().Shape().Size()Outer indices must be M + 1. Got: outer_num == (rows + 1)SparseCsrToDenseTensorDenseTensorToSparseCooDenseTensorToSparseCsrInput must be ofUnable to convert strings tensor to a sparse tensor that is not SparseCooToDenseSupport 2-D matrDenseTensorToSpasor that not on tensor size overflowsize_t onnxruntime::Tensor::SizeInBytes() const/onnxruntime_src/onnxruntime/core/framework/tensor.ccshape.Size() must >=0dtype_ != nullptrvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t)Tensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr)tensor failed memory size calculationonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, std::shared_ptr, ptrdiff_t)Trace/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hWeight buffer for initializer 'Get preallocated buffer for initializer 'Mem pattern for initializer ' is not found' failedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtMemoryInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtMemoryInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Failed to get allocator for location: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]duplicated locationFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEoffsetchecksummodel format error! Need a key for the external or! Need a value for the externaor! Missing 'locInvalid tensor shape slice argument.onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccdimstart <= dimend && dimend <= size()Invalid dimension of dimension <= num_dims for SizeFromDimension. Tensor has int64_t onnxruntime::TensorShape::SizeToDimension(size_t) constint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constUnsupported indices_format passedconst onnxruntime::Tensor& {anonymous}::GetIndicesTensor(const OrtValue&, OrtSparseIndicesFormat)/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.ccthe ort_value must contain a constructed tensor or sparse tensorOrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)Argument is not a tensor UnpackTensor: the pre-allocate size does not match the size in protoInvalid TensorProtoz[[<, std::function)indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Element_size of: is not supported.ConstantNodeProtoToTensorProtoUnsupported attribute value type of onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&, const string&) in 'Constant' node 'Vl\VDVVMust have a valid data typeHasDataType(dense_proto) data_type: onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&)DenseTensorToSparseTensorProto*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_External data type cannot be STRe/framework/tensGetExternalDataIUnpackTensorWithExternalDataImplUnpackInitializeCalculateStaticCopyInfoForFeed/onnxruntime_src/onnxruntime/core/framework/utils.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = onnxruntime::SessionState::NodeInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SessionState::NodeInfo&]InternalTestingExecutionProviderconst OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&)const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&)exec_plan_ptrFindMemoryInfoForValueCalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfobool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector&, std::vector&)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector&, std::vector&)fetch_alloc_info.size() == copy_info.size()Failed to find allocator for device onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, std::vector*, std::vector*)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SrcDstPair; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IDataTransfer::SrcDstPair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SparseSrcDstPair; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IDataTransfer::SparseSrcDstPair&]allocator != nullptrBatchOrCopyMLValueCopyOneInputAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, const std::vector&, std::vector&, const std::vector&)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]ExecuteGraphImplcopy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesExecuteGraphstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const OrtMemoryInfo*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtMemoryInfo*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Unsupported OrtValue type to copy between devicee/framework/utilExecuteGraphImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)Previous entry was not terminated.void onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t)/onnxruntime_src/onnxruntime/core/framework/sequential_execution_plan.hstarts_.size() == ends_.size()Invalid 'start'. Value is smaller than previous 'end'.starts_.empty() || start > ends_.back()No matching 'start' entry.end >= starts_.back()void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t)starts_.size() == ends_.size() + 1Invalid 'end'. Value is larger than 'start'.SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap&, onnxruntime::NodeIndex)entry != kernel_create_info_map.cend()static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&)nullptr != tensor_type_basestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long unsigned int&]void onnxruntime::PlannerImpl::GenerateDeallocationPlan()AllocPlan(ml_value_idx).program_counter.Ends().back() == program_countervoid onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_foronnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&)p_providerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&, const KernelCreateInfoMap&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtMemoryInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtMemoryInfo&]void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const onnxruntime::GraphViewer&, const InitializedTensorSet&, const KernelCreateInfoMap&, const string&, size_t, std::vector >&)specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()ComputeReusePlan!IsNonTensor(*node_output)Only tensors are supported for external outputs for now.onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeReusePlan()vector::_M_fill_insertvoid onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)id >= 0 && static_cast(id) < ort_value_info_.size()Should not have entry in kernel create info with nullptr for kernel_defonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts()Can not find the execution provider Can not find the node p_kernel_defComputeUseCountsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan&]Invalid program_counter entries at index void onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule()entry.program_counter.HasValidEntries()CreatePlanThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::ComputeUseCounts()::found_in_outer_scope_location_mapN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanEZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_source and destination buffer size mismatche/framework/endiCopyLittleEndianfunc info for node: not found.GetFuncs/onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc already exist.std::__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; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = std::unordered_map, onnxruntime::FuncManager::FuncInfo>]Can't use func with null ptr*ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvE1_Unknown exception was caught by catch-all handler.Exception running nodes starting at _fence_before_kernel_time_fence_afterBegin executionRunNodeAsync does not.entry != nullptr node. Name:'' Status Message: /onnxruntime_src/onnxruntime/core/framework/parallel_executor.ccExiting due to terminate flag being set to true.onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&)Got nullptr from GetKernel for node: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ExecutionFrame; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ExecutionFrame&]All implicit inputs should have OrtValue instances by now. onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&)/onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hNon-zero status code returned while running Multiple errors were found.N11onnxruntime9IExecutorEN11onnxruntime16ParallelExecutorEN11onnxruntime23OpKernelContextInternalE*ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_e/framework/parallel_executor.ccthread_schedulinParallelExecutoruint64_t onnxruntime::PrePackedWeights::GetHash() const/onnxruntime_src/onnxruntime/core/framework/prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()Unsupported device allocator in the context of pre-packed weights caching: onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const string&)/onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.cc*ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKSsEUliE_graph_indexexec_plan_indexactivation_sizeparameter_sizeoutput_sizethread_scheduling_statsReleaseNodeMLValuesSequentialExecutor::Execute/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccN11onnxruntime18SequentialExecutorEN11onnxruntime21SimpleTensorAllocatorEAttribute expected to have tensor or sparse tensor typeinputs are expected to have tensor type and output type should not be null.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Input must be an optional-type value containing an element with type information. expected to have tensor or sparse type expected to have tensor type expected to have tensor or sparse tensor type: isinf_onlyisnan_only!(isinf_only && isnan_only)Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions./onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cconnxruntime::contrib::RegisterContribSchemas():: expected to have rank > but has rank Dimension mismatch in unification between Attribute should specify a shapeNegative values are not allowed in a shape specificationIncompatible dimensionsInputs 0 shall be 3 dimensionsqkv_hidden_sizes should have 3 elementsInputs 4 shall be 5 dimensionsInvalid bias shapeqkv_hidden_sizesoutput_shapeoutput_padding'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesinput_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.input_as_shapeextra_shapeInput's shape must be 4-D) + bottom_border () + right_border () + scale[0] () + scale[1] ('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)) needs to be greater than or equal to the top_border () needs to be greater than or equal to the left_border (transBtransAFirst input does not have rank 2Second input does not have rank 2Input axis is invalid: 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 rank must be >= 2. != mat_h:The inner-most 2 dimensions must have the same size (mat_w: expected to have rank stash_typeInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorShapeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorShapeProto*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::FunctionBodyHelper::NodeDef; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::FunctionBodyHelper::NodeDef&]ConstantX_biasT5T6T7XShapeRankZero1DAxis1DPrefixShapeNumReducedAxesSuffixShapeReducedShapeX2DXUMean2DMeanOfSquareSquareOfMeanVarVarPlusEpsilonDeviationNormalizedNormalizedTBiasedInvStdDev2DInvStdDevHalfOneERFCXERFCXPlus1PhiXNumber of attention headspastextra_addpresentinput_scaleweight_scaleinput_zero_pointweight_zero_pointLongformerAttentionwindowglobal_weightglobal_biasglobalConstrain to integer typesinput_idssegment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embeddingword_embedding_quantposition_embedding_quantgamma_quantbeta_quantScale for word embeddingsword_embedding_scaleScale for position embeddingsposition_embedding_scaleScale for segment embeddingssegment_embedding_scaleScale for 1D gamma tensorgamma_scaleScale for 1D beta tensorbeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointZero Point for 1D beta tensorbeta_zero_pointLayerNorm Outputlayernorm_outMask Index Outputmask_index_outbias tensorskipinv_std_varThe NGram size.ngram_sizescoresscores_outWhether every token can only attend to previous tokens. Default value is 0.Hidden layer sizes of Q, K, V paths in Attention3D input tensor with shape (batch_size, sequence_length, input_hidden_size)2D input tensor with shape (input_hidden_size, 3 * hidden_size), where hidden_size = num_heads * head_size1D input tensor with shape (3 * hidden_size)Attention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).additional add to QxK' with shape (batch_size, num_heads, sequence_length, sequence_length).3D output tensor with shape (batch_size, append_length, hidden_size)present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to float tensors.Constrain mask index to integer types2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.scale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.zero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantization3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.One sided attention windows length W, or half of total window length3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)Attention mask with shape (batch_size, sequence_length)Global attention flags with shape (batch_size, sequence_length)The epsilon value to use to avoid division by zero.2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.Zero point for word embeddingsZero point for position embeddingsZero Point for segment embeddingsZero Point for 1D gamma tensorConstrain input and output types to float32 tensors.Constrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.Constrain mean and inv_std_var to float tensors.2D input tensor with shape (batch_size, sequence_length)2D input tensor with shape (batch_size, vocab_size)2D output tensor with shape (batch_size, vocab_size)Constrain indices to integer typesConstrain scores input and output types to float tensors.Value of alphaValue of beta1D input tensor1D output tensorThe scale to apply.Threshold valueInput tensorOutput tensorstartsendsSliced data tensor.GivenTensorFillThe shape of filled tensorThe filled tensorInput data to be scaledOutput data after scalingGRUUnitdrop_stateshidden_prevgatesseq_lengthshiddenacross_channelsnormalize_varianceScaling valueRfftsignal_ndimnormalizedonesidedIrfftComplexMulinput_0input_1ComplexMulConjactivation_paramsactivation_alphaactivation_betaactivation_gammaStrings to tokenizeTokenized stringsmarkpad_valuetokenexpseparatorsmincharnumN-dimensional matrix AN-dimensional matrix BMatrix multiply resultsN-dimensional dense matrix Bsparse_tensor(float)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)An input tensor to hash.32-bit hash value.seedTensor of rank r >= 1.Tensor of rank q >= 1.conv_window_sizechar_embedding_sizeSpecify weights of convSpecify bias of convConstrain to tensor(int32).Constrain to tensor(float).Input tensor.Tensor after padding.2D matrix with shape (M,N)2D matrix with shape (K,N)extrapolation_valueroisbatch_indicescrop_sizeScale tensor.Bias tensor.Output data tensor.The input data as Tensor.The output.The normal input data.TorchEmbeddingpadding_idxscale_grad_by_frequpperBiasSoftmaxsoftmax_axisbroadcast_axisThe bias (or mask) as Tensor.residualtraining_modeThe output mask of dropout.IsAllFiniteIf true, check only for NaN.Input tensors to check.OptionalThe input element.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(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))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(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))OptionalHasElementThe optional input.OptionalGetElementpadding_modeGridBias applied to each channel, same size as C.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Tensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Constrain input and output types to all tensor types.Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.The previous GRU hidden state.Unactivated gate outputs from forget, update, and output gates, pre-activation.Array of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.The new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.The scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain input0 and output types to float tensorsConstrain input and output types to float tensorsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Specified axis to insert a dimensionInput/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 ignoredMatrix multiply results from A * BConstrain 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).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 multiplication2-dimensional sparse matrix A. Either COO or CSR formatConstrain 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.Tensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embeddingSpecify embedding vector of charThree 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 arrayTensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput data tensor from the previous layer.Saved inverse standard deviation used during training to speed up gradient computation.Constrain input types and output Y type to float tensors.Type of Mean and InvStdDev tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.A 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.A 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.apply softmax to elements for dimensions softmax_axis or higherbroadcast bias across input for dimensions broadcast_axis to softmax_axis-1(Optional) Seed to the random generator, if not specified we will auto generate one.The bias input, a vector with the same shape as last dim of dataThe residual input, must have the same shape as dataThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of input, which is typically the case during training.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.If true, check only for Inf, -Inf.Constrain the output to a boolean tensor.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Type of the element in the optional outputThe optional output enclosing the input element.Constrains input type to all tensor and sequence types.optional(seq(tensor(float16)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))Constrains output type to all optional tensor or optional sequence types.A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.Constrains input type to optional tensor and optional sequence types.Constrains output to a boolean tensor.Output element in the optional input.Constrain output type to all tensor or sequence types.Three 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.4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.4-D tensor of shape (N, C, H_out, W_out).Constrain input types to all tensor types.Constrain output types to float tensors.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.OptionalHasElement is expected to have 1 input.OptionalHasElement is expected to have 1 output.*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE1_*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE17_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE19_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE24_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE25_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE26_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE27_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE28_̼+̼+ F?AD?>;f?tensor rank too smallinvalid scales dimensioninvalid scales valueFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_PFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE2_e/graph/contrib_ops/nchwc_schemaGlobalAveragePooInput tensor must have at least 2 dimensionsOutput tensor must have at least 2 dimensionsinputs 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.x_scalex_zero_pointw_scalew_zero_pointy_scaley_zero_pointInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.Scale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'. It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to singed/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.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input scale. It's a scalar, which means a per-tensor/layer quantization.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.N4onnx16InferenceContextEN11onnxruntime7contrib20NhwcInferenceContextE*ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE2_ops/nhwc_schema_QLinearGlobalAveQLinearAveragePo Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} Input A's scale. It's a scalar, which means a per-tensor/layer quantization.Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Input B's scale. It's a scalar, which means a per-tensor/layer quantization.Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Result, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors.{name}{additionalDocumentation}First operand.A_scaleA_zero_pointSecond operand.B_scaleB_zero_pointC_scaleC_zero_pointInput 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.axis must be in [-rank, rank) Dimension=Required attribute axis is missingAll inputs to Concat must have same rankCan't merge shape info. Both source and target dimension have values but they differ. Source=axis must be in [-rank, rank-1]. input rank was /onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.ccThe 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.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).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.Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointInput X's scale. It's a scalar, which means a per-tensor/layer quantization.Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output Y's scale. It's a scalar, which means a per-tensor/layer quantization.Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional 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.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.List of tensors/scale/zero_point for concatenationConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Scale of quantized input 'A'. It is a scalar,which means a per-tensor quantization.Zero point tensor for input 'A'. It is a scalar.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Its type is int32_t and must be quantized with zero_point = 0 and scale = alpha / beta * a_scale * b_scale.Scale of output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Zero point tensor for output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Constrain 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.ReduceSumIntegerAn input tensor.Reduced output tensor.reducedb_scaleb_zero_pointadditionmultiplicationQLinearReduceMeandata_scaledata_zero_pointreduced_scalereduced_zero_pointCoefficient of leakage.X_scaleX_zero_pointY_scaleY_zero_pointhidden_sizeclipactivationsinput_forgetsequence_lensinitial_hinitial_cY_hY_cWhich axis to concat onY's scale.T8Y's zero point.TVConcatenated tensor*ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE6_limitdelta1-D Tensor of the range.Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.Can not get shape initializer data!Unsupported non-raw-data data type!delta in Range operator can not be zero!Unsupported type:*ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]777777777ForThisAndAllSubgraphs/onnxruntime_src/onnxruntime/core/graph/graph.ccInputs: Nodes: ) -> Outputs: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&]Graph attribute inferencing failed: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]No Graph instance was found for attribute Mismatch between number of source and target dimensions. Source=ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()/onnxruntime_src/onnxruntime/core/graph/model_load_utils.hInput to set must exist.input->Exists()void onnxruntime::Graph::SetInputs(const std::vector&)TypeProto must have shape for this to runconst onnx::TensorShapeProto& onnxruntime::utils::GetShape(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.hconst T* onnxruntime::ConstPointerContainer::at(size_t) const [with Container = std::vector; onnxruntime::ConstPointerContainer::T = onnxruntime::NodeArg; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hindex < data_.size()] op_type [], could not find NodeArg fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [Serialization of fused function body is not currently supported, does not have the graph for key Node [This is an invalid model. The sum of input arg count is not equal to size of input defs in node (std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::experimental::fbs::EdgeEnd; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::experimental::fbs::EdgeEnd&]LoadEdgesFromOrtFormatinput index: input edgesoutput edges is not the same as this node's index:Invalid node indexes specified when adding edge.Invalid source node arg slot specified when adding edge.void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Graph*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Graph*&]ai.onnx.trainingai.onnx.preview.trainingstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]__builtin_expect(__first != __last, true)void std::pop_heap(_RandomAccessIterator, _RandomAccessIterator, _Compare) [with _RandomAccessIterator = __gnu_cxx::__normal_iterator >; _Compare = std::function]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_heap.hvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator]Some nodes are not included in the topological sort, graph have a cycle.void onnxruntime::Graph::KahnsTopologicalSort(const std::function&, const std::function&) const_token_Error: Duplicate definition-site for (SetOuterScopeNodeArgsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = gsl::not_null&]Graph state to be loaded into must be empty.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()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:) does not exist in the graph.This is an invalid model. Graph output (node_arg must be either specified in graph inputs or graph initializers.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (InitInputsInitializersOutputsstd::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; std::deque<_Tp, _Alloc>::reference = long unsigned int&]This is an invalid model. Error: the graph is not acyclic.Source and target must both be either tensors or sparse tensorsError merging shape info for output. '. Falling back to lenient merge.MergeShapeInfo' source: target:Output:Type mismatch. Current= Input=UpdateTypeAndShapeTensor element type mismatch. SparseTensor element type mismatch. This is an invalid model. Model input () does not have type information.Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer ' has element type does not match. but different TensorProto.existing->second == &tensorAddInitializedTensor already has tensor with name void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)This is an invalid model. Node () does not have type information set by parent node.This is an invalid model. Type Error: Type 'std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]) does not match expected type () input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types (InferAndVerifyTypeMatchNode () output arg () type inference failedType Error: Type () of output arg () of node (Node:) Op () UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&)node.GetAttributeNameToMutableSubgraphMap().empty() as it still has output edges.bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node BuildConnectionsInvalid model. Node input ' Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()nullptr != func_meta_defSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missinggsl::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 >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]graph_proto cannot be nullgraph_proto != nullptr' Model is invalid.utils::HasName(sparse_tensor). Please, fix your model.Initializer onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const std::vector&, const onnxruntime::logging::Logger&)Duplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidDuplicate sparse_tensor_initializer: 'Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializerThis 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.NoOponnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Function; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Function&]sparse_tensor_names_ not in sync with name_to_initial_tensor_void onnxruntime::Graph::RemoveInitializedTensor(const string&)sparse_tensor_names_.count(tensor_name) == 0graph_proto_ is not in sync with name_to_initial_tensor_.!foundstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]Duplicate initializer (dense or ConstantNode): 'Node index is out of rangeOuter scope node arg name ''was added but does not exist. void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) conststd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&]Function body initialization failed for node '. Execution will fail if ORT does not have a specialized kernel for this opInitFunctionBodyForNode' optype VerifyNodeAndOpMatchFatal error: ) attribute ( is not a registered function/op) is required but not specified.This is an invalid model. Error in Node:PerformTypeAndShapeInferencingSize mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TypeProto*&] inputs but subgraph has inputs and requires Subgraph input missing type.InferAndVerifySubgraphTypesResolveShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&)outer_scope_node_args_consumed.empty()InlineFunctionFailed to convert dense initializer to sparseonnx::GraphProto onnxruntime::Graph::ToGraphProto() constCleanUnusedInitializersRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node.FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEN4onnx15GraphInferencerEN4onnx24FunctionBodyBuildContextEN4onnx28FunctionBodyBuildContextImplEN11onnxruntime5GraphEN11onnxruntime19GraphInferencerImplEN11onnxruntime20InferenceContextImplEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_e/graph/graph.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]SaveInitializerOrtFormat/onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: O_xQpR0TUS`\`V`ZPMissing 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.LoadInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null 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.\llԙUNDEFINEDINTFLOATSINTSSTRINGSGRAPHSSPARSE_TENSORSPARSE_TENSORSFlatBuffers 1.12.0e/graph/graph_flatbuffers_utils.LoadInitializerOLoadSparseInitiaEmpty graph proto from deserialization of ORT fostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&]->Failed since multiple edges matched:itr != node_args.end()Attempting to get index by a name which does not exist:int onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const string&, bool)/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccAttempting to get an input that does not exist.const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)index >= 0 && static_cast(index) < inputs.size()Attempting to get an output that does not exist.const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)index >= 0 && static_cast(index) < outputs.size()void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) ImplicitInputs: ExplicitInputs:. Index:Invalid input index for node Implicit input name cannot be safely updated to in one of the subgraphs.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Can only add a new input at the end of the current ones.void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)num_explicit_inputs == static_cast(target_input_idx)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::graph_utils::GraphEdge&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper&]void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Node must only have one used outputbool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)Initializer with same name exists. Name:onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)!graph.GetInitializedTensor(new_initializer.name(), existing)e/graph/graph_utCanUpdateImplicitInputNameInSubgInvalid ExecutionOrderconst std::vector& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const/onnxruntime_src/onnxruntime/core/graph/graph_viewer.ccNot supported with filtered graph.const std::vector& onnxruntime::GraphViewer::GetRootNodes() constfilter_info_ == nullptrnodeargIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*)graph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: *ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_N11onnxruntime11NodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_N11onnxruntime19PriorityNodeCompareEtypename 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&]Protobuf parsing failed. is till opset ValidateOpsetForDomain 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 ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) 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 std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]/onnxruntime_src/onnxruntime/core/graph/model.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::FunctionProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::FunctionProto*&]ModelProto does not have a graph.onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unsupported model IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Missing model IR version., max supported IR version: No graph was found in the protobuf.std::__shared_ptr<_Tp, _Lp>::_SafeConv<_Yp> std::__shared_ptr<_Tp, _Lp>::reset(_Yp*) [with _Yp = onnxruntime::Model; _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; std::__shared_ptr<_Tp, _Lp>::_SafeConv<_Yp> = void]Failed to load model with error: __p == 0 || __p != _M_ptrGraph is null. Invalid ORT format model. less than 0. failed. File doesn't existLoad model system error number is less than 0.SaveProtobuf serialization failed.SaveModelSt15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEALLOW_RELEASED_Oe/graph/model.ccDomain already set in registrystd::deque<_Tp, _Alloc>::const_reference std::deque<_Tp, _Alloc>::operator[](std::deque<_Tp, _Alloc>::size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::const_reference = const std::shared_ptr&; std::deque<_Tp, _Alloc>::size_type = long unsigned int]RegisterOpSchemaInternal, but it its domain is not/onnxruntime_src/onnxruntime/core/graph/schema_registry.cc, but it its version is higherthan the operator set version RegisterOpSetN4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime27OnnxRuntimeOpSchemaRegistryEN11onnxruntime21SchemaRegistryManagerEe/graph/schema_rRegisterOpSchemaQWMWmemory_seq_lensAWCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. 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&]Not supportedvirtual onnxruntime::Graph& onnxruntime::ViewerFunctionImpl::MutableBody()/onnxruntime_src/onnxruntime/core/graph/function_impl.hvirtual const onnxruntime::Graph& onnxruntime::ViewerFunctionImpl::Body() constA node with a function body within a subgraph within another function body is currently not supported in ORTvoid onnxruntime::UpdateSubgraphsWithinFunctionBody(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map, int>&, const std::unordered_map, int>&)/onnxruntime_src/onnxruntime/core/graph/function.cc is out of bounds.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.input_arg->Type() != nullptrstd::unique_ptr onnxruntime::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&)No opset registered for domain void onnxruntime::IOTypeConstraintHelper(const onnx::FunctionProto&, std::unique_ptr&, const std::unordered_map, int>&, const std::unordered_map, int>&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair, std::basic_string >; _Alloc = std::allocator, std::basic_string > >; std::vector<_Tp, _Alloc>::reference = std::pair, std::basic_string >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] in function opset imports.domain_version != -1TinToutstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::SparseTensorProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::SparseTensorProto*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorProto*&]Cannot infer type and shape for function referenced by function body node . No opset import for domainfused_function_subgraphonnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::logging::Logger&)_dummyInitNestedModelLocalFunction'. Error message Resolve subgraph failed:Function body initialization failed for Function 'onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const std::unordered_map, const onnx::FunctionProto*>&, std::vector >&, const onnxruntime::logging::Logger&, bool)'s number of inputs is different from function body graph's number of input.node_in_parent_graph->InputDefs().size() == function_body_graph.GetInputsIncludingInitializers().size()'s number of outputs is different from function body graph's number of outputs.node_in_parent_graph->OutputDefs().size() == function_body_graph.GetOutputs().size()FSsRKSsS0_EN11onnxruntime8FunctionEN11onnxruntime12FunctionImplEN11onnxruntime18ViewerFunctionImplEN4onnx15shape_inference20InferenceContextImplEPFSsRKSsS0_E*ZN11onnxruntime12FunctionImplC4ERNS_5GraphERKmRKN4onnx13FunctionProtoERKSt13unordered_mapISsPS7_St4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEERSt6vectorISt10unique_ptrINS_8FunctionESt14default_deleteISO_EESaISR_EERKNS_7logging6LoggerEbEUlRNS5_16InferenceContextEE_?? @*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z13MlasGemmBatchRK27MLAS_GEMM_U8X8_SHAPE_PARAMSPK26MLAS_GEMM_U8X8_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_0Abad dimensions*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_?@@@@@@@p p!"H#8`AQ5@.N3;A8X} <2?~>++{16?:>=4?~??AQ5@.N3;A8X} <2?~>++{16?:>=4?~?AAAAQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} 2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;AAAAA33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KUB5?l?33{@7f9~;B;?@Kr1mL:e9 <*=*>>A ;E<# =p>u>33{@33{@33{@33{@l?l?l?l?A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>r1r1r1r1mL:mL:mL:mL:e9 *>*>*>>>>>rB¥B@K;?r1:n/ <*=(*>>??µµµrBrBrBrB::::n/ (*>(*>(*>38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSORElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!hmhnmmm/C*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_DGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_    *Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_Failed to initialize CPU info.onnxruntime::CPUIDInfo::CPUIDInfo()/onnxruntime_src/onnxruntime/core/common/cpuid_info.ccDefault logger already set. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&)/onnxruntime_src/onnxruntime/core/common/logging/logging.ccISink must be provided.onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int)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.SystemonnxruntimeN@Failed to parse path root: Parse/onnxruntime_src/onnxruntime/core/common/path.ccstatic onnxruntime::Path onnxruntime::Path::Parse(const PathString&)/\e/common/path.ccWriting profiler data to file [ ] {"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : } }, onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() const/onnxruntime_src/onnxruntime/core/common/profiler.ccenabled_void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)session_logger != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::profiling::EventRecord; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::profiling::EventRecord&]Maximum number of events reached, could not record profile event.e/common/profileEndTimeAndRecordonnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&)/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)SystemError[ONNXRuntimeError]GENERAL ERRORSUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAIL8z799@9*9989l9V99typename 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&]UnknownEventDistributionWaitWaitRevokeDistributionEnqueue^t^^^^std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]unnamed_thread_poolstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >; std::vector<_Tp, _Alloc>::reference = std::chrono::time_point > >&]"num_run": thread::id of a non-executing threadLogStart must pair with LogEndvoid onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)/onnxruntime_src/onnxruntime/core/common/threadpool.cc!points_.empty()void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = std::pair; _Alloc = std::allocator >]void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()"thread_id": "", "block_size": [], "core": Profiler not started yet{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()Nested parallelism not supportedonnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*)!current_parallel_sectionvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function&)n >= 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]More 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_src/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.hvoid onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(onnxruntime::concurrency::ThreadPoolParallelSection&, std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjlEUljE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE21RunInParallelInternalERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjbSt8functionIFvjEEEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE26ScheduleOnPreferredWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionERSt6vectorIiSaIiEEjjSt8functionIFvjEEEUlvE_N5Eigen19ThreadPoolInterfaceEN11onnxruntime11concurrency27ExtendedThreadPoolInterfaceEN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEE*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_*ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_?j@?A@{Gz?N11onnxruntime7logging8CLogSinkE\/Failed to get symbol with error: lib.soFailed to load library Got null library handleFailed to unload library with error: file "" failed: closeopen error msg: pthread_attr_init failed, error code: onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&)/onnxruntime_src/onnxruntime/core/platform/posix/env.ccpthread_attr_setstacksize failed, error code: pthread_create failed, error code: pthread_setaffinity_np failed, error code: Failed to close file descriptor - error code: munmap failed. error code: remove() failed. Error code: , path: DeleteFolder(): nftw() failed with error: Invalid fd was supplied: fstatfile_path == nullptroffset < 0mmaplength > buffer.size()lseekreadFile: , offset: , length: ReadFileIntoBuffer - unexpected end of file. realpathN11onnxruntime9EnvThreadEN11onnxruntime3EnvE*N11onnxruntime12_GLOBAL__N_111PosixThreadE*N11onnxruntime12_GLOBAL__N_18PosixEnvEe/platform/posixReceived negative size from statMapFileIntoMemorReadFileIntoBuffN11onnxruntime9TelemetryE is not a valid year is not a valid day is not a valid dateVIWEFN11onnxruntime7logging11OStreamSinkEN11onnxruntime7EnvTimeE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeESaveTensorTypeAndShapeOrtFormat/onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.ccSaveTypeInfoOrtFormatSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatWe do not support type [] for nowSaveValueInfoOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for Model must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.dim_param value with no name. Invalid ORT format model.LoadTensorShapeOrtFormatLoadTypeInfoOrtFormatLoadSequenceTypeOrtFormatLoadMapTypeOrtFormat is not supported currentlyNull tensor type info. Invalid ORT format model.LoadTensorTypeAndShapeOrtFormatNull sequence type info. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.Null map type info. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.Null type info for LoadValueInfoOrtFormate/flatbuffers/flLoadOpsetImportOLoadTensorDimensLoadTypeInfoOrtFLoadSequenceTypeLoadMapTypeOrtFoLoadValueInfoOrtSparse tensor indices (Sparse tensor () index value at position [] out of range.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.) has values, but NNZ is ] out of range [0, ] not in sorted order.Field 'name' of value_info is required to be non-empty. is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: aTW]aTWaTW ]\aTW\0]P]data_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_data) should be stored in TensorProto ( tensor name: TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'setting data_type field (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field., but it doesn't exist or is not accessible.) is stored externally but doesn't have a location.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: _W`_____Ha`a__a0a0a`a_sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.) dimensions are not positive.ATenConstantFillop_typeNodeProto (name: , type: No opset import for domain 'No Op registered for with domain_version of ) has zero input and zero output.Warning: Checker does not support models with experimental ops: is deprecated in domain_version of init' of node: OpType: ==> Context: Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.Tensor 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 ' is not output of any previous nodes.' has been used as output names multiple times.Bad node spec for node. Name: attrAttribute (name: type field and data field mismatch in attribute ) should not contain more than one value field.) should refer to attribute in parent node.N4onnx7checker15ValidationErrorEIf node has but subgraphs produce then_branch and else_branch produce different number of outputs. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TypeProto*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Input element type of Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank axis value Input was expected to have sequence type. Got Element type of input was unknownInput was expected to have either tensor or sequence type. Got num_scan_inputsscan_input_axesscan_output_axesbody was not a tensor.Scan input outputs. Expected was notNumber of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Graph attribute inferencing returned type information for Scan '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 Condition for the ifAll Tensor and Sequence typesOnly boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.ccv_initialv_final_and_scan_outputsA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].initial_state_and_scan_inputsfinal_state_and_scan_outputsscan_input_directionsscan_output_directionsInt64 tensorAll Tensor types then=Mismatched type for output else=Mismatched tensor element type for output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors)./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.ccFinal N loop carried dependency values then K scan_outputsAn 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.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Loop 'body' subgraph outputs should all be tensors but output Invalid data type Invalid tensor data type seq(optional(map(sparse_tensor(Unsuported type proto value case.جWTrجWجWs::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::NodeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::NodeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] not specifiedValue of attribute should be of integer type and specify a type. does not specify a valid type. expected to have: or UNDEFINED. Got: Invalid shape value: Attribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorShape input must be a one-dimensional tensor.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]dtypesparse_valuevalue_intvalue_intsvalue_stringvalue_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics.Constrain input types.(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'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionhighThe 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.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.sample_size(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors.The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.All values in input have to be in the range:[0, 1].The returned output tensor only has values 0 or 1, same shape as input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliAll inputs to 'Range' op must be of the same typeInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2loop_body_attributecond_outsub_resultsub_result_casteddelta_casteddiv_resultceil_resultceil_result_reluceil_result_relu_intceil_result_relu_boolvariadic_outputScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.Scalar. Value to step by.X_greaterX_random*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_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./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_tensor(complex64tensor(complex12Constrain input and output types to all tensor tResult tensor.First input operand for the logical operator.Second input operand for the logical operator.Constrains input to boolean tensor.Constrains output to boolean tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.ccxorlessConstrains input types to all numeric tensors.Constrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).less_equalO1O2greater_equal*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Constrains input types to all nuEnable broadcastingbroadcastIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to float tensors.Constrains input to integral tensors.*ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_The input tensor of rank >= axis.The output values with the same shape as the input tensor.Rank of input Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)List of tensors for Output tensor.'axis' must be in []. Its actual value is: Weight rank must be 1.reductionnoneTarget rank must be 1 less than the input rank.Input and target dimension value mismatch.Invalid rank for broadcasting: () vs (Wrong op_type name for running propagation: 'shape' input must be 1D tensor of type INT64Invalid value for attribute axisK 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./onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.ccsubtractionWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensordivisionConstrain input and output types to signed numeric 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).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XOutput tensor (same size as X)slopeValue of alpha.Value of beta.Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsSoftmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) normalized exponentialLogSoftmax(input, axis) = Log(Softmax(input, axis=axis))log of softmaxHardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwisehardmaxThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseOptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Tensor of shape [a_1, a_2, ..., a_n, r]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain input and output types to numeric tensors.Constrain index tensor to int64Dimension 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.sortedThe sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.N-dimensional quantized matrix azero point of quantized input aN-dimensional quantized matrix bzero point of quantized input bzero point of quantized output yQuantized matrix multiply results from a * bConstrain 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.scale of quantized input ascale of quantized input bscale of quantized output yZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsConstrain input and output types to high-precision numeric tensors.axis tensor can be int32 or int64 onlyexclusiveConstrain input and output types to floating-point tensors.Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typeslossNegativeLogLikelihoodLossEinsum expression string.equationOperandsConstrain input and output types to all numerical tensor types.The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).labelslog_probSoftmaxCrossEntropyLossconst_zero_floatconst_zero_castedconst_one_floatconst_one_castedconst_zeroconst_oneexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_ignore_indexconst_zero_target_typedexpanded_target_int64transform_targetssqueeze_maskweight_gather_tempweight_gather_temp_1input_gather_element_transformHS_XHardSwishX_alphaElu_ResultX_ReduceMaxX_SubX_ExpX_ReduceSumX_LogShape3DX_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeinput and zero_point pair is expected to have same type.FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output. to all numeric to high-precision numeric tenso to numeric tensPass 1 to enable broadcastingconsumed_inputslegacy optimization attribute.If set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as ADescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeThe input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).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)./onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cclogsoftmax1 for the first maximum value, and 0 for all others 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. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.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 1.0.1-D input tensorSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.data_0Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CNumber of top elements to retrieveDimension on which to do the sort.Invalid value for attribute k*ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ 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. to all tensors.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasti 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 elementwisp value of the Lp norm used to pool over the input data.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).pooled_shapespatial_scaleConstrain input and output types to float and 8 bit tensors.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the convolution kernel. If not present, should be inferred from input W.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.number of groups input channels and output channels are divided into.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.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.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionInput shape must have either [C] or [1,C] dimensions where C > 0ngram_indexesngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 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.RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero)./onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.ccaverageThe output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).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.Output data tensor that contains the result of the unpooling.a filterScale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain 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.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)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.lp poolThis 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. 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 data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.momentumScale tensor of shape (C).Bias tensor of shape (C).input_meaninput_varrunning_meanrunning_varThe input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.The output mask.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.lambdA tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.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 overOutput tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Scaling parameter.The exponent.Input for n-gram extractionNgram results1-D tensor of floatsmax_gram_lengthmin_gram_lengthmax_skip_countpool_stringspool_int64sngram_countsInput is ether string UTF-8 or int32/int64Maximum 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)UTF-8 strings to normalizeUTF-8 Normalized stringscase_change_actionis_case_sensitivestopwordslocalestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.The pads attribute cannot be used simultaneously with auto_pad attributeInvalid value() for attribute 'axis'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.ExponentX_RMEX_squaredE_XsquaredVarianceX_varianceProcessed_STDA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_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.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Stride along each spatial axis.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter./onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.ccThe output of each pooling window is divided by the number of elements exclude pad.The output of each pooling window is maximum number of elements exclude pad.Dilation value along each spatial axis of filter.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If 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.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.is_testsaved_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. The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation. 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.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as 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.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_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.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.ccoutput_heightoutput_widthsampling_ratioAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer 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.boxesmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indicescenter_point_box*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_/onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_/onnxruntime_src/cmake/external/onnx/onnx/defs/optional/defs.cc*ZN4onnx11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_seq(tensor(uint8seq(tensor(uint1seq(tensor(uint3seq(tensor(uint6seq(tensor(int8)seq(tensor(int16seq(tensor(int32seq(tensor(int64seq(tensor(floatseq(tensor(doublseq(tensor(strinseq(tensor(bool)seq(tensor(comploptional(seq(tensor(complex64)))sor(complex128))optional(tensor( type to all tensor and sequenceConstrains output type to all optional tensor or optional sequen type to optional tensor and optional sequence tConstrain outputsor or sequence Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Quantized output tensorQ_MinQ_MaxX_MinX_Min_AdjustedX_MaxX_Max_AdjustedX_RangeMin_ScaledInitial_ZeroPoint_FPClipped_ZeroPoint_FPRounded_ZeroPoint_FPZeropointOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_32DynamicQuantizeLinear_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified./onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/old.ccScale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.select_last_index'axis' must be in [-rank(indices), rank(indices)-1]Constrain input and output types to high-precision and 8 bit numeric tensors.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).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).noop_with_empty_axesaxes as an input and attribute cannot be specified at the same time./onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.ccsum squareproductlog sumlog sum exponentL1 normL2 norm*ZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices./onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc*ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_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].First input tensor must have rank 3The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0./onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0linear_before_resetThe 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 = [num_directions, batch_size, hidden_size].*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if nOptional 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.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.output_sequence/onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_Attributes:(unnamed)(no doc)(no type)Outputs:(no documentation yet)Defined at std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] typestr: , has unsupported type: has inconsistent type has unsupported type Operator ') has input size not in range [min=, max= not in allowed input sizes.) has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute 'Unrecognized attribute: for operator has unknown expected typeRequired attribute '' is missing.' has been deprecated since version is marked single but has an empty string in the graphMismatched 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'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs'' is expected to have field 'type_protos'iXt$t$t$4(((''''l't$S''ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()): failed validating the check: Duplicate type constraint nameAttribute specification type mismatch.N4onnx16OpSchemaRegistryE*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input type for input at index 0 is null. Type info is expected.new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Attribute dtype should be of integer type and specify a type. sum of split values=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 not equal to 'input' dim size on 'axis'. 'axis' dim size=Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=(Optional) The data type of the tensors in the output sequence. The default type is 'float'./onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.ccEmpty sequence.Tensors.Sequence enclosing the input tensors.Input sequence.input_sequencepositionInput tensor to be inserted into the input sequence.Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).The tensor to splitLength of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.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.Sequence of tensors for concatenationnew_axisconcat_result*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ types to any tetypes to any tenConstrain to anytypes to all ten types to all teMismatched type: source= target=Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type.target sequence type missing element type.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsaxis must be in [-rank, rank-1].'step' cannot be 0 for SliceInput rank for starts and ends should be the same: (The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.ccInput tensor to be cast.The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.Output tensor produced by casting the first input tensor to have the same type as the second input tensor.target_typeCastLike(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.allowzeroSpecified shape for output.Reshaped data.reshapedShape of the input tensor(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(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.Input tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationConstrain output types to any tensor type.Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.One or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.stepsA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Transposed output.transposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.updatesTensor of rank q + r - indices_shape[-1] - 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.Tensors with at least max(dims) dimensions.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).Reshaped tensor with same data as input.squeezedOriginal tensorexpandedList 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).Blocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].blocksizeDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Input tensor of any shape.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.N-D tensorN-D tensor after resizingTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)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'.Constrain input 'X' and output 'Y' to all tensor types.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, 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".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.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain roi type to float or double.cubic_coeff_aexclude_outsideroiTensor to copy input into.Constrain input and output types to all tensor and sequence 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).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.condition(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.depthConstrain to any tensor type.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) 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_positivedetect_negativeConstrain to boolean tensors.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.batch_axisTensor of rank r >= 2.(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).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'inverse_indicesThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.batch_dimsconstant_valueSupported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.OneHot 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.Indices tensor must have rank >= 1Target shape may not have multiple -1 dimensions.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesInvalid position of 0.Invalid dimension value: Invalid attribute perm {}, input shape = { and Output does not match type of output: expected to have tensor or sparse tensor typedata tensor must have rank >= 1axis must be in [-r, r-1]'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape'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.'input' must have rank >= 2'sequence_lens' must have rank of 1Both `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` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Mismatch between number of splits (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittable) and outputs (Blocksize 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 lengthInput axes has invalid data*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_/onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cclength of each output. Values should be >= 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_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).The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrains input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0paddingsNew shapeNumber of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.tilesAxis along which to repeat.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.width_scaleheight_scale4-D tensor, [N,C,H,W]The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. 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`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.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.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Target shape may not have multiple -1 dimensionsInvalid Target shape product of 0Invalid position of 0rank must be greater than axisRanks inferred () is not equal to the existing rank value (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 length'axis' must be in [-rank(indices)-1, rank(indices)]'step' cannot be 0*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_) is not equal to the existing dim value (Dimension value inferred (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.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.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe 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.map(int64, string)map(int64, float)cast_tomap_formmax_mapThe input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input datacats_stringscats_int64sdefault_int64Output data. If strings are input, the output values are integers, and vice versa.The 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' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)string_vocabularyint64_vocabularyA 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The 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.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listinputdimensionsData to be processed.Imputed output dataValue(s) to change toimputed_value_floatsA value that needs replacing.replaced_value_floatValue(s) to change to.imputed_value_int64sreplaced_value_int64The 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.Input data. It can be either tensor or scalar.The 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 strings. One and only one of 'value_*'s should be set.Output data.A list of ints.A list of floats.A string.An integer.A float.default_floatData to be classified.coefficientsA collection of intercepts.interceptsmulti_classclasslabels_intsClassification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Weights of the model(s).Regression outputs (one per target, per example).The 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 intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List 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.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.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 the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.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.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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.kernel_typekernel_paramsvectors_per_classsupport_vectorsprob_aprob_brhoRegression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Chosen support vectorsone_classSupport vector coefficients.n_supportsInput of shape [N,F]N, Top class for each pointTree id for each node.Feature id for each node.The class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'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 defalt 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.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesThe node id of each weightThe weight for each targetThe total number of targets.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(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.Input's shape should be 1D or 2D*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.A list of labels.The 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./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_type case unsupported for symbolic shape inference. inferred=, node name: (op_type:sequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SETTXXgXgXXXgX.XAXXInferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension type case mismatch. existing= inferred=type case unsupported. existing=Data for input already exists.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorProto*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator.Shape inference error(s): inputs but Graph has were providedFvRN4onnx22DataPropagationContextEEN4onnx22DataPropagationContextEN4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference26DataPropagationContextImplEPFvRN4onnx22DataPropagationContextEEZZZȦZئ0Q8QQQ`x8X 0 @ 7x5(54h4476X7@66x63282,KJ,K,KJtLK,L::max() - SerialArena::kBlockHeaderSize): N6google8protobuf8internal19ImplicitWeakMessageE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.ccCan't BackUp() after failed Next().CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19ZeroCopyInputStreamEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io19OstreamOutputStreamE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: (buffer_used_) == (buffer_size_): 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().CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): N6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io26CopyingOutputStreamAdaptorE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Can't message of type "" because it is missing required fields: N6google8protobuf11MessageLiteE(cannot determine missing fields for lite messag$$P$@$$$/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happen472L10\6L$OPdRDMM[libprotobuf %s %s:%d] %s %lld%luN6google8protobuf14FatalExceptionEINFOWARNINGFATAL/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamE/onnxruntime_src/cmake/external/re2/re2/re2.ccUnknown encoding Invalid RE2: startpos: endpos: text size: Unexpected re_anchor value: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRE2: invalid startpos, endpos pair. [Error parsing '': Error compiling 'pattern too large - compile failedError reverse compiling 'std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_stack.hunexpected error/onnxruntime_src/cmake/external/re2/re2/regexp.ccCaptureNamesWalker::ShortVisit calledNumCapturesWalker::ShortVisit called/onnxruntime_src/cmake/external/re2/re2/walker-inl.hStack not empty.Regexp not destroyed.Bad reference count Walk NULLstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]Unexpected op in Regexp::Equal: 3%##$L$<$<$$$$$$$#$#######%$$std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Regexp*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::RuneRange; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::RuneRange&]N3re26Regexp6WalkerIiEEN3re217NumCapturesWalkerEno errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \invalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupno argument for repetition operator@B/onnxruntime_src/cmake/external/re2/re2/bitstate.cctypename 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]GrowStack() failed: njob_ = job_.size() = 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]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]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]Unexpected opcode: HlH HGlGFLF Fstd::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]!empty()std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::front() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char]std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char]/onnxruntime_src/cmake/external/re2/re2/compile.ccCompiler::Copy called!void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]should never happenstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]No ranges in char classMissing case in Compiler: بآȣ8hȥب`Ф`8 N3re26Regexp6WalkerINS_4FragEEEN3re28CompilerEunhandled opcode: typename 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]/onnxruntime_src/cmake/external/re2/re2/dfa.cc +tDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnBytecontext does not contain textFailed to analyze start state.StateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Resetunhandled in Prog::Fanout()/onnxruntime_src/cmake/external/re2/re2/nfa.cc in AddToThreadqstd::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::NFA::Thread; _Alloc = std::allocator; std::deque<_Tp, _Alloc>::reference = re2::NFA::Thread&]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]::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Splice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Splice&]/onnxruntime_src/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledBad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.RE2: unexpected op: AnyBad hex digit /TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTNTTTTTTTTTTT TTTTTTConcat of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Frame&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Splice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Splice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = re2::Frame; _Alloc = std::allocator]unknown round: PPP0@PPPPPPPPPPPPPPPPPP8PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPN3re216RepetitionWalkerE[: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:]\d\D\s\S\w\W09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az 09std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]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>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&]GFFFFF::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::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]/onnxruntime_src/cmake/external/re2/re2/simplify.ccMalformed repeat CoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is x0hh~hxxhhhhhhxCase not handled in ComputeSimple: ܕܕܕ6lDSimplify case not handled: sPPPPММhPPPPPPPPȝPN3re26Regexp6WalkerIPS0_EEN3re214CoalesceWalkerEN3re214SimplifyWalkerE[]^-\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\basic_string::_M_replace_aux(?:?P<kRegexpCapture cap() == 0/onnxruntime_src/cmake/external/re2/re2/tostring.cc[^\x00-\x{10ffff}](?-m:^)(?-m:$)(?:)basic_string::eraseBad final char: ?{%d,}{%d}{%d,%d}\C^$\z\b\B(?HaveMatch:%d)|8+88(P8 [truncated]N3re214ToStringWalkerEfAZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&C&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$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~Z0ZZZZZZZZ§çħħŧŧZƧƧuǧʧSS`p0h!: AZ'((O(  @   @n_n `nn!""CAdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeChorasmianCoCopticCsCuneiformCypriotCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNabataeanNandinagariNdNew_Tai_LueNewaNkoNlNoNushuNyiakeng_Puachue_HmongOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWanchoWarang_CitiYezidiYiZanabazar_SquareZlZpZsGƤ+0-g-o-p---GIlq:@[    ( * 9 = D F H J M U V X Z ` c f o w  oop ª۪ߪ ^`|Pmpt`lnprs  JMO`j,P0Y PŨΨ٨-0>0S__          @w`kEkPkYk[kakckwk}kk      ( * 0 2 3 5 9 < D G H K M U W \ ] _ c f w H `  'Pz  #-/      P,0=@INOoop[]a`@j^j`jijnjoj`l xDPYoJoOoooo        @nn  6::<=?GPYp    @[^^    D F H J O T c f  Pv 9 ? ? 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from /proc/cpuinfofailed to allocate %zu bytes for descriptions of %u logical processorsfailed to allocate %zu bytes for mapping entries of %u logical processorsfailed to allocate %zu bytes for mapping entries of %u coresfailed 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 physical packagesfailed 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 cachesfailed to allocate %zu bytes for descriptions of %u L4 caches/proc/cpuinfo/sys/devices/system/cpu/kernel_maxfailed to parse file %s: file is emptyfailed to parse file %s: "%.*s" is not an unsigned 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B hT Q+H84'EC DRq A [ E q MH'A.Q.Ph p7'BDB B(A0A8JJ 8A0A(B BBBF POH#'< Px 0P LxFAL U ABE DEh x 'FBB B(A0F8IC 8A0A(B BBBG  8A0A(B BBBE ( RH<&0 }FAL q ABI pDELԗ ~,&BDB B(A0D8GH 8A0A(B BBBH d*SHpW& X PP TP J&FBB B(A0A8O 8A0A(B BBBI  TH"&L 8^&BDB B(D0A8GH 8A0A(B BBBH + RYH& <8 ܎2&&FBA F(G` (A ABBK i ZH%D ďO&FAF I0A  AABE   DABE  \H%h %FBB B(A0F8G`7 8A0A(B BBBE  8E0A(B BBBN x \Hu%Lt n%FBB B(A0C8LB 8A0A(B BBBH d \H3%Lܚ x?%BDB B(A0D8GH 8A0A(B BBBH . ]H% 8X %FAL0 DBH T ABI  ^_H$0 (FAL U ABE DE0H ĜFAL U ABE DE8 `md$FAJ@r ABJ  DBO P_H1$Lh |/$BBB B(A0A8Gw 8D0A(B BBBK <_Ho$8 L 'k$BBB B(A0D8Id 8A0A(B BBBD taHhH$ <` |$FBD F(Gp (D ABBE < O$FBD F(Gp (D ABBE , D"$EKIP~ AAG \c eH$L( #BDB B(A0D8G 8A0A(B BBBI  DeHs# 4 L 0#BDB B(A0D8G 8A0A(B BBBI   H  lHf8\ h fEC HKO. P.> H D 0lHf4 "EC DKK. . A IFlH)8 4$fEC HKO. P.= I  lHfw8T P&gEC HK|. .= E < -lHfQ8 (1AEC HD. .M E  ?lH{00 + EC BDm. N. A nlH4H ,.,EC DKl. N.= A K.lH)0 1EC BDm. .q A 8 lH8 P2EC HKs. P.= E ̏ kHfj08 4ZEC BDL. . A kH44 6$$EC FK. . 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