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k]HHK8HunHH@nHL$ HL$ m]HHK8HurnHH@nHL$ HL$ o]HHK8Hu=nHH@nHL$ dHL$ q]HHK8HunHH@JnHL$ /HL$ ZHHK8HuӅnHH@nHL$ HL$ PK_HHK8HunHH@nHL$ HL$ +bHHK8HuinHH@nHL$ HL$ v(aHHK8Hu4nHH@vnHL$ [HL$ abHHK8HunHH@AnHL$ &HL$ 蜺^HHK8HuʄnHH@ nHL$ HL$ _HHK8HunHH@׏nHL$ HL$ bL_HHK8Hu`nHH@nHL$ HL$ -[HHK8Hu+nHH@mnHL$ RHL$ I`HHK8HunHH@8nHL$ HK8HtHH;H@ nHC8H$HĀ]H\$HL$UH$HHHL$ y4\HHK8HugnHH@nHL$ HL$ |_HHK8Hu2nHH@tnHL$ YHL$ [HHK8HunHH@?nHL$ $HL$ cHHK8HuȂnHH@ nHL$ HL$ |`HHK8HunHH@ՍnHL$ HL$ aHHK8Hu^nHH@nHL$ HL$ v`HHK8Hu)nHH@knHL$ PHL$ 膊_HHK8HunHH@6nHL$ HL$ \HHK8HunHH@nHL$ HL$ `HHK8HunHH@̌nHL$ HL$ z]HHK8HuUnHH@nHL$ |HL$ 2t]HHK8Hu nHH@bnHL$ GHL$ ZHHK8HunHH@-nHL$ HL$ ZHHK8HunHH@nHL$ HL$ #ncHHK8HunHH@ËnHL$ HL$ pcHHK8HuLnHH@nHL$ sHL$ )W\HHK8HunHH@YnHL$ >HL$ 4jcHHK8HunHH@$nHL$ HL$ b\HHK8HunHH@nHL$ HL$ ecHHK8HuxnHH@nHL$ HK8HtHH;H@ nHC8H$HĀ]H\$HL$UH$ HHHL$ aHHK8Hu~nHH@)nHL$ HL$ 4i[HHK8Hu~nHH@nHL$ ٿHL$ d\HHK8Hu}~nHH@nHL$ 褿HL$ JbHHK8HuH~nHH@nHL$ oHL$ U}]HHK8Hu~nHH@UnHL$ :HL$ `I]HHK8Hu}nHH@ nHL$ HL$ +_HHK8Hu}nHH@nHL$ оHL$ &5cHHK8Hut}nHH@nHL$ 蛾HL$ q[cHHK8Hu?}nHH@nHL$ fHL$ TaHHK8Hu }nHH@LnHL$ 1HL$ g_HHK8Hu|nHH@nHL$ HL$ _HHK8Hu|nHH@nHL$ ǽHL$ [HHK8Huk|nHH@nHL$ 蒽HL$ cHHK8Hu6|nHH@xnHL$ ]HL$ s6_HHK8Hu|nHH@CnHL$ (HL$ ^!`HHK8Hu{nHH@nHL$ HL$ }]HHK8Hu{nHH@نnHL$ 込HL$ 4_HHK8Hub{nHH@nHL$ 艼HL$ Ͽ_HHK8Hu-{nHH@onHL$ THL$ *_HHK8HuznHH@:nHL$ HL$ F\HHK8HuznHH@nHL$ HL$ ^HHK8HuznHH@ЅnHL$ 赻HL$ _HHK8HuYznHH@nHL$ 耻HL$ _HHK8Hu$znHH@fnHL$ KHL$ Q:`HHK8HuynHH@1nHL$ HL$ 茖aHHK8HuynHH@nHL$ HL$ הaHHK8HuynHH@DŽnHL$ 謺HL$ haHHK8HuPynHH@nHL$ wHL$ iaHHK8HuynHH@]nHL$ BHL$ eaHHK8HuxnHH@(nHL$ HL$ cfaHHK8HuxnHH@nHL$ عHL$ aHHK8Hu|xnHH@nHL$ 裹HL$ yaaHHK8HuGxnHH@nHL$ nHL$ 4aHHK8HuxnHH@TnHL$ 9HL$ baHHK8HuwnHH@nHL$ HL$ ]aHHK8HuwnHH@nHL$ ϸHL$ %_aHHK8HuswnHH@nHL$ 蚸HL$ P>\HHK8Hu>wnHH@nHL$ eHL$ ^HHK8Hu wnHH@KnHL$ 0HL$ ƻaHHK8HuvnHH@nHL$ HL$ aHHK8HuvnHH@nHL$ ƷHL$ \aHHK8HujvnHH@nHL$ 葷HL$ G@aHHK8Hu5vnHH@wnHL$ \HL$ B^HHK8HuvnHH@BnHL$ 'HL$ -ZHHK8HuunHH@ nHL$ HL$ q`HHK8HuunHH@؀nHL$ 轶HL$ #ZHHK8HuaunHH@nHL$ 舶HL$ `HHK8Hu,unHH@nnHL$ SHL$ ɮZHHK8HutnHH@9nHL$ HL$ ZHHK8HutnHH@nHL$ HL$ ]HHK8HutnHH@nHL$ 贵HL$ *]HHK8HuXtnHH@nHL$ H3]HHK8Hu!tnHH@cnHFHL$ ,]HHK8HusnHH@,nHL$ H]HHK8HusnHH@~nHشHL$ ]HHK8Hu|snHH@~nHL$ 裴H]HHK8HuEsnHH@~nHjHL$ ^HHK8HusnHH@P~nHL$ 5HI@_HHK8HurnHH@~nHHL$ aHHK8HurnHH@}nHL$ dzHk)aHHK8HuirnHH@}nH莳HL$ $_HHK8Hu2rnHH@t}nHL$ YHK8HtHH;H@ L}nHC8H$H]H\$HL$UH$HHHL$ )faHHK8HuqnHH@|nHL$ βHL$ tgaHHK8HurqnHH@|nHL$ 虲HL$ /aHHK8Hu=qnHH@|nHL$ dHL$ ]HHK8HuqnHH@J|nHL$ /HL$ ՑZHHK8HupnHH@|nHL$ HL$ SaHHK8HupnHH@{nHL$ űHL$ UaHHK8HuipnHH@{nHL$ 萱HL$ :_HHK8Hu4pnHH@v{nHL$ [HL$ jbHHK8HuonHH@A{nHL$ &HL$ ,`HHK8HuonHH@ {nHL$ HL$ EcHHK8HuonHH@znHL$ 輰HL$ r\HHK8Hu`onHH@znHL$ 臰HL$ aHHK8Hu+onHH@mznHL$ RHL$ aHHK8HunnHH@8znHL$ HL$ 3;cHHK8HunnHH@znHL$ HL$ =cHHK8HunnHH@ynHL$ 賯HL$ 9bHHK8HuWnnHH@ynHL$ ~HL$ aHHK8Hu"nnHH@dynHL$ IHK8HtHH;H@ inHH@tnHL$ eHL$ aHHK8Hu inHH@KtnHL$ 0HL$ \HHK8HuhnHH@tnHL$ HL$ q]HHK8HuhnHH@snHL$ ƩHL$ ]HHK8HujhnHH@snHL$ 葩HL$ ]HHK8Hu5hnHH@wsnHL$ \HL$ R ]HHK8HuhnHH@BsnHL$ 'HL$ ]HHK8HugnHH@ snHL$ HL$ #]HHK8HugnHH@rnHL$ 轨HL$ c0]HHK8HuagnHH@rnHL$ 舨HL$ NG]HHK8Hu,gnHH@nrnHL$ SHL$ T]HHK8HufnHH@9rnHL$ HL$ 4W]HHK8HufnHH@rnHL$ HL$ O]HHK8HufnHH@qnHL$ 货HL$ y]HHK8HuXfnHH@qnHL$ HL$ EZHHK8Hu#fnHH@eqnHL$ JHL$ 0ZHHK8HuenHH@0qnHL$ HL$ ZHHK8HuenHH@pnHL$ HL$ ZHHK8HuenHH@pnHL$ 諦HL$ 衃aHHK8HuOenHH@pnHL$ vHL$ aHHK8HuenHH@\pnHL$ AHL$ wg[HHK8HudnHH@'pnHL$ HL$ "aHHK8HudnHH@onHL$ ץHL$ }aHHK8Hu{dnHH@onHL$ 袥HL$ ؈aHHK8HuFdnHH@onHL$ mHL$ 3aHHK8HudnHH@SonHL$ 8HL$ 莋aHHK8HucnHH@onHL$ HL$ aHHK8HucnHH@nnHL$ ΤHL$ DaHHK8HurcnHH@nnHL$ 虤HL$ w[HHK8Hu=cnHH@nnHL$ dHL$ *y[HHK8HucnHH@JnnHL$ /H3[HHK8HubnHH@nnHHL$ ܛ^HHK8HubnHH@mnHL$ H蕨^HHK8HucbnHH@mnH舣HL$ ~[HHK8Hu,bnHH@nmnHL$ SHG[HHK8HuanHH@7mnHHL$ ^HHK8HuanHH@mnHL$ H [HHK8HuanHH@lnH謢HL$ r[HHK8HuPanHH@lnHL$ wH^HHK8HuanHH@[lnH>HL$ t^HHK8Hu`nHH@$lnHL$ H[HHK8Hu`nHH@knHСHL$ [HHK8Hut`nHH@knHL$ 蛡Ho[HHK8Hu=`nHH@knHbHL$ [HHK8Hu`nHH@HknHL$ -H[HHK8Hu_nHH@knHHL$ \HHK8Hu_nHH@jnHL$ 迠H3 \HHK8Hua_nHH@jnH膠HL$ _HHK8Hu*_nHH@ljnHL$ QHe_HHK8Hu^nHH@5jnHHL$ n3\HHK8Hu^nHH@inHL$ H>\HHK8Hu^nHH@inH誟HL$ M\HHK8HuN^nHH@inHL$ uH蹱_HHK8Hu^nHH@YinHRHHK8HutGnHH@RnHL$ 蛈HL$ "RHHK8Hu?GnHH@RnHL$ fHL$ <%RHHK8Hu GnHH@LRnHL$ 1HL$ GRHHK8HuFnHH@RnHL$ HL$ QRHHK8HuFnHH@QnHL$ LJHL$ -(RHHK8HukFnHH@QnHL$ 蒇HL$ CRHHK8Hu6FnHH@xQnHL$ ]HL$ 3JRHHK8HuFnHH@CQnHL$ (HL$ .2RHHK8HuEnHH@QnHL$ HL$ (RHHK8HuEnHH@PnHL$ 辆HL$ *RHHK8HubEnHH@PnHL$ 艆HL$ 4RHHK8Hu-EnHH@oPnHL$ THL$ J7RHHK8HuDnHH@:PnHL$ HL$ u RHHK8HuDnHH@PnHL$ HL$ RHHK8HuDnHH@OnHL$ 赅HL$ RHHK8HuYDnHH@OnHL$ 者HL$ gRHHK8Hu$DnHH@fOnHL$ KHL$ vRHHK8HuCnHH@1OnHL$ HL$ qRHHK8HuCnHH@NnHL$ HL$ 'dRHHK8HuCnHH@NnHL$ 謄HL$ "aRHHK8HuPCnHH@NnHL$ wHL$ }jRHHK8HuCnHH@]NnHL$ BHL$ wRHHK8HuBnHH@(NnHL$ HL$ 裮PHHK8HuBnHH@MnHL$ ؃HL$ KQHHK8Hu|BnHH@MnHL$ 裃HL$ 8QHHK8HuGBnHH@MnHL$ nHL$ TAnHH@LnHL$ eHL$ ۣQHHK8Hu AnHH@KLnHL$ 0HL$ cQHHK8Hu@nHH@LnHL$ HL$ qeQHHK8Hu@nHH@KnHL$ ƁHL$ ,gQHHK8Huj@nHH@KnHL$ 葁HL$ 臥QHHK8Hu5@nHH@wKnHL$ \HL$ PHHK8Hu@nHH@BKnHL$ 'HL$ PHHK8Hu?nHH@ KnHL$ HL$ 舻PHHK8Hu?nHH@JnHL$ 轀HL$ tQHHK8Hua?nHH@JnHL$ 舀HL$ nPHHK8Hu,?nHH@nJnHL$ SHL$ iQHHK8Hu>nHH@9JnHL$ HL$ tmQHHK8Hu>nHH@JnHL$ HL$ /QHHK8Hu>nHH@InHL$ HL$ 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0bQPp*bp*b`*bP*bQP2b2b0b0bQP*b*b*b)bQPp0bp0b`0bP0bQP,b,bp+b`+bQP`3b`3bP3b@3bQP2b2b2b2bQP`)b`)bP)b@)bQP03b03b 3b3bQP2b2b2b2bQP)b)b)b)bQP*b*b*b*bQPP+bP+b@+b0+bQP.b.b[.bQP@*b@*b0*b *bQP*b*b*b*bQP-b-b[,bQP+b+b*b*bQP0b0bY0bQP.b.b.b.bQP3b3b [2bQP`4b`4b3b3bQPp.bp.b -b-bQP.b.b.b.bQP@]b@]b\b\bQP@`b@`b0`b `bQP`b`b^b^bQP^b^b`]bP]bQP^b^b`]b^bQPxbxbxbxbQPybybybxbQPpcpc`cPcQPcc ccQPPcPc`cPcQPЍcЍcccQP0c0c ccQP@c@c0c cQP`c`cPc@cQPpcpcc`cQP`c`cccQPccДccQPccccQPcccpcQPpcpc`cPcQPccPc@cQP`c`cPc@cQPpcpcccQPccccQPccccQPccccQPccpc`cQPccccQPccccQPpcpc`cPcQPPcPcc@cQPccccQPЏcЏcccQPcccpcQPccccQP0c0c ccQPccccQP0c0cccQPcc cpcQPccЊccQP@c@cАccQPВcВcccQPccccQP@c@c0c cQPccccQPccccQPccЎccQPccccQP@c@c0c cQPccccQPccccQPccccQPcccpcQP`c`cccQP d dddQPdd`]bdQP@d@dddQPddddQPddddQPPdPd@d0dQPdddpdQPddpd`dQPpdpdddQPddddQP@d@d`]b0dQPddddQPdd`dPdQP d dddQPPdPd@d0dQPddddQP`d`dpd`dQP d dddQP d dddQPddddQPdddpdQPdd`]bdQP`d`dPd@dQPddddQPdd`WdQPddddQPdd`dPdQPddddQPLeLeLeLeQP@Oe@OepNe`NeQPPNePNeMeMeQPOeOeOeOeQPMeMeLeLeQPpOepOe`OePOeQPOeOeOeOeQPLeLeLeLeQPffffQPPfPfc@fQPмfмfcfQPffcfQP`f`fPf@fQP f fffQP`f`fcPfQP`f`fPf@fQPffffQPffffQP0f0f ffQPff`cfQPffffQPffffQP0f0f ffQPffАcfQP@f@f0f fQPйfйfffQP@f@fc0fQPffcfQPffffQPffcfQPкfкfcfQPff`cfQPPfPf@f0fQPffpcfQPffcfQPffcfQPffYfQPffcоfQP0f0f ffQPpfpf`fPfQP f f`cfQPfffpfQPpfpf@f`fQP@f@fc0fQPfffpfQP`f`fcPfQPffнffQPffffQPпfпfДcfQPffcfQPffcpfQP f f0cfQPffcpfQPPfPf@f0fQPffcfQPffлffQPpfpfc`fQPff`cfQPff@ffQPpfpff`fQPffcfQPffffQPffffQPffffQPff@f0fQPggggQPggpggQPggggQPggpg`gQP0g0g ggQPPgPgg@gQPggggQPggggQPggpg`gQP0g0gg gQPggggQPgg*bpgQPgggpgQPggPgpgQPgg*bgQP`g`gPg@gQPgg 3bgQP@g@ggpgQPgg*bgQPgg@YgQP g gggQPgg@+bкgQP g gPg@gQP`g`gPg@gQP0g0g*b gQPgg@g0gQPggggQP0g0g ggQPggggQP g gggQP`g`gPg@gQPмgмg@ggQP`g`g*bPgQPggggQP=h=hmplpJml@DmqOHm0Hm lDmFmjlpl0lqO l l lll@ smljmqOqmpm l kmnmj`lPllqOll l@lpl mlmqOmm lm mpjll@lqOl@l lPllPmlzmqOmpm l@|mPmPVPl@lPlqOll llllpPml LmqOpOmNmlPLmNmpl`empllqOlpl llllPml mqOmpm l@mmlPcmPlWmqOZmZm l0XmYml`l`llqO@ll lPl`lllllqOlpl lll0l`dm`lQmqOpUmTm lQmSmlam@l ]mqOam`m l`]mP_mlPymltmqOxm xm l umwm0lmllqOm@m l0lPmpl[m0lWmqOZmZm l0XmYm0lPVm lQmqOpUmTm lQmSm8ml`!mqO3mP0m l"mP)m`mmmmo@mmmmmmn0nnn'n0'n@'nP'no`m7n7nm8nnnnnnЯnnnpNoXoXoPo XopNoaoaoDopNoaoPoPoPo@mio lomUnknown exceptionbad array new lengthstring too longD:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)D:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longD:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: D:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s wȧwЧwاwCan't happenfalsetrue\\x%02xty10invalid string position00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899D:\a\_work\_temp\protobuf\protobuf-3.20.2\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): ...\\?\\/INVALID_ARGUMENT:, 'map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.narrowing_errorStacktrace: Cpuai.onnx.mlai.onnx.preview.trainingai.onnx.trainingVIWEFAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::IDataTransfer::CopyTensorAsync is not implementedD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)CPUExecutionProviderelem_type_ != nullptrTensor sequence must contain only primitive typesD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::Geti < tensors_.size()Trying to get a Tensor, but got: OrtValue::GetIsTensor()OrtValue::GetMutableD:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hIsTensorSequence()Trying to get a TensorSeq, but got: IsSparseTensor()Trying to get a SparseTensor, but got: SafeIntExceptionHandler::SafeIntOnOverflowInteger overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hORT_LOAD_CONFIG_FROM_MODELUnknown Exceptionsize overflowtried creating tensor with negative value in shapenot enough space: expected , got `anonymous-namespace'::GetDataTransferNot able to find appropriate IDataTransfer to copy sparse dataStrings can only reside in CPU memoryD:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shape`anonymous-namespace'::ValidateFillInputArgsOrtApis::FillSparseTensorCsrOrtApis::FillSparseTensorCooOrtApis::FillSparseTensorBlockSparsetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::UseCooIndicesCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::UseBlockSparseIndicesOrtApis::UseCsrIndicesUse GetStringTensor*() API to retrieve stringsthe ort_value must contain a constructed tensorRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledRegisterCustomOpsUsingFunction: Registration function was not foundinput name cannot be emptyonnxruntime_profile_output name cannot be emptyNULL input supplied for input lengths allocation failedOutput buffer allocation failedstring buffer allocation failedinput array doesn't equal tensor sizeOrtValue should contain a Tensor or a Sparse Tensorelement index is out of boundsThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse dataindex is out of boundsshape is invalidoutput buffer is too small. Use GetStringTensorDataLength.offsets buffer is not equal to tensor sizeout of indexbuffer size is too small for string elementindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.input array is too shortin[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.OrtCreateValueImplSeqHelperAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrSpecified provider is not supported.location dimensions do not match shape sizethis API does not support stringsinvalid location rangearena_extend_strategymax_memmax_dead_bytes_per_chunkinitial_chunk_size_bytesInvalid key found: initial_growth_chunk_size_bytesThe given version [%u] is not supported, only version 1 to %u is supported in this build. Training APIs are not supported with this build. Please build onnxruntime from source with the build flags enable_training_apis to retrieve the training APIs. 1.14.0T utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. onnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataTensor must always contain primitive types. Found: Invalid index requested for map type.OrtCreateValueImplMapHelpervalue_type != nullptrValue type is not supported yet: Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::check++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscriptonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggernot implementedD:\a\_work\1\s\onnxruntime\core\session\abi_session_options.ccFailed to load dynamic library OrtSessionOptions::RegisterCustomOpsLibraryFailed to unload handle for dynamic library graph_optimization_level is not validexecution_mode is not validSession config entry '' was not found. contains null pointersOutput buffer is not large enough for session config entryInput index: Provider options key/value cannot be empty execution provider is not supported in this build. Maximum string length for a provider options key/value is 1024.XNNPACKSNPEUnknown provider name. Currently supported values are 'SNPE', 'XNNPACK', and 'AZURE'AZUREai.onnxonnxruntime::OpKernel::ComputeAsyncResult buffer is not large enough::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input does not have a typeOutput buffer is not large enough for ::OrtKernelInfo input name::OrtKernelInfo output index is out of boundsUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKernel::OrtKernelInfo output does not have a type' in custom op 'Only the last input to a custom op may be marked variadic.i == input_count - 1D:\a\_work\1\s\onnxruntime\core\session\custom_ops.cccustom op registered at runtimeInputall typesonnxruntime::CreateCustomRegistryTThere 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.type_id_counter == 1Only the last output to a custom op may be marked variadic.i == output_count - 1T0Outputonnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size (onnxruntime::TensorSeq::AddTensorSeq: tensor to be added has a different data type.IsSameDataType(tensor) is not present.Required output at index D:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.output_ptronnxruntime::OpKernelContext::RequiredOutputp_ml_valueonnxruntime::OpKernelContext::OutputD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.ccProvider_SetHostonnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::EnsureGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime_providers_rocm.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_cuda.dllonnxruntime_providers_cann.dllonnxruntime_providers_migraphx.dllonnxruntime::TryGetProviderInfo_CUDAonnxruntime_providers_openvino.dllonnxruntime_providers_tensorrt.dllonnxruntime::TryGetProviderInfo_ROCMSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared librarySessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.invalid unordered_map keyMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputRequired input at index input_ptrCreate_State_Compute_Release_State_, expect: matching node is missingkernel already mapped to existing nodearg_num < static_cast(input_count_)invalid arg_num.invalid node output countinvalid node input count: onnxruntime::standalone::CreateOpAttribute type not supported yet.D:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsonnxruntime::standalone::InvokeOpstandalone_OrtMemoryInfo is nullEnv is nullNo requested allocator availableProvided OrtMemoryInfo 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 allocator is nullenv_ptr == p_instance_.get()D:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::Release index= it.second=D:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputonnxruntime::IOBinding::SynchronizeInputsonnxruntime::SyncProvidersmapped_feed_names_.size() == feed_names_.size()Size mismatch:onnxruntime::IOBinding::BindOutputImplmapped_output_names_.size() == output_names_.size()Size mismatchonnxruntime::IOBinding::SynchronizeOutputsp_providerbad castinvalid stoi argumentstoi argument out of range]Device:[DeviceType: MemoryType: DeviceId:name: id: OrtMemType: OrtAllocatorType:OrtMemoryInfo:[CUDAExecutionProviderDmlExecutionProviderD:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider has already been registered.onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraphIsGraphCaptured()D:\a\_work\1\s\onnxruntime\core/session/inference_session.hCached EP instance for graph replay is not set yet before calling ReplayGraph()IsOptionalTensor(type)Provided type is not an optional tensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.honnxruntime::utils::GetElementTypeFromOptionalTensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorMemcpyTransformerORTM/[json.exception.] session.use_env_allocatorssession.set_denormal_as_zerosession.save_model_formatsession.load_model_formatsession.use_ort_model_bytes_for_initializerssession.use_ort_model_bytes_directlysession.intra_op.allow_spinningsession.inter_op.allow_spinningoptimization.minimal_build_optimizationssession.strict_shape_type_inferencesession.force_spinning_stopsession.dynamic_block_basedisable_synchronize_execution_providersmemory.enable_memory_arena_shrinkagesession.intra_op_thread_affinitiesapply value of 'save' is only valid when saving an ORT format model.save%Y-%m-%d_%H-%M-%SThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: Invalid value for ModelProto needs to be parsed to check for ORT config within itD:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configFlush-to-zero and denormal-as-zero are offonis_model_proto_parsedonnxruntime::InferenceSession::ConstructorCommonstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()-intra-op-Creating and using per session threadpools since use_per_session_threads_ is truegraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()!to.affinity_str.empty()Affinity string must not be emptyDynamic block base set to session-custom join thread function not set for inter op thread pool-inter-opto.custom_join_thread_fncustom join thread function not set for intra op thread poolsession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.Using global/env threadpools since use_per_session_threads_ is falseFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: CastFloat16Transformeronnxruntime::InferenceSession::{dtor}::::operator ()Error during EndProfiling(): resultExecution providers must be registered before the session is initialized. Received nullptr for exec provideronnxruntime::InferenceSession::~InferenceSessionUnknown error during EndProfiling()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.Execution providers must be registered before the session is initialized.onnxruntime::InferenceSession::RegisterExecutionProviderReceived nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsParallel 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.This session already contains a loaded model.Exception during loading: Failed to save ORT format model to file: onnxruntime::InferenceSession::SaveToOrtFormat failed:model_loading_uriEncountered unknown exception in LoadWithLoader()onnxruntime::InferenceSession::LoadWithLoaderFailed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Load model from 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_array bytes were able to be read.onnxruntime::LoadOrtModelBytesonnxruntime::InferenceSession::TransformGraphmodel_loading_from_saved_protoThis session has already been initialized.onnxruntime::InferenceSession::LoadOrtModelWithLoaderonnxruntime::InferenceSession::LoadOrtModel::::operator () failed. Only ) has saved runtime optimizations. They will be ignored.Serialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.ORT model verification failed.Missing Model. Invalid ORT format model.The ORT format model version [] is not supported in this build The old ORT format model (version onnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelThe session already has a PrePackedWeightsContainer instanceThe provided PrePackedWeightsContainer instance to be added to the session is nullInitializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: Session has already been initialized.Model was not loaded.Model was not loadedonnxruntime::InferenceSession::InitializeThis session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session will use the allocator registered with the environment.Adding default CPU execution provider.This session will use the CUDA Graph feature as requested by the user.This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been partitioned to the CUDA EP. as all the graph nodes have not been partitioned to the CUDA EP.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.Session successfully initialized.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.NchwcTransformerUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Expected: Please fix either the inputs or the model.session_initializationEncountered unknown exception in Initialize()Got invalid dimensions for input: for the following indices Invalid rank for input: Got: )) , expected: ((Unexpected input data type. Actual: ( index: Size mismatch: feed_names has elements, but feeds has elements.))tensorInput with name: is not expected to be of type tensor.Invalid Feed Input Name: is not expected to be of type tensor sequence.sparse_tensor is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputsp_fetches->size(): At least one output should be requested.Output vector pointer is NULLseqReplaying the captured CUDA Graph for this model with tag: Invalid Output Name:Output vector incorrectly sized: output_names.size(): Running with tag: Session not initialized.Session was not initializedonnxruntime::InferenceSession::Runonnxruntime::InferenceSession::GetModelMetadataStart the second Run() to capture the graph. The first one is for necessary memory allocation;The second one is for capturing the graph.model_runEncountered unknown exception in Run()onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingProfiler is disabled. combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Unsupported device specified in the memory arena shrink list: error message: The registered allocator for device-id combination is not an arena based allocator: Did not find an arena based allocator registered for device-id run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformers961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5_.jsonlist too longother_erroronnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0Hwcom.microsoft.nchwccom.ms.internal.nhwccom.microsoftorg.pytorch.atencom.microsoft.experimentalMismatch between source and target type. Source=[ShapeInferenceError] Unsupported Source/Target type= Target=tensor(uint64)tensor(int32)tensor(int64)tensor(float16)unknowntensor(uint32)tensor(uint16)tensor(int8)tensor(int16)seq(tensor(uint8))tensor(float)tensor(double)tensor(bfloat16)tensor(uint8)seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))tensor(complex64)tensor(complex128)seq(tensor(string))seq(tensor(bool))seq(tensor(float))seq(tensor(double))tensor(string)tensor(bool)seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16)) line , but it is already registered from file ) from file version: Schema error: in onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you (domain: Trying to register schema with name known by the checker., but its domain is notin the inclusive range [, but its version is not Only CPU allocators can be shared between multiple sessions for now.An allocator for this device has already been registered for sharing.Only CPU devices are supported for now.Exception caught: intra-opinter-opD:\a\_work\1\s\onnxruntime\core\session\environment.ccReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.No allocator for this device has been registered for sharing.stringYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostMemcpyFromHostinputXoutputvalue, column onnxruntime::SetIntraOpNumThreadsD:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: at line ort_configsession_optionsUnsupported value for intra_op_num_threads: Sequential modeParallel modeSetting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting enable_profiling to onnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoORT config json from the model: The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelJson stored in the `ort_config` key cannot be parsed. Error message: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()The Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threads' not foundkey 'cannot use at() with graph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: cannot use key() for non-object iteratorsnullobjectarraybooleaninvalid map keycannot get valuebinarydiscardednumberout_of_rangetype_errorinvalid_iteratorparse errorparse_errorsyntax error while parsing - ; last read: 'invalid literaltrue literalfalse literalunexpected ; expected invalid BOM; must be 0xEF 0xBB 0xBF if given'{'']''}'':'null literalstring literalnumber literal'['unknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponent','end of input'[', '{', or a literalinvalid 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 number; expected digit after exponent signinvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: missing closing quoteinvalid 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+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+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+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+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+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+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 \u001Finvalid 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 \u001Bvector too longinvalid string: ill-formed UTF-8 byteobject keyobject separatornumber overflow parsing 'cannot compare iterators of different containerscannot use erase() with iterator does not fit current valueiterator out of rangetype must be number, but is Max:Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()D:\a\_work\1\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.hInsertedCast_cast node to cast from float16 to float32 on cpuCastonnxruntime::GraphTransformer::Recurseonnxruntime::InsertCastTransformer::ApplyImpldtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()toRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccACLExecutionProviderArmNNExecutionProviderROCMExecutionProviderNhwcTransformersession.disable_double_qdq_removersession.enable_quant_qdq_cleanupoptimization.enable_gelu_approximationQDQFinalCleanupTransformerRocmBlasAltImplsession.disable_quant_qdqAttentionFusionAvx2WeightS8ToU8TransformerBiasDropoutFusionBiasGeluFusionsession.qdqisint8allowedsession.x64quantprecisionBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationConvConvBNFusionConvMulFusionDivMulFusionConstantSharingConvAddFusionEliminateDropoutDropoutDynamicQuantizeMatMulFusionEmbedLayerNormFusionDivDoubleQDQPairsRemoverGatherToSliceFusionGeluApproximationGeluFusionGemmActivationFusionExpandEliminationExpandFastGeluFusionGatherToSplitFusionEliminateIdentityIdentityGemmSumFusionGemmGemmTransposeFusionMatMulScaleFusionMatmulTransposeFusionNoopEliminationAddLayerNormFusionSimplifiedLayerNormFusionMatMulAddFusionMatMulIntegerToFloatFusionClipQuantRewriteClipQDQPropagationTransformerQDQS8ToU8TransformerSubMulNotWhereFusionWhereReshapeFusionSkipLayerNormFusionEliminateSliceSliceReluQuantRewriteReluQuickGeluFusionFuseReluClipLevelUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.ccTransposeOptimizerUnsqueezeEliminationUnsqueeze_RuleBasedTransformeronnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformeronnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildVitisAIExecutionProviderTensorrtExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexMemcpyExecution type 'dup_replacements.find(&arg) == dup_replacements.end()Copy from/to host memoryD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImpl' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessDefsonnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::ApplyBad optional accessGatherTransposeSqueezeQLinearConvNhwcMaxPool does not match rank Permutation entry out of bounds for shape Failed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDimsonnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrsuccessFailed to find initializer for name: onnxruntime::ApiTensor::NumElementssize >= 0No NodeArg found for name onnxruntime::ApiTensor::Datanew_num_elts == old_num_eltsCannot reshape initializer Added in transpose optimizer_outFailed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializer to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializeropset_import_iter != domain_to_version_map.end() domain not found in opset imports.Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.Transpose optimizer is expected to add only onnx domain ops. Domain: provided for op: onnxruntime::GetSinceVersionForNewOpdomain == kOnnxDomainFusedConvonnxruntime::ApiGraph::CopyValueInfoQLinearGlobalAveragePoolQLinearAveragePoolconst_transpose_optimizersince_version.has_value()dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Existing destination type is not compatible with source type.Resizechannels_lastOptimization after layout transformation failed: wBxwBx wBx wp;xwp;x wp;xwp;xw8Bxw8Bx w8Bx w(Bx w(Bx w8>x w8>x w8>x x PBx x @Bx D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered axisonnxruntime::GatherToSplitFusion::ApplyImplaxesnum_outputsSplitSqueeze for Fused Gather nodessplitSplit for Fused Gather nodesD:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.ccShapeUnsqueeze_UnsqueezeAxesInitializerunsqueeze_output_Unsqueeze for Fused Gather nodesRangerange_input_defs.size() == 3SqueezeAxesInitializeronnxruntime::GatherToSliceFusion::ApplyImplSliceAxesInitializerSlice for Fused Gather nodesCould not find OrtValue with name 'ConstantFoldingstartCould not find a CPU kernel and hence fetches.size() == node->OutputDefs().size() node 'can't constant fold onnxruntime::ConstantFolding::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccendDequantizeLinear. Can't constant fold Unsupported output type of Unsupported data type: Constant initializer NodeArg shape should not be null. NodeArg: input_node.InputDefs().size() == 2 && scale_and_index->second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergesonnxruntime::`anonymous-namespace'::GetScaleFromNodemul_inputs.size() == 2D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccdiv_inputs.size() == 2shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializer_FusedMatMulAndScaleonnxruntime::MatMulScaleFusion::ApplyImplalphaFused MatMul and ScaleMatMulFusedMatMuloutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Slice parameter is not expected. Input index:Start MatchGemmSubgraphSlice ends is less than INT_MAXExpected value:D:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsinput_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersGemm does not have 3 inputsGemm bias is not constantInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathConcatFaild to match gemm pathonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueconcat first input value is not -1Faild to match concat node for Gather pathsGemm bias shape not expectedCheckSliceParameters return falseGemm bias is not constant initializerGemm bias shape is not expectedStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializergather input 1 value is not expectedPass MatchGemmSubgraphunsqueeze_after_gather axes value not expectedgather axis value not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedPass ValidateGemmInitializerunidir mask is not constantGemm weight is not constant initializerGemm weight shape is not expectedDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskExpect mask data type is uint8 or floatStart MatchUnidirMaskSubgraphOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceSoftmaxFailed to find Softmax nodeStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskmask_sub const input not matchedmask_mul const input not matchedmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2Output edge count not expected for mask nodesSoftmax attribute axis is expected to be 3Output edge count not expected for SoftmaxFailed to find path for maskequal const not matched.Failed to find reshape shape path 1where const not matched.Failed to find shape pathEqualFailed to find mask pathPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find path for present_kFailed to find path for present_v and past_vonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for past_kPass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphFailed to find reshape shape path 2gather indices not matched.past_v_gather indices != 1past_k_gather indices != 0present_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedFailed to match v_concatpermOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Start CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVPass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapepast_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedreshape initializer value is not expectedStart CheckNodesInPathQv_reshape initializer value is not expectedPass CheckNodesInPathVFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizek_transpose perm attribute not matchedk_reshape const not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose has not perm attributePass CheckNodesInPathQStart CheckNodesInPathKqk_div const not matched.q_transpose perm attribute not matchedFaild to find path to qkv_matmulFaild to find path v to SplitStart FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionCast mask from int64 to int32Pass CheckNodesInPathKMask_Int32Using transpose optimized patternopt_k_transpose perm attribute not matchedq and v are not from same Split nodeCheckNodesInPathQ returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qCheckNodesInPathV return falseMatchInputMaskSubgraph returns falsenum_headsunidirectionalFused Attention subgraphs AttentionCheckNodesInPathK returns falseMatchPastSubgraph returns falseFailed to find path for kk and v are not from same Split nodeMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplonnxruntime::ConvertMaskToInt32D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccqkv_biasMask shape is unknown or not 2D, or data type unknownFused an attention node for GPT.qkv_weightsk root is not layer normk_matmul and k_add shape not matchedq root should be layer normalizationq_matmul and q_add shape not matchedTotal fused Attention node count: onnxruntime::FuseSubGraphQKImplLayerNormalizationshape of layer norm bias tensor not expectedFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFailed to convert mask to int32onnxruntime::FuseSubGraphQKFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed in match input mask subgraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapebn_B_tensor_protobn_mean_tensor_protoonnxruntime::ConvBNFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccepsilonbn_scale_tensor_protoConvBnFusion_BN_B_BatchNormalizationconv_B_tensor_protoConvBnFusion_W_bn_var_tensor_protoconv_W_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccqdq_s8_to_u8_quantonnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_zp_conversiondropSplitQDQGlobalAveragePoolSigmoidAveragePoolLeakyReluArgMax1DQMaxPooldropDQQDQSelectorActionTransformer2DQ*DQxD:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.honnxruntime::NodesToOptimize::GetNodeindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)Tanhoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Failed to get Clip min/max constants.activationD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvActivation::ExtraAttributesactivation != nullptrExpected activation node.HardSigmoidConvActivationFusionConvAddReluConvActonnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"Expected Conv then Add.activation_paramsbetaD:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination_QuickGeluD:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.cconnxruntime::QuickGeluFusion::ApplyImpl_min_zero_constantD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of minFuseReluClip_BiasGeluTotal Gelu Approximation (FastGelu) node count: FastGeluGelu approximationD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.cconnxruntime::GeluApproximation::ApplyImplGelux0xPxFused reshape node: allowzeroD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::ApplyImplonnxruntime::ReshapeFusion::Fuse_SubgraphCannot replace concat node with initializer:ConcatTrainingendsstartsTotal fused reshape node count: transAtransBnew_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccgraph.RemoveNode(sum_node.Index())Sumother_sum_input != nullptronnxruntime::GemmSumFusion::SatisfyConditionnew_gemm_output_defs.size() == 1Fused Gemm with Sum_sum_transformedgemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0!sum_input_movedgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0 and Invalid node index node != nullptronnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndnode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputQuantizeLinearAt least one graph node must be specified in the propagation edge.src_node || dst_nodeonnxruntime::`anonymous-namespace'::InsertQDQPairD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cc"._qFailed to set op schema for added Q node._dqFailed to set op schema for added DQ node.src_node != nullptronnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgedst_node != nullptronnxruntime::`anonymous-namespace'::GetNextPropagationEdge at NodeArg "", index: node ("Inserting Q/DQ pair between _pre_qInserted by QDQPropagationTransformeronnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::QDQPropagationTransformer::ApplyImpl_Int32Cast Input from int64 to int32Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeNonZero_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputD:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.position_embeddingsmask_indexfused EmbedLayerNorm subgraphs EmbedLayerNormalizationWord embedding shape not expected.ConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplonnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.YieldOponnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeLinearDynamicQuantizeMatMulFreeDimensionOverrideTransformerInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of with a fixed dimension size ' The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplgemmfused Matmul and Add SeluEluFusedGemmfused Gemm activation_fused SoftsignSoftplusParametricSoftplusScaledTanhonnxruntime::GemmActivationFusion::ApplyImplThresholdedReluwith activation D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::GetQConstantLowerUpperUnexpected data type for QuantizeLinear input y_zero_point of onnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ExtraAttributesonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivation::ValueMovesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.ccConvAddActivationFusionConvAddActweight_zp_s8_2_u8 int8 overflow might impact precision !D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.ccUnable transforming DynamicQuantizeLSTM operator, cannot locate recurrence tensor of const int8 type,QAttentionMatMulIntegerQGemmQLinearMatMulonnxruntime::TryConvertDynamicQuantizeLSTM unable to locate recurrence tensor or its zero point value,onnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplDynamicQuantizeLSTMFused Gemm with Transpose_transformedonnxruntime::UpdateConsumerCount!node_consumers.empty()onnxruntime::ReorderCastAndTransposecast != nullptrCreated a new Transpose node to interchange Cast and Transpose nodesCreated a new Cast node to interchange Cast and Transpose nodestransBatchAonnxruntime::MatmulTransposeFusion::ApplyImpltranspose_node.InputDefs().size() == 1D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cconnxruntime::GetTransposePermsfused MatMul and Transpose transBatchBMatMul_With_Transposedeque too longmul_B_tensor_protoConvMulFusion_W_onnxruntime::ConvMulFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccConvMulFusion_Mul_B___backwardpassonnxruntime::RocmBlasAltImpl::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.cconnxruntime::LayerNormFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccPowfused LayerNorm subgraphs ReduceMeanSqrtSimplifiedLayerNormalizationstash_typeonnxruntime::SimplifiedLayerNormFusion::ApplyImplx0x @xx0xPxonnxruntime::SkipLayerNormFusion::ApplyImplSkipLayerNormalizationD:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs stridesdilationsReorderInputpadsNOTSETSAME_UPPERkernel_shapeauto_padreorderreshapespatialchannelsmode_bn_nchwcReorderOutputgroupBuffer overflowSAME_LOWERVALIDD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cc_nchwcoutput_channels <= nchwc_output_channelsonnxruntime::NchwcTransformerImpl::TransformConvGlobalMaxPoolonnxruntime::NchwcTransformer::ApplyImplUpsamplescalescoordinate_transformation_modeasymmetricnearestlinearnearest_modeflooralign_cornershalf_pixel into softmax(input + bias)onnxruntime::BiasSoftmaxFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.ccBiasSoftmaxis_inner_broadcastaxis axis >= -tensor_rank && axis <= tensor_rank - 1 is not in valid range [-onnxruntime::HandleNegativeAxisD:\a\_work\1\s\onnxruntime\core/providers/common.h8x`wDoubleQDQRemoved_ of node [] because it's the graph's output.representative.output_index != kInvalidOutputIndexNot eliminating output D:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::CommonSubexpressionElimination::ApplyImplCleaning up back-to-back nodes: " and with name ""onnxruntime::QDQFinalCleanupTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cconnxruntime::`anonymous-namespace'::CleanUpNodeSequenceNotD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.cconnxruntime::TransposeOptimizer::ApplyImplTranspose optimizer failed: D:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.cconnxruntime::NhwcTransformer::ApplyImplBitmaskBiasDropoutBiasDropoutBitmaskDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccfused Add-Dropout-(Add) for onnxruntime::BiasDropoutFusion::ApplyImplonnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplonnxruntime::BiasGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputfused GPT2Gelu subgraphs GPT2Geluonnxruntime::FastGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccx0xPx0xTotal shared scalar initializer count: onnxruntime::ConstantSharing::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.cconnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()  ortshared_ConvAddFusion_B_add_B_tensor_protoConvAddFusion_Add_B_D:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::Applystorage_ordernoop_with_empty_axesLogExpReduceSumkeepdimsCosSinSinhTanSignShrinkHardSwishCeluRoundNegIsNaNIsInfAbsReciprocalFloorCeilPReluModGreaterBitShiftAndMinXorOrAcoshAsinhMaxAtanhAsinCoshAtanAcosReduceL2ReduceL1TileArgMinReduceMinReduceMaxReduceSumSquareReduceProdPadCastLikeReduceLogSumExpReduceLogSumGreaterOrEqualLessMeanLessOrEqualDepthToSpaceGridSampleConvTransposeSpaceToDepthcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAveragePoolLRNUnsupported ONNX opsetcom.microsoft.QLinearConcatcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearMulcom.microsoft.QLinearAddLogSoftmaxHardmaxcom.microsoft.QLinearSigmoidcom.microsoft.QLinearReduceMeanMaxUnpoolonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncecalled_ == 1onnxruntime::Initializer::mulonnxruntime::Initializer::subAxis must be non-negativeonnxruntime::Initializer::divonnxruntime::Initializer::adddata_type() == other.data_type()size() == other.size()Expecting the same sizemodel_path must not be empty. Ensure that a path is provided when the model is created or loaded.D:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccExpecting the same data type!model_path.IsEmpty()Initializer must have a datatypeonnxruntime::Initializer::Initializerutils::HasDataType(tensor_proto)onnxruntime::Initializer::scale_by_axisaxis >= 0scalers.size() == 1 || scalers.size() == num_blocksInvalid other(scalers) sizeonnxruntime::Tensor::MutableDataAsSpanRandomUniformonnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of maxD:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccRandomUniformLikeRandomNormalMultinomialRandomNormalLikexxx xxallocator_ptr_Failed to get allocator for optimizeronnxruntime::TryCreateKernelonnxruntime::OptimizerExecutionFrame::Info::InfoD:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()Tried to allocate without valid type information, ort_value index=D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.ccq_or_dq_input_defs.size() >= 2onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointinserted_in_name_to_entryonnxruntime::SelectorActionRegistry::RegisterSelectorAndActionExisting registration with name Nodes to optimize are not valid, skipping action.Applying runtime optimization action Missing action onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations for transformer New node op (Too many produced nodes in the runtime optimization record.).) does not match produced node op in runtime optimization record (Not enough produced nodes in the runtime optimization record.onnxruntime::SetOpSinceVersionForProducedNodesonnxruntime::MatchAndProcessonnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMatched opsetinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14agenerated at runtimeQLinearD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::CreateReplacementNodeonnxruntime::MergeIntoTarget::RunFailed to set node op schema.Failed to remove node.onnxruntime::ReplaceWithNew::Runonnxruntime::ReplaceWithNew::RunForSaveonnxruntime::MoveInputOutputtarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.Node index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::`anonymous-namespace'::MoveInputOutputImplD:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.ccIndex out of rangeOpenVINOExecutionProviderDnnlExecutionProviderAzureExecutionProviderCANNExecutionProviderXnnpackExecutionProviderTvmExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderNnapiExecutionProviderH?x`wh?x8xx xx(?xлxx8x7xxwxhxPx8xwonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsperm: does not align with rank of input data: [ is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max()Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::Transpose::Computeinput_tensor_ptr != nullptrMismatched data types between input and output Tensors. (local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::NonMaxSuppressionBase::PrepareComputeboxes_tensorNonMaxSuppressionD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1center_point_boxoutput != nullptronnxruntime::NonMaxSuppression::Computeonnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].The most inner dimension in boxes must have 4 data.boxes and scores should have same spatial_dimension.boxes and scores should have same num_batches.scores must be a 3D tensor.boxes must be a 3D tensor.scores_tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccTindD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.honnxruntime::GatherBase::GatherBaseinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueGather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::Computeindices element out of data bounds, idx= must be within the inclusive range [D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBaseinfo.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareComputeX != nullptronnxruntime::Unsqueeze::Computenullptr != p.output_tensor'axes' has a duplicate axis'axes' has an out of range axisaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a scalar or a 1-D tensor.axes_tensor != nullptrAxes input is nulltypeOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()onnxruntime::Optional::OptionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::Computecan only be of type(tensor) or (seq(tensor))The TypeProto attribute in the Optional op Trying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::OptionalGetElement::ComputeVOOptionalBOptionalHasElementOptionalGetElementonnxruntime::PropagateInputOrtValueToFirstOutputD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hIf 'num_outputs' is specified, the 'split' input should not be provided.Invalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBasesplit_tensor->Shape().NumDimensions() == 1onnxruntime::SplitImpl::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cc. Size of dimension being split is Invalid num_outputs value of NumOutputs= Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=Cannot split using values in 'split' attribute. Axis=The split tensor must be a vector tensor.src and dst types must matchdst.DataType() == src.DataType()onnxruntime::DispatchStridedCopyD:\a\_work\1\s\onnxruntime\core/framework/copy.hUnsupported input data type of onnxruntime::StridedCopy::::operator ()src and dst must have same shape and not be rank 0.dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()onnxruntime::StridedCopycopy shape must have non-negative sizetotal_num_elements_to_copy >= 0last >= firstonnxruntime::StridedCopy::::operator ()counter.current_offset == lastonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.ScatterScatterElementsCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'. is greater than input dim= at pos=Indices dim=Indices type is not supported.data type is different from updates typeIndices and updates must have the same rank vs Indices vs updates dimensions differs at position=. Input rank=Indices must have the same rank as Input. Indices rank=onnxruntime::Scatter,class std::allocator > > >::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccreductionnoneCPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.ScatterND, indices shape: input shape: input tensor and indices tensor must has rank larger than 0. last dimension of indices must not be larger than rank of input tensor, data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::PrepareForComputeinvalid indice found, indice = onnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()constantreflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valuePads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)onnxruntime::Pad::ComputeAxes tensor should be a 1D tensor axes_tensor_dims.size() == 1Pads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1dims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()onnxruntime::SliceIteratorBase::InitCannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of onnxruntime::PadValueFromFloatD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccInput tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesdata_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZero((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::BroadcastIterator::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid.Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterInputBroadcaster can only start at span boundary!offset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length:Invalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:Broadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocatorBitwiseAndBitwiseNotBitwiseOrBitwiseXorUnsupported X type: Must have 1 or more inputsinputCount >= 1onnxruntime::Min_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapedata_n.Shape() == shapeonnxruntime::Max_6::Computeonnxruntime::Mean_6::Computedata_1.Shape() == shapeExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()T1onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ExpandBroadcastLooperTensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandfmodfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfmod attribute must be true for floating point typesonnxruntime::mod_internal::CallModImpl::operator ()input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeInvalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BroadcastLooperinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computecur1 == end1onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()Unsupported Y type: onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKerneldirectiononnxruntime::BitShift::BitShiftLEFTRIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'onnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()Col2Imdilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.cconnxruntime::Col2Im::Computepads_.size() == 2 * image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.strides_.size() == image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.strides_.empty()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.honnxruntime::Col2Im::Col2Impads_.empty()dilations_.empty()ceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()count_include_padkernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]Pad should be smaller than kernel.Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:dilations.size() == kernel_shape.size()onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::InferOutputSizeinput_dims.size() >= 2onnxruntime::StringToAutoPadTypeUnknown AutoPadType StringGlobalLpPoolinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributes`InlinedVector::at(size_type)` failed bounds checkonnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.info.GetAttr("p", &p_).IsOK()pD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initonnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.Unsupported pooling size.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccIkernel_shape num_dims is not compatible with X num_dims.LpPoolonnxruntime::Pool::Computeonnxruntime::LpPoolV18::ComputeUnsupported pooling size : D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.honnxruntime::LpPoolV18::LpPoolV18Unsupported kernel dimension : onnxruntime::MaxPoolV8::ComputeImplfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKReduction on all axes, output size should be 1.onnxruntime::ValidateFastReduceRKRonnxruntime::ValidateNoTransposeReducecount == 1An axes tensor must be a vector tensor.onnxruntime::ValidateCommonFastReduceCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:axes_tensor->Shape().NumDimensions() == 1onnxruntime::ValidateKeepDimsonnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptylast_loop_red_size > 0last_loop_size > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccmust be overloaded.projected_index.size() > 0Only works on matrices with two dimensions.onnxruntime::ValidateMustBeOverloadedonnxruntime::ValidateFastReduceKRfast_shape.size() == 2fast_shape[0] == output.Shape().Size()Output size mismatch.fast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape.size() == 3Only works on matrices with three dimensions.onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()select_last_indexD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<0>::ReduceKernelBasewhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.hantialiaskeep_aspect_ratio_policyextrapolation_valueinfo.GetAttrs("scales", scales_).IsOK()stretch`tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floor model uses the deprecated attributeyet this opset and anti-aliasing is set to exclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to cubic_coeff_aexclude_outsideinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBasecubicnearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeround_prefer_ceilceilScale value should be greater than 0.Resize operatorScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::ScalesValidation'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 should be greater than 0.Upsample operator'Linear' mode only support: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the onnxruntime::UpsampleBase::ParseScalesDataall values in axes should be less than rank of the data. It can only be mode attribute is or (default) or not_smaller] is not supported!onnxruntime::UpsampleBase::StringToUpsampleModenot_largerpytorch_half_pixeltf_half_pixel_for_nnkeep_aspect_ratio of [onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicycoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModetf_crop_and_resize] is not supportted!UT2dft_length must be a scalar value.onnxruntime::discrete_fourier_transformDFTSTFTonesidedSafeIntExceptionHandler::SafeIntOnDivZeroinverseDivide by zeroUnsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::DFT::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.ccdft_length must be greater than zero.onnxruntime::STFT::ComputeUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.ratio input should have a single value.D:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.hsignal shape must end in 1 (real) or 2 (real, imaginary).tensor->Shape().Size() == 1onnxruntime::signal::get_scalar_value_from_tensorIf both frame_length and window are set, then the size of the window must be equal to the frame_length.frame_length == window_lengthsignal_components == 1 || signal_components == 2onnxruntime::short_time_fourier_transformEnsure that the dft size is smaller than the signal.window_size < signal_sizeUnsupported bit size.onnxruntime::bit_reverse@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?periodicoutput_datatypeHannWindowHammingWindowMelWeightMatrixBlackmanWindowT3onnxruntime::CreateMelWeightMatrix::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cclower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()upper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputAttempt to retrieve final output before it was set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hbodyinfo.GetAttr("body", &proto).IsOK()num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::Scan<9>::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccscan_input_axes but expected scan_input_directionsscan_output_directions' dimension but input 'Invalid scan input: has length of of Invalid value in scan_input_axes for input Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsOutput OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariablesscan_output_axesNumber of entries in 'scan_output_axes' was Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_info_ == nullptronnxruntime::Scan<9>::SetupSubgraphExecutionInfogsl::narrow_cast(output_axes_.size()) == num_scan_outputsSetupSubgraphExecutionInfo should only be called once for each subgraph.onnxruntime::Scan<9>::ComputeSubgraph SessionState was not found for 'body' attribute.CreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_ dimensions or more but input had shape of Expected session_stateonnxruntime::ScanImpl::InitializeInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::Execute. Output tensor rank was onnxruntime::ScanImpl::TransposeOutputScanonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeNo attribute with name:''is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeunknown kernel typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ElementWiseRangedTransform::Createonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelgammaD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ScaledTanh::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelgrid_dims[3] == 2Last dimension of grid: Grid batch size does not match input batch size Only 4-D tensor is supported, expect 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cconnxruntime::GridSample::Computegrid_dims[0] == NreflectionborderD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.honnxruntime::GridSample::GridSamplepadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"padding_mode "" not supported, expect zeros, border or reflectionpadding_modezerosbilinearmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"mode "" not supported, expect bilinear, nearest or bicubicbicubicThe existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.Invalid mode of value specified. It should be either avg or maxavgoutput_widthoutput_heightD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.honnxruntime::RoiAlignBase::RoiAlignBasespatial_scalesampling_ratio_ >= 0Sampling ratio should be >=0, but it was sampling_ratioNull batch_indices_ptrNull rois_ptrNull input X ptrRoiAlignFirst dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly Number of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1onnxruntime::Loop::InitInconsistent shape in loop output for output. Expected: Got:'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoLoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: 'Loop' input 'cond' should be a scalar tensor. Got shape of onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()All scan outputs MUST be tensors inputs. Found:LoopD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::Execute::::operator ()If outputs. outputs which doesn't match the subgraph's Failed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for If output onnxruntime::IfImpl::Executethen_branchinfo.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::Initelse_branch'If' node has num_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Compute' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::Initializeinfo.GetAttr("else_branch", &proto).IsOK()info == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatoronnxruntime::IdentityOp<1>::Computebad allocationInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: onnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid input var: 0th dimension != Invalid input var: Invalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::ComputemomentumTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNormtraining_modeonnxruntime::BatchNorm::BatchNormRNNforwardreversebidirectionalonnxruntime::RNN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccAffineclipallowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betaactivationslayoutinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNND:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hRNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0info.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.h'. Must be one of 'forward', 'reverse', or 'bidirectional'.info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.finput_forgetsigmoidinfo.GetAttr("direction", &direction).IsOK()onnxruntime::LSTMBase::LSTMBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::Computetanhactivation_func_names.size() == static_cast(num_directions_) * 3LSTMonnxruntime::DeepCpuLstmOp::PrePackGRUactivation_func_names.size() == static_cast(num_directions_) * 2Invalid data type for GRU operator of GRU operator does not support double yetD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::Computeonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_Can not get unpacked span from prepacked weightsonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= NB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::SafeRawPointercur + size <= endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointerrequested_shape[i] >= -1A dimension cannot be less than -1, got D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelperThe input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:gsl::narrow_cast(input_shape.Size()) == sizesize != 0 && (input_shape.Size() % size) == 0unknown_dim == -1At most one dimension can be -1.i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.Attribute shape is not set.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1A shape tensor must be a vector tensor.onnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::Triluk should be a 1-D or 0-D tensor.Triluinfo.GetAttr("upper", &temp).IsOK()upperonnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)Input tensor should have a rank of at least 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccAxis tensor should be of type `int32_t` or `int64_t`Axis tensor should be 0D or 1DCumSumAxis tensor must be provided to the CumSum oponnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeexclusiveonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initsteps.size()=dims.size() == extents_.size()onnxruntime::WritableSliceIterator::Initdims.size() == steps.size()dims.size() == starts.size()dims.size()=extents.size()=onnxruntime::WritableSliceIterator<__int64>::Initstarts.size()=onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13Hardmax inputs N, D and N * D must be < D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cconnxruntime::Hardmax::Compute. N=, D=input_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computebatch_dimsindicesGatherNDindices tensor data type not supportedindices tensor must has rank larger than 0invalid index found, index = X input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computefailed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccNull input ptrinfo.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseblocksizeAttribute blocksize is not set.DCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDepthToSpace requires input depth to be a multiple of (block_size * blok_size)SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceDepth ops require a 4-D input. Provided rank: Unsupported input type in DepthToSpace op: Unsupported input type in SpaceToDepth op: onnxruntime::DepthToSpace::Computeonnxruntime::SpaceToDepth::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::DepthToSpace::DepthToSpaceinput count mismatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsonnxruntime::GatherElements::GatherElementsGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Out of range value in index tensoronnxruntime::core_implD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.ccGatherElements op: Unsupported tensor type, size:onnxruntime::GetIndexInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the first one is emptyTile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.ccTile doesn't support string type yet!input_tensor.IsDataType()'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::Squeeze::Computeinput_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShape must be 1 instead of Dimension of input . shape=Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attribute'step' value cannot be 0'axes' has duplicates'axes' has an axis outside of the tensor dimension count!has_axes || attr_axes_.size() == attr_starts_.size()Data type for starts and ends inputs' is not supported in this build. Got Starts and steps shape mismatchStarts and axes shape mismatchStarts and ends shape mismatchEnds must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::ComputeCannot slice scalarsonnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of output == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::ConcatBase::ComputeImplData type mismatchNon concat axis dimensions must match: Axis has mismatched dimensions of input_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: new_axisD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attribute got: Cannot concatenate scalarsinput != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForCompute!mask || mask->Shape() == X_shapeX and mask should have the same shapeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Compute0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1seedInput is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationMeanVarianceNormalization CPU EP only supports NHW and NCHW reduction for axes attribute.normalize_varianceonnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_1::MeanVarianceNormalization_1info.GetAttr("normalize_variance", &normalize_variance_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4biasbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()size_ % 2 == 1size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()sizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenFlattenonnxruntime::Flatten::Computegsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axiszero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.ccscale 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)x_zero_point must be null or a scalar or 1D tensor or size 1.DequantizeLinear with type int32 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })onnxruntime::DequantizeLinear::ComputeSizeinfo.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBase::GemmBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transB", &temp).IsOK()Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: c_shape is required if c_data is providedc_shape != nullptronnxruntime::GemmBroadcastBiasright operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMulComputeHelper::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim onnxruntime::MatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computemin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.max->Shape().IsScalar()stoull argument out of rangeinvalid stod argumentstod argument out of rangeinvalid stoll argumentinvalid stoull argumentstoll argument out of rangeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastAttribute to is not set.snprintf() failed with return value: %.8gonnxruntime::`anonymous-namespace'::CastToStringsnprintf_result > 0snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().NaNINF-INFinvalid expand shapeinfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::EinsumEinsum op: There must be atleast one inputEinsumonnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum opequation-> is not supported yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.ccEinsum op: An implementation for the input type Invalid argument for depth; it's not a scalar.OneHotInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp::ComputeDepth is negative.onnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::Computek_temp > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()largestonnxruntime::TopkOpset11ConstructorCommonsortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()konnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()TopKop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()] should not be greater than specified axis dim value [input count mismatch, expected 1 input - the tensor to be processedk argument [k tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuevalue of k must not be negativeoutput count mismatch, expected 2 outputs to be present for TopK operatorUnsupported tensor type of Uniquelimit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!onnxruntime::CreateCopyAndAppendCpuTensor), input tensor data type (onnxruntime::SequenceEmpty::ComputeSequenceInsertSequenceEraseonnxruntime::SequenceErase::ComputeData type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceInsert::Computeonnxruntime::GetSeqIdxD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLengthSequenceAtSequenceEmptyUnsupported 'dtype' value: ) specified for sequence of size (Invalid sequence index (Sonnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputsplit_size_sum (Invalid data type for split tensor onnxruntime::SequenceConstruct::ComputeViolation of the requirment that all input tensors must have the same data type.SequenceConstructnum_inputs >= 1SplitToSequence operator does not support ) != split_dim_size (SplitToSequence yetsplit_scalar > 0onnxruntime::SplitToSequence::ComputeImplSplit should be > 0Invalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccGot nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeConcatFromSequence group: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: X: X num_dims does not match W num_dims. channels_last: kernel_shape is not compatible with W shape.auto_pad == AutoPadType::NOTSETonnxruntime::ConvAttributes::ConvAttributesA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesonnxruntime::ConvTranspose::DoConvTranspose*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapefilter number not equal to input channel number.Input channels is not divisible by group. num_input_channels: filter_number: dim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapeonnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hInvalid input shape: output_shapegroup count is <= 0output_paddingD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. kernel channels: C: ComputePad: pad type not supported.onnxruntime::ComputePadAndOutputShapeoutput and sum shape must matchonnxruntime::Conv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.ccOutput channels M is not divisible by group.Not enough elements in strides. Expected: Input channels C is not equal to kernel channels * group. M: Not enough elements in pads. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeNot enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: strides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.cconnxruntime::MaxUnpool::Computeoutput_shape is smaller than minimum required. output_shape: inferred output shape:Shape must be 1 dimensional as it's tensor data of a shapeIndex tensor shape should be same as that of the input data tensor to unpool.DetMatrix dimensions are not equal. Square matrix is expectedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()CompressD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeonnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.ccbatch_axis != time_axistime_axis and batch_axis must have different values but both are time_axis < 2info.GetAttr("time_axis", &time_axis).IsOK()time_axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpInvalid time_axis of batch_axis < 2Invalid batch_axis of . Must be 0 or 1info.GetAttr("batch_axis", &batch_axis).IsOK()batch_axissequence_lens shape must be {batch_size}. Got:. batch_size=ReverseSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of Invalid sequence length: . Value must be in range [0,D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativeFailed to obtain detect_negativedetect_positiveFailed to obtain detect_positiveonnxruntime::IsInf::IsInfT4onnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccprepacked_buffers[0].get() == nullptrQLinearConv : zero point of per-channel filter must be sameW_zero_point_data[i] == W_zero_point_valueonnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::ComputeOffsetQLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)IsValidQuantParam(W_scale, M)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeOffsetonnxruntime::QLinearConv::ComputeOutputScaleQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidQLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)ConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(W_Zero_Point)IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.ccNon per-tensor quantization is not supported now.MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.onnxruntime::MatMulComputeHelper::Compute::::operator ()IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)IsScalarOr1ElementVector(a_zero_point)onnxruntime::MatMulInteger::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccMatmulInteger : B zero point is not validQLinearMatmul : 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)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)onnxruntime::QLinearMatMul::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.ccQLinearMatmul : 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 : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)*Conversion Error:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleStringNormalizerConversion ErrorInput dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 charsp.secondStopword contains invalid utf8 charswstr != wconv_errorSingle dimension value must be greater than 0stopwordsEmpty stopwords not allowed!sw.empty()Duplicate stopwords not allowedUPPERNONEattribute case_change_action has invalid valuelocaleonnxruntime::StringNormalizer::StringNormalizercase_change_actionattribute case_change_action is not setLOWERD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USis_case_sensitiveattribute is_case_sensitive is not setbad conversionResize: input tensor's rank does not match the output tensor's rank.onnxruntime::UpsampleBase::ParseSizesDataUpsample: input shape needs to be at least a single dimension.Upsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionCannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeResize: input/output value is nullptraxes should be less than output_dims.size() Output dim value: Input dim value: Dimension: Invalid roi input index.onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.onnxruntime::Upsample::BaseComputeResize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeResize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.onnxruntime::UpsampleNearest <= status.IsOK() && !impl_->ngram_counts_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: impl_->max_skip_count_ >= 0max_skip_count must be non-negative: Non-empty ngram_counts is requiredngram_countsimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: max_skip_count is requiredmax_skip_countimpl_->min_gram_length_ > 0Required min_gram_length must be positive: >= max_gram_lengthimpl_->weighting_criteria_ != kNonemode: min_gram_length is requiredmin_gram_lengthTF is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFmode is requiredTfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizerNumber of items must compose whole end_idx >= start_idx && end_idx <= total_itemsInput shape must have either [C] or [B,C] dimensions with B > 0.(items % ngram_size == 0)status.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not providedn-gram counts out of bounds for -gramspool_strings must not be empty if specifiedpool_stringspool_int64s!pool_strings.empty() but ngram_indexes size: must be of equal sizeimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: Negative ngram_indexes values are not allowedstatus.IsOK() && !impl_->ngram_indexes_.empty()weightsstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: Non-empty ngram_indexes is requiredngram_indexesDuplicate ngram detected, size: id: onnxruntime::ngram_details::PopulateGramsp.first->second->id_ == 0op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()lambdop_kernel_info.GetAttr("bias", &bias_temp).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::ShrinkEyeLikeEyeLike : Input tensor dimension is not 2t_proto_p->dims()[0] == 1Must have a single dimension of 1Must have a single dimensionD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims_size() == 1Unsupported output datatype with size: D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.onnxruntime::`anonymous-namespace'::ConstantOfShape::Computeutils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: Unsupported value attribute datatype: ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProto!utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was p_mlvalueonnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was sequence_lens length of did not match batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Compute but has batch size of D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Initonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsScan<8> spec does not support transpose of output. This should never be called.roi_batch_id >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id < batch_sizeMaxRoiPoolonnxruntime::RoiPool::ComputeR->Shape()[1] == 5pooled_shapeonnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_shape.size() == 2D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_width_ > 0pooled_height_ > 0LpNormalizationonnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK() vs. Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: onnxruntime::InstanceNorm::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInstanceNormalizationInvalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::Multinomialonnxruntime::RandomUniformLike::RandomUniformLikesample_sizelowinfo.GetAttr("low", &low_).IsOK()info.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformonnxruntime::RandomNormalLike::RandomNormalLikehighONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::RandomNormal::RandomNormalD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hmeaninfo.GetAttr("mean", &mean_).IsOK()Output type not supported in this build: num_samples is < 1Invalid data type of batch_size is < 1num_classes is < 1onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::Computeonnxruntime::MultinomialComputeonnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.ccSUMSOFTMAX_ZEROLOGISTICSOFTMAXTreeEnsembleRegressorInput shape needs to be at least a single dimension.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon::computebase_valuesaggregate_functionnodes_falsenodeidsn_targetsnodes_hitratesnodes_featureidsnodes_modesnodes_missing_value_tracks_truenodes_treeidsnodes_nodeidsnodes_valuesnodes_truenodeidstarget_idspost_transformtarget_treeidstarget_nodeidstarget_weights_as_tensortarget_weightsnodes_hitrates_as_tensornodes_values_as_tensoronnxruntime::ml::detail::TreeEnsembleCommon::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.h-base_values_as_tensor but input tensor has One path in the graph requests feature onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg features. (weights).TreeEnsemble only works on 1D, 2D tensors.One falsenode is pointing either to itself, either to another tree. (falsenode). (truenode).Unable to find node in tree Node base_values.empty() is already there.nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggTreeEnsembleClassifierX dims is empty.onnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclass_nodeidsclass_idsclass_weightsclass_treeidsclasslabels_int64sclasslabels_stringsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclass_weights_as_tensoronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binarydefault_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsvalues_int64skeys_int64sdefault_floatvalues_floatskeys_strings_Unusedvalues_stringskeys_floatsInput of tensor(int64) must have output of tensor(string)Input of tensor(string) must have output of tensor(int64)LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()However, the number of key is and the number of values is info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. Input features_per_batch[] != number of classlabels[Zipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.ZipMaponnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramskernel_typePOLYLINEARSVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hUnexpected mode:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::Computenum_features == feature_count_one_class!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()coefficientsonnxruntime::ml::SVMRegressor::SVMRegressorinfo.GetAttrs("rho", rho_).IsOK()rhosupport_vectorsn_supportsUnsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classSVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hUnexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)ScalerEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()Scale size: () != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetUnknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64sD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hMAXInvalid normalize value of onnxruntime::ml::MakeNormalizenorminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of LinearRegressorinterceptstargetsinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.size() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of shape_size == out.size()onnxruntime::ml::CastInputToFloatMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeImputerimputed_value_floatsimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedEmpty value of imputed values.Empty input dimensions.input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer) does not match number of inputdimensions values (Number of inputs (DictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string stof argument out of rangeinvalid stof argument%fTO_FLOATTO_INT64TO_STRINGInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSEonnxruntime::ml::MakeCast Expected DENSE or SPARSESPARSEonnxruntime::ml::MakePackInvalid PACK_MAP value of info.GetAttr("cast_to", &attr).IsOK()cast_toD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapinfo.GetAttr("map_form", &attr).IsOK()map_forminfo.GetAttr("max_map", &max_map_).IsOK()max_mapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSE Expected std::map or std::mapCastMap0.fInvalid input type of value: Unexpected CAST_TO value of cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.cconnxruntime::ml::CastMap::ComputeImpl is NaNInput data with index: BinarizerthresholdArrayFeatureExtractorInvalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.] () >=Invalid Y argument: index is out of range: Y[onnxruntime::LayerNormImpl::LayerNormImplop_kernel_info.GetAttr("axis", &axis_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cc and bias size of Size of X.shape()[axis:] == . Size of scale and bias (if provided) must match this. Got scale size of onnxruntime::`anonymous-namespace'::ComputeImpl inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoNumber of entries in '' was onnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesInvalid values in ''. 0 == forward. 1 == reverse. did not.validonnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Unsupported typeInput X must have 3 dimensions only. Actual:}. Actual:Input B must have shape {Input sequence_lens must have shape {Input W must have shape {Input R must have shape {affinereluInvalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {scaledtanhhardsigmoidleakyreluthresholdedrelusoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got elusoftsignA + (M * K) <= A_endweights.quant_para_onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameQuantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInput initial_c must have shape {Input P must have shape {D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.cconnxruntime::LSTMBase::ComputeImplSoftmaxCPU inputs N, D and N * D must be < left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputs Right shape: Ranks of pair-wise operands must be equal. Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()left_dim == right_dimEinsum op: Input shapes do not aligncandidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutput Left shape override: left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessLeft shape: The override dims are not compatible with given tensor's shape. Right shape: right.Shape().Size() == right_shape_override.Size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Found '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputs. The shape is: is incompatible in the dimension in the same dimensionAnother operand has a dim value of Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input dim_iter == rank...onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersInputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains letters not seen in the inputsNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: onnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal The innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal for input shape Cannot parse the diagonal elements along dims onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulData types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: Inverseonnxruntime::contrib::SkipLayerNorm::SkipLayerNormepsilon_ >= 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNormLast dimension of gamma and input does not matchgamma is expected to have 1 dimension, got skip is expected to have same shape as inputinput is expected to have 3 dimensions, got Last dimension of bias and input does not matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchbeta is expected to have 1 dimension, got Scaleinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerinfo.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerDynamicSliceAttribute border needs to be specified with four border elements, got input dimensions instead) + bottomBorder (Input's width () + scale_[0] () + scale_[1] (Crop) needs to be greater than or equal to the topBorder (Input's height () + rightBorder () needs to be greater than or equal to the leftBorder (onnxruntime::contrib::Crop::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.hmin_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_dataonnxruntime::contrib::BifurcationDetector::BifurcationDetectorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_size_ > 0max_ngram_sizeonnxruntime::contrib::BifurcationDetector::Computepred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)BifurcationDetectorD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()ngram_sizeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockTidNGramRepeatBlockinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computescores_dims.size() == 2scores_dims[0] == batch_sizeonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::ComputeCDistThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist::CDistmetricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistCropAndResize specified. It should be either bilinear or nearestD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeTransposeMatMulpositivesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeinput_num_bytes % 4 == 0keysMurmurHash3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::ComputeSparseToDenseMatMulExpecting COO 2-D indices shapeCan not multiply A and B as inner dimension does not match. inner_A: Expecting the same number NNZ == size of Inner indicesExpecting 2xValues == indicesonnxruntime::contrib::SparseToDenseMatMul::ComputeCurrently supporting only 2-D matrices vs inner_B: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccCurrently support only COO and CSR(x64) formatsOuter size must be M + 1COO indices must be 2-D, got: is out of bounds of lhs_right: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator () is out of bounds of out_left: COO k index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()COO m index: Conv kernal size 2 : Char embedding size does not match conv kernal size 2. char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute . Char embedding size: conv kernal size 1: conv_window_size attribute: WordConvEmbeddingonnxruntime::contrib::WordConvEmbedding::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccconv_window_sizechar_embedding_sizeembedding_size embedding_size attribute: Conv filter size does not match embedding_size attribute. conv filter size: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerTokenizermarktokenexpEither one of the separators OR tokenexp attributes required but none is setmincharnum_ > 0separatorsattribute mincharnum is not setattribute mincharnum must have a positive valueattribute pad_value is not setmincharnumCan not digest separators: Can not digest tokenexp: mincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2separators must not be empty!separators.empty()Expecting a non-empty tokenexp!tokenexp.empty()Input dimensions are either [C] or [N][C] allowedtensor(string) expected as inputInput string contains invalid utf8 chars: Match contains invalid utf8 chars: Input string contains invalid utf8 charsonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpAttention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3Attention query layer weight shape error! Expected:{}. Got: Attention memory layer weight shape error! Expected:{}, Got: Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got: found!], while Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention v weight shape error! Expected:{, aw_attn_size}. Got:onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplinit_decoderdecoderencoderinfo.GetAttr("encoder", &proto).IsOK()onnxruntime::contrib::transformers::Sampling::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.ccSamplingparameters_.model_type == IGenerationParameters::kModelTypeGptdecoder_session_stateonnxruntime::contrib::transformers::Sampling::ComputeNot ImplementedSubgraph SessionState was not found for 'decoder' attribute.onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfoinit_run_gpt_subgraph_ == nullptrinfo.GetAttr("decoder", &proto).IsOK()gpt_subgraph_ == nullptrinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_init_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesdecoder_feeds_fetches_manager_init_run_decoder_session_stateNode input is required should be a scalar. Got shape of D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.hmax_lengthD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializemin_lengthonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenInput 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'input_ids' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have same shape as input_idsInput 'presence_mask' is expected to have 2 dimensions, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeonnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.ccGreedySearchonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()]. Axis is D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.ExpandDimsaxis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseinput index out of rangeonnxruntime::contrib::EmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::Initparameters_.model_type == IGenerationParameters::kModelTypeGpt || parameters_.model_type == IGenerationParameters::kModelTypeT5BeamSearchencoder_session_stateSubgraph SessionState was not found for 'encoder' attribute.onnxruntime::contrib::transformers::BeamSearch::Computet5_decoder_subgraph_ == nullptrEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availableEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyt5_encoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoencoder_feeds_fetches_manager_D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.honnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputs'num_return_sequences' has to be smaller or equal to 'num_beams'.length_penaltynum_return_sequencesnum_beamsonnxruntime::contrib::transformers::BeamSearchT5::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Executeparameters->sequence_length == 1qkv_hidden_sizesmask_filter_valueD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.honnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0past_present_share_bufferD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttention4D mask in attention cpu kernel is not supportedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::Attention::ComputeSampleOpy_scale == nullptr || IsScalarOr1ElementVector(y_scale)TATBTCTYZTYb_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 1D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsQGemm : 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 NSegment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1onnxruntime::contrib::QEmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::NhwcMaxPool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccIsScalarOr1ElementVector(a_zero_point_tensor)onnxruntime::contrib::MatMulIntegerToFloat::Computeonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.Input onnxruntime::contrib::DynamicQuantizeLSTM::PrePackR_scaleWeight zero point must be zeroDynamicQuantizeLSTM : Weight point must be constantRecurrentD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cc} for per-channel quantization. Actual:, 4*} for per-tensor/layer quantization or shape { must have shape {W_zero_pointR_zero_pointW_scaleinput zero point must be a scalar or 1D tensor of size 1.onnxruntime::contrib::QAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1QLinearMulQLinearAddMatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.ccMatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)onnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13onnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxQLinearSoftmaxQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedtensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)opset must be existed in attributes of QlinearSoftmaxD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.ccQLinearSigmoidQLinearLeakyReluInput x_scale must be a scalar or 1D tensor of size 1QLinear Pooling unsupported pooling size!tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::contrib::QLinearAveragePool::ComputeImplIsScalarOr1ElementVector(tensor_x_scale)input x_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput y_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale) inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)input_def_count == kExpected_input_countThere must be Input scale is not float for quantized output z @ 7tensor_y_scale->IsDataType()Wrong input type encountered for zero point of quantized input @tensor_z_scale->IsDataType()QLinearWhereD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.cconnxruntime::contrib::QLinearWhere::QLinearWheretensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Wrong input type encountered for zero point input def of x, y, ztensor_x_scale->IsDataType()Input scale is not float for quantized input x @ 2Input scale is not float for quantized input y @ 5onnxruntime::contrib::QLinearWhere::Compute Each input must be (tensor, scale, zero_point) tuple!onnxruntime::contrib::QLinearConcat::QLinearConcatinput_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.cctensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @onnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 3 && input_count_x3 % 3 == 0QLinearConcatInput scale is not float for quantized input @onnxruntime::contrib::QLinearGlobalAveragePool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)onnxruntime::contrib::MatMulInteger16::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels_last", &channels_last_).IsOK()invalid channel countchannels_ > 0info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputtransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode '' for NCHWc UpsampleUnsupported transformation mode 'D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0X_rank == 4onnxruntime::contrib::ReorderInput::Computeonnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Computeonnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.(for attribute 'Unexpected type (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cc'.n_elements > 0proto.data_type() == proto_type' has one dimension but is empty.' is specified but is empty.Attribute:'n_dims == 1onnxruntime::ml::GetNumberOfElementsAttrsOrDefault' must be a vector.Attribute 'onnxruntime::ml::GetVectorAttrsOrDefaultInput 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got rows[.. row[ [seqno=onnxruntime::contrib::BahdanauAttention::PrepareMemorymem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_not support normalize yet.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.cconnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processprefix.size() == narrow(prefix_length)pad_token_ideos_token_idmodel_typetop_ptemperatureno_repeat_ngram_sizedecoder_start_token_idD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.cccustompresence_penaltymin_tokens_to_keepfilter_valueonnxruntime::contrib::transformers::SamplingParameters::ParseFromInputsseed >= 0Seed must be >= 0vocab_sizelogitssubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 0 shall have length of 2subgraph logits output is expected to have 3 dimension, got subgraph input 1 shall be named as present_0, got: present_0subgraph output 0 shall be named as logits, got: attention_masksubgraph input 1 shall be named as position_ids, got: position_idssubgraph input 0 shall be named as input_ids, got: subgraph past state is expected to have 5 dimension, got subgraph input 3 shall be named as past_0, got: past_0subgraph input 2 shall be named as attention_mask, got: subgraph input 2 (attention_mask) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 0 (input_ids) shall have int32 typesubgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 0 (logits) shall be float or float16 data typeinput_ids_shape.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.cconnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedssession_state_ != nullptrinput_idsInvalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer)onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).Setup must be called before CreateInitialFeedsonnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsBeamSearch op: An implementation for the input type onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputselement_type == DataTypeImpl::GetType()onnxruntime::contrib::SamplingCpuHelper::SampleD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.hlast_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitslogits_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedspast_sequence_lengthonnxruntime::contrib::transformers::Subgraph::SetupD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.cconnxruntime::contrib::transformers::Subgraph::GetParameterssubgraph output present_key_self_0 is expected to have 4 dimension, got max_length (max_length > sequence_length) shall be no more than max_length <= kMaxSequenceLengthD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.ccinput_ids shall have 2 dimensions. Got dims.size() == 2) shall be greater than input sequence length (context != nullptronnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputsrepetition_penalty shall be greater than 0, got repetition_penalty > 0.0fearly_stoppingonnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.cc) shall be be no more than num_beams (num_return_sequences (num_beams >= num_return_sequencesnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got num_return_sequences >= 1unimplemented activation: activation_params count mismatchgamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got Input 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got input_ids and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeInput 0 and 1 shall have same shapehypothesis_buffer_offset_ <= hypothesis_buffer_length_sequences != nullptronnxruntime::contrib::transformers::BeamSearchScorer::Finalizeoutput_sequences != nullptrnext_scores.size() == next_indices.size()Batch can only be done if all beams have been generatedbeam_hyp.Size() >= gsl::narrow_cast(num_beams_)beam_idx == num_beams_next_beam_scores_.empty()onnxruntime::contrib::transformers::BeamSearchScorer::Initializenext_scores.size() == next_tokens.size()onnxruntime::contrib::transformers::BeamSearchScorer::Processtop_k <= Size()onnxruntime::contrib::transformers::BeamHypotheses::OutputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.ccdecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph output shall have same data type as that of encoder_hidden_statesonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsInvalid hidden_states expension: has_hidden_state_ == falsedecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph input 0 shall be named as input_ids, got: encoder_attention_maskdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: onnxruntime::contrib::transformers::T5DecoderSubgraph::ValidateD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.ccnumber of outputs expected to be kFirstPastInputIndex + 4 * layers, got:number of outputs expected to be 1 + 2 * layers, got:encoder_hidden_stateskFirstPastInputIndex currently only supports 2 or 3expect 3 inputs, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.cconnxruntime::contrib::transformers::T5EncoderSubgraph::Validateencoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typepresent_key_self_0encoder subgraph output 1 shall be named encoder_hidden_states, got: present_value_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: decoder_input_idsencoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: number of outputs expected to be 2 + 4 * layers, got:expect >=6 outputs, got:encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder_input_idsencoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph output 0 (logits) shall be float or float16 data typecpu_allocator shouldn't be nullptronnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsqkv_hidden_sizes attribute should have 3 elementsInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes first element should be same as the secondhidden_size should be divisible by num_heads:Input 'bias' is expected to have 1 dimension, got Input 'input' is expected to have 3 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'weights' is expected to have 2 dimensions, got Attention cannot have both past and extra_add_qkwhen past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length past_sequence_length tensor must be of one element when past_present_share_buffer is setInput 'extra_add_qk' dimension 0 should be same as batch_size, got Input 'extra_add_qk' is expected to have 4 dimensions, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 2 shall have length of Inputs 'past' dimension 2 shall have length of num_headsInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes: q_hidden_size=Input 'past' is expected to have 5 dimension, got Input 'past' expect k_hidden_size == v_hidden_sizebias_dims[0]=Hidden size of Q, K and V shall be same k_hidden_size= v_hidden_size=D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.cconnxruntime::contrib::AttentionBase::GetPresentInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseExpect to have present state output when past state input is givenInputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInput 'extra_add_qk' dimension 2 should be same as sequence_length, got Input 'extra_add_qk' dimension 1 should be same as number of heads, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInput 'extra_add_qk' dimension 3 should be same as total_sequence_length, got QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.ccQlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QlinearBuildLookupTable is not supportedTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType.OpenVINO_GPUDMLCudaCudaPinnedD:\a\_work\1\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Specified device is not supported.Unsupported indices_format passedD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.cc`anonymous-namespace'::GetIndicesTensorthe ort_value must contain a constructed tensor or sparse tensorNot implementedOrtApis::GetTensorTypeAndShapeArgument is not a tensortype_proto is not of type sequence!type_proto is not of type map!Tensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Initonnxruntime::Tensor::Tensortensor size overflowtensor failed memory size calculationp_type != nullptrD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0strides.empty()onnxruntime::Tensor::CalculateTensorStorageSizeStrided tensor is supported for training only for now.onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopyTensoronnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::SetD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptrOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::IsCompatibleD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccvalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::Setexpected a registered ONNX typeInvalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker is not currently registered or supportedMLDataType for: sparse tensor type (unknown type)tensor type float16bfloat16int64uint64thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleonnxruntime::OptionalTypeBase::IsCompatibleutils::HasElemType(thisProto->optional_type())utils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kOptionalTypethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())utils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypethisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleonnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: int32uint32int16uint16int8uint8booldouble(null)floatonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatiblethe ort_value must contain a constructed sparse tensoronnxruntime::SparseTensor::GetSparseTensorFromOrtValueonnxruntime::`anonymous-namespace'::CopyDataD:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cc dst_size: Must have the same size. Got src_size: onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting indices to have 2-D shape . Got: Expecting one index. Got: Expecting to have at lest 3-D shape. Got:Format() == SparseFormat::kBlockSparseonnxruntime::SparseTensor::AsBlockSparseonnxruntime::SparseTensor::MakeCsrStringsMust contain BlockSparse format. Got: Use MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataThis method does not expect allocator to be setonnxruntime::SparseTensor::UseCsrIndices rows: Outer index count must be rows + 1 or zero. Got: the same as values size: Expecting inner index size: Destination must have a CPU allocator setX-device copy of strings not supportedThis instance should not be emptyDestination should be emptyUnable to find a data transfer for copying from device type: onnxruntime::SparseTensor::Copyonnxruntime::SparseTensor::MakeBlockSparseStrings to device type: Use MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataExpecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::UseBlockSparseIndicesExpecting index blocks: Expecting fully sparse tensors to have value shape {0}Indices shape must have dim[0] == 2 to be equal to values blocks: Sparse format must not be set. Already contains format: onnxruntime::SparseTensor::UseCooIndicesvalues_count == index_sizeonnxruntime::SparseTensor::GetCooIndexDims must be equal to or twice the values size: Index size: Expecting to contain one index, got: format_data_.size() == 1UFormat() == SparseFormat::kCooonnxruntime::SparseTensor::AsCooSparseTensor Allocation failed for size: Must contain Coo format. Got: Values size onnxruntime::SparseTensor::AllocateBufferthis tensor already has populated sparse_indices must be less than total buffer size: onnxruntime::SparseTensor::ValidateCsrIndicesInner and Outer indices must either be both zero or non-zeroformat_data_.size() == 2Udense shape must 2-D. Got: onnxruntime::SparseTensor::AsCsrExpecting two indices. Got: Must contain Csr format. Contains: Format() == SparseFormat::kCsrcThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptronnxruntime::SparseTensor::MakeCooStringsFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooDataExpecting data type to be set as stringNot expecting an allocator setUse MakeCooStringsSrc and Dst must be of the same typeMust have the same shape{}dimstart <= dimend && dimend <= values_.size()onnxruntime::TensorShape::Sliceonnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.onnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dims dimensions. for SizeFromDimension. Tensor has D:\a\_work\1\s\onnxruntime\core\framework\config_options.ccConfig with key [onnxruntime::ConfigOptions::AddConfigEntry]. It will be overwritten] already exists with value [Config key is empty or longer than maximum length 128Config value is longer than maximum length: init_num == values.size()onnxruntime::SessionOptions::AddExternalInitializersAn OrtValue for this name has already been added: Expecting same size spansonnxruntime::SessionOptions::AddInitializerD:\a\_work\1\s\onnxruntime\core\framework\session_options.ccReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.Received nullptr for nameReceived nullptr for OrtValueAttribute name and type don't matchNo attribute with this name is defined.Attribute: but is of type: expected to be of type: is defined.No attribute with name: Requested attribute: is expected to have type: D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_Invalid arg_num of . Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was nullonnxruntime::OpKernelContext::GetOrCreateOutputMLValueCPU allocator not foundTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()D:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocator type: */_ORT_MEM_ADDR_/*Unsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoonnxruntime::utils::TensorProtoToMLValueThe preallocated buffer is too small. Requires , Got string tensor can not use pre-allocated bufferTensorProtoToMLValue() must take a pre-allocated MemBuffer!Initialized tensor with unexpected type: tensor can't contain negative dimsstring tensor can not have raw dataonnxruntime::utils::TensorProtoToTensorTensorProto type can not be writen into Tensor type TensorProtoToTensor() tensor shape mismatch!External initializer: HasDataType(dense_proto)Must have a valid data typeUnsupported sparse tensor data type of Element_size of: is not supported.onnxruntime::utils::SparseTensorProtoToDenseTensorProtoInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == indices_data.end()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorSparse indices int32 data size does not match expectedSparse indices int64 data size does not match expectedonnxruntime::utils::CopySparseDataSparse Indices raw data size does not match expected.`anonymous-namespace'::ReadExternalDataForTensorTensorProto: external data size mismatch. Computed size: , external_data.length: External data type cannot be UNDEFINED or STRING.D:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc`anonymous-namespace'::GetExternalDataInfoTensor does not have external data to read from.UnpackTensor: the pre-allocated size does not match the raw data size, expected offset: size to read: given file_length: are out of bounds or can not be read in full.GetFileLength for onnxruntime::utils::GetExtDataFromTensorProtoutils::HasExternalData(tensor_proto)onnxruntime::utils::GetFileContentdata overflowUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size() does not match the data size() in protoonnxruntime::utils::UnpackTensorWithExternalDataImplnullptr == p_dataInvalid TensorProtoUnsupported type: onnxruntime::utils::UnpackInitializerData data_type: onnxruntime::utils::DenseTensorToSparseTensorProtoD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cconnxruntime::KernelDefBuilder::VariadicAliasinput_offset >= 0 && output_offset >= 0 node_version: kernel start version: kernel_end_version: type_proto != nullptrstatic_cast(first_arg_idx) < actual_inputs.size()D:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cconnxruntime::`anonymous-namespace'::MatchKernelDefTypes but the node in the model has the following type (),This op has been implemented only for the following types (Failed to add kernel for : Conflicting with a registered kernel with op versions.kernel def can't be NULLKernel not found kernel is not supported in Encountered following errors: (Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. Op with name ( and type ( Version mismatch. because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeonnxruntime::GetCpuPreferredNodeskernel_info != nullptrD:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cconnxruntime::GetCpuPreferredNodes::::operator ()Candidate for fallback CPU execution: ORT optimization- Force fallback to CPU execution for node: Received invalid value of arena_extend_strategy onnxruntime::CreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cc New allocator: Existing allocator: device:Duplicate allocator for OrtMemType:onnxruntime::AllocatorManager::InsertAllocatormetadef_id_generator_onnxruntime::IExecutionProvider::GenerateMetaDefIdonnxruntime::IExecutionProvider::InsertAllocatorD:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatorInvalid index: onnxruntime::sparse_utils::DenseTensorToSparseCooUnable to convert strings tensor to a sparse tensor that not on CPUonnxruntime::sparse_utils::DenseTensorToSparseCsrD:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccUnsupported element size: Support 2-D matrices onlyInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUExpecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()onnxruntime::sparse_utils::SparseCsrToDenseTensorOuter indices must be M + 1. Got: outer_num == (rows + 1)Input must be of COO formatExpecting indices to be equal the number of values or be twice as manyonnxruntime::sparse_utils::SparseCooToDenseTensor > dense_size: Currently do not support dims higher than 2 dimensions: D:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopySparseTensorsdata_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsSNPEExecutionProviderinvalid allocator.Unsupported OrtValue type.Failed to find allocator for device allocator != nullptronnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.ccInternalTestingExecutionProvider while parent graph node running on device: Subgraph has nodes running on device: onnxruntime::utils::UpdateWithParentStreamonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphUnsupported OrtValue type to copy between device.onnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesfeed_streams.size() == num_feedsonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevices, this is not supported yet.Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while loading custom ops with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibonnxruntime::SessionState::CreateKernels. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:onnxruntime::KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node +allocator_for_caching.get() != nullptronnxruntime::SessionState::PrepackConstantInitializedTensors::::operator () doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be empty!op_type.empty() which is of op type: used in the node: session.disable_prepackingsession.node_partition_config_file. Ignoring allocator from Allocator already registered for onnxruntime::SessionState::SetupAllocatorsD:\a\_work\1\s\onnxruntime\core\framework\session_state.ccSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfoDone saving OrtValue mappings.onnxruntime::SessionState::PopulateKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::FinalizeSessionStateonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulatorsubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::AccumulateAllNestedSubgraphsInfoonnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::SessionState::FinalizeSessionStateImpl Attribute: Index:' OpType:Missing session state for subgraph. Node:'entry != node_to_subgraph_ss.second.cend()p_op_kernelnode_index_info_.has_value()onnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::CreateSubgraphSessionState) node with name 'Could not find an implementation for onnxruntime::VerifyEachNodeIsAssignedToAnEpImplonnxruntime::VerifyEachNodeIsAssignedToAnEpNode placements]. Number of nodes: All nodes placed on [ Node(s) placed on [ Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.Using cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container () and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMapping for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::DeviceStreamCollectionImpl::CleanUpD:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.ccidx < num_streams_onnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamstream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::GetStreambad variant accessFailed to find kernel for The node is not placed on any Execution Provider. found duplicated provider in KernelRegistryManager (node ). onnxruntime::utils::GetMLDataTypenullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccktsrD:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.cconnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferFailed to verify KernelTypeStrResolver flatbuffers data.onnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolveronnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping feeds: D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::PartitionOrtFormatModelImplonnxruntime::PartitionOnnxFormatModelonnxruntime::PartitionOrtFormatModelsingle_node_compute_func should have 1 element.onnxruntime::GraphPartitioner::PartitionNo provider specified.!provider_type_.empty()provider_type must be specified.D:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.honnxruntime::KernelLookup::KernelLookuponnxruntime::GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer. by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP.D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc was inserted using the NHWC format as requested by , but was not selectedNode ' with domain:The Function Style fusion is deprecated.onnxruntime::PartitionOnnxFormatModelImplonnxruntime::InlineNodes did not return correct number of compiled functionsD:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::CreateAPI VERSION Create state function failed. Return value:xp_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForentry != regions_.end()Could not find Region for: Could not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionFor initial_growth_chunk_size_bytes: memory limit: with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: onnxruntime::BFCArena::BFCArenaCreating BFCArena for bins of max chunk size D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizep_int >= base_intD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinc->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0!c->in_use()onnxruntime::BFCArena::FreeAndMaybeCoalesceonnxruntime::BFCArena::CoalesceExtending BFCArena for . bin_num:Failed to find a free memory block despite calling Extend. rounded_bytes=!chunk->in_use()!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtr The total allocated bytes is now onnxruntime::BFCArena::SplitChunk BFC Arena shrunk by bytes. onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::Shrinkonnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use() && c1->stream == c2->streamTotal allocated bytes: onnxruntime::BFCArena::ExtendAllocated memory at size: onnxruntime::BFCArena::ReserveReserving memory in BFCArena for h != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytes (requested) num_bytes: (actual) rounded_bytes:BinForSize(bin_size) == BinFromIndex(b)Creating BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)h < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)cudaMalloconnxruntime::BFCArena::ChunkFromHandleIncorrect arena extend strategy.hipMalloc is smaller than requested bytes of onnxruntime::BFCArena::Extend::::operator ()Failed to allocate memory for requested buffer of size Available memory of Extended allocation by bytes.. onnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrFailed to find op_id: '. If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.cc', but the existing argument with that formal parameter name has a different formal parameter type string.Failed to find args for kernel type string 'onnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()Kernel type string already exists for formal parameter name 'Op schema must be available.onnxruntime::KernelTypeStrResolver::RegisterOpSchemaonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasonnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaop_kernel_type_str_args is null.onnxruntime::KernelTypeStrResolver::SaveToOrtFormatop_id is null.op_kernel_type_str_args entry is null.Invalid ORT format model.kernel_type_str_args entry is null.kernel_type_str_args is null.args is null.kernel_type_str is null.Duplicate entry for kernel type str: args entry is null.onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrDuplicate entry for op id: Dw_{offset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndex Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::IExecutionFrame::IExecutionFrameD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccshape && sp_tensor.DenseShape() == *shapeinvalid index shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValuefetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::InitAllocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()Fetches vector passed to GetOutputs contains failed. Error:node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()Could not find OrtValue with idx 'onnxruntime::IExecutionFrame::GetMLValueD:\a\_work\1\s\onnxruntime\core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_TraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan does not match actual shape of Expected shape from model of Invalid allocation kind: for output failed: size=onnxruntime::ExecutionFrame::VerifyOutputSizesonnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::TraceFreeTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocate returned nullptrTrying to allocate memory for unused optional inputs/outputsbuffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame, fall back to default allocation behavior but the actually size is: Tensor shape cannot contain any negative valueonnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, block in memory pattern size is: For ort_value with index: Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer. Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::CreateD:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccCan't slice a non-tensor OrtValue. Type was ort_value.IsTensor(). Shape:Insufficient dimensions to slice on OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()Invalid dim0_offset of dim0_offset < dim0_sizegsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is onnxruntime::LibraryHandles::UnloadLibrariesD:\a\_work\1\s\onnxruntime\core\framework\library_handles.ccsource and destination buffer size mismatchonnxruntime::utils::detail::CopyLittleEndianD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccmodel format error! Need a key for the external data info failedparsing model format error! Need a value for the external data infolocationmodel format error!model format error! Missing 'location'lengthchecksumAll implicit inputs should have OrtValue instances by now. entry != nullptr does not.thread_scheduling_stats_kernel_timeinput_type_shapeoutput_type_shapeonnxruntime::ExecuteKernel' Status Message: _fence_afterAsync Kernel Support is not implemented yet. launch kernel with idx stream node. Name:'Non-zero status code returned while running Number of streams: onnxruntime::ExecuteThePlan]}":[onnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::SessionScope::SessionScopeD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cc{"Begin execution_fence_beforeSequentialExecutor::Executeop_nameparameter_sizeoutput_sizenode_indexactivation_sizeD:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cconnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:n >= 0 && static_cast(n) < ort_value_info_.size()invalid value index: against size onnxruntime::PlannerImpl::Indexonnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()) : devicesstreamsonnxruntime::DeviceBasedPartitioner::InitializePartitioner type is not DeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception during saving DeviceBasedPartitioner config: node_names_by_stream_.size() == device_types_.size()Number of streams does not equal to number of device types!Failed to create partitionerUse DeviceBasedPartition as defaultonnxruntime::IGraphPartitioner::CreateGraphPartitionerCaught exception when reading partition config file: execution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.device.Type()onnxruntime::PlannerImpl::BuildExecutionPlanep" in node-stream mapDeviceBasedPartitioneronnxruntime::PlannerImpl::CreatePlantrigger_point_it != node_to_trigger_points.end()onnxruntime::DeviceBasedPartitioner::PartitionGraphiter != node_stream_map.end()Failed to find node " as an input reused by as inputonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()!IsNonTensor(*node_output)Only tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStreamonnxruntime::PlannerImpl::ComputeSingleStreamReusePlanonnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::ComputeReuseCountCan not find the node onnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseShould not have entry in kernel create info with nullptr for kernel_defonnxruntime::PlannerImpl::ComputeValueLocation::::operator ()found_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location mapallocatorCan not find the execution provider onnxruntime::PlannerImpl::ComputeValueLocationp_kernel_defonnxruntime::PlannerImpl::GeneratePlanForWeightsHelperspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GetLocationForNodeInput\u%04xincomplete UTF-8 string; last byte: 0xinvalid UTF-8 byte at index : 0x{}":, ": { ], "bytes": [[ []null}],"subtype":{"bytes":["subtype": cannot use insert() with cannot use operator[] with a string argument with cannot use operator[] with a numeric argument with �type must be string, but is 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨::operator ()Cannot use user supplied initializer with name: () because the ORT planned memory location device entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensorsUsing user supplied initializer with name (Skipping entry for missing optional value at idx [Memory] SessionStateInitializer statically allocates bytes for onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()Done saving initialized tensorsDeserialize tensor failed.Failed memory size calculationsession.use_device_allocator_for_initializersInternal error. The preallocated buffer is too small. Requires DeserializeTensorProto() takes either pre-allocated buffer or an allocator!D:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::ExtDataTensorProtoToTensor ) is different from what is supplied (Failed to copy tensor to string tensor is not supported for copying between allocatorsonnxruntime::session_state_utils::DeserializeTensorProto input with name is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()GraphSubgraphonnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorUnsupported device allocator in the context of pre-packed weights caching: D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccFailed to get allocator for location: Internal error.D:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlanduplicated locationWeight buffer for initializer '' is not foundMem pattern for initializer is not foundonnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer '' failedD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncs not found.Can't use func with null ptrfunc info for node: already exist.current <= buffer_size_D:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.honnxruntime::MemPatternPlanner::TraceAllocation!using_counters_D:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.ccframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()onnxruntime::StreamExecutionContext::RecycleNodeInputs releasedidx < device_stream_map_->NumStreams()onnxruntime::StreamExecutionContext::GetDeviceStreamort value Exiting due to terminate flag being set to true.since == endonnxruntime::RunSincev >= 0onnxruntime::StreamExecutionContext::CountDownBarrier::SetD:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.h activate notification with index onnxruntime::ActivateNotificationStep::ExecuteActivateNotificationStep: activate notification with id: TriggerDownstreamStep: trigger downstream of trigger point: , count: D:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cc wait on Notification with id: WaitOnEPStep: wait on notification with id: Launch kernel with node id: Set a barrier with id: WaitOnEPStep.wait_handle is nullwait_handle_onnxruntime::WaitOnEPStep::Execute[TypeInferenceError] NoOpConstantALLOW_RELEASED_ONNX_OPSET_ONLYTypeProto must have shape for this to runshape != nullptronnxruntime::utils::GetShapeD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hLoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instancefbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()node_arg_name cannot be null], could not find NodeArg input index: onnxruntime::Node::LoadEdgesFromOrtFormatinput edgesoutput edgesNode::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index: Input=Type mismatch. Current=onnxruntime::NodeArg::UpdateTypeAndShape . Got: Error merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.ccTensor element type mismatch. Node [Serialization of fused function body is not currently supported, does not have the graph for key onnxruntime::Node::SaveToOrtFormatOptional Type mismatch. Expected: ] op_type [ , or sparse tensors. Falling back to lenient merge. target:' source:Output:Source and target must both be tensors , or optional typed entitiesNo Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: in node UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) Op (Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesInvalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous node.Invalid model. Node input 'Some nodes are not included in the topological sort, graph have a cycle.Argument mismatch when removing edge.onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer This is an invalid model. Subgraph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (Invalid node indexes specified when adding edge.node_arg) does not exist in the graph.This is an invalid model. Graph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.onnxruntime::Graph::Graph' Model is invalid.Duplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptrDuplicate initializer (dense, sparse or ConstantNode): 'This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter utils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: '. Please, fix your model.' the model will use the latest encountered initializeronnxruntime::Graph::ReplaceInitializedTensorImplTrying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!foundFailed to find existing initializer with name onnxruntime::Graph::SaveToOrtFormatgraph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()onnxruntime::Graph::InjectExternalInitializedTensorsReplaced external initializer: onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::Resolve) is required but not specified.) attribute (Error: Duplicate definition-site for (existing->second == &tensoronnxruntime::Graph::AddInitializedTensorsparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.AddInitializedTensor already has tensor with name ) does not have type information.This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type ) does not match expected type () of node () of output arg (Type Error: Type (This is an invalid model. In Node, onnxruntime::Graph::VerifyNodeAndOpMatch) is not a registered function/opFatal error: Type Error: Data in initializer ' does not match. Type Error: Shape of initializer , Error ) is invalid.) in node () of operator (' of input parameter (Node () does not have type information set by parent node.) input arg (This is an invalid model. Node (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) type inference failed) output arg (This is an invalid model. Type Error: Type ' in node () bound to different types () of Optype (Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.onnxruntime::Graph::SetInputs) : (("""onnxruntime::Graph::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()) -> ( must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodenullptr != func_meta_defRemoving initializer ''. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg 'onnxruntime::Graph::FinalizeFuseSubGraphdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Node has no function body and cannot be inlined.callnode.TryGetFunctionProto(inlined_fp)onnxruntime::Graph::CreateFusedSubGraphNode OpType:Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)onnxruntime::Graph::RemoveNodeFailed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProto_token_ as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0Cannot find NodeArgs for [initializer_node_arg != nullptronnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is not used by any node and should be removed from the model.'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternalindex < data_.size()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.habsl::container_internal::raw_hash_map<>::atDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cc, but it its version is higheronnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker., but it its domain is notthan the operator set version D:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.cconnxruntime::GraphViewerToProtoMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: nodeInvalid ExecutionOrderonnxruntime::GraphViewer::GraphViewerD:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Input not found:nodeargonnxruntime::GraphViewer::GetNodesInTopologicalOrderIndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptrGraph is null. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatNo graph was found in the protobuf.Failed to load model with error: Unsupported model IR version: , max supported IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. is less than 0. less than 0.Protobuf serialization failed.onnxruntime::Model::Saveonnxruntime::Model::LoadProtobuf parsing failed. is till opset D:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet are '0' and '1'. The environment variable contained the value: D:\a\_work\1\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelMissing model IR version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. 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 onnxruntime::model_load_utils::ValidateOpsetForDomainModelProto does not have a graph.onnxruntime::SaveModelsystem error number Load model failed. File doesn't exist{Output tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last. should specify a shape expected to have tensor typeNegative values are not allowed in a shape specificationAttribute expected to have tensor or sparse typeOutput D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccConstrain input and output types to float tensors.Value of betaValue of alphaA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).The scale to apply.Bias applied to each channel, same size as C.) + scale[1] (If 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Scaling valuehiddenGRUUnit) + right_border () needs to be greater than or equal to the top_border () + scale[0] () needs to be greater than or equal to the left_border ('Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D) + bottom_border ('Scale' must contain exactly 2 values - (height, width)Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.valuesGivenTensorFillgatesseq_lengthsdrop_stateshidden_prevdataThreshold valueinput_as_shapeConstrain indices to integer typesextra_shapeConstrain input and output types to all tensor types.Attribute expected to have tensor or sparse tensor typeextra_addpastquerykeyConstrain mask index to integer typespresentD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccInputs 2 (value) shall be 3 dimensionsWhether every token can only attend to previous tokens. Default value is 0.Number of attention headsinput shall be 2 dimensionsinput shall be 3 dimensionsInputs 0 (query) shall be 3 dimensionstoken_offset shall be 2 dimensionsMCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCustom scale will be used if specified. Default value is 1/sqrt(head_size)The value to be filled in the attention mask. Default value is -10000.0fError unexpected extra input in node:Error parsing node:key and value cache shall be 4 dimensions = Constant()segment_idsDecoderAttentionConstrain key_padding_mask to bool tensors.The epsilon value to use to avoid division by zero.word_embeddingsegment_embeddingposition_embeddingstatic_kvuse_pastkey_cachevalue_cachenew_key_cacheConstrain input and output types to float and float16 tensors.new_value_cachehas_layer_statehas_key_padding_maskglobalLongformerAttentionConstrain to integer typesglobal_biasglobal_weightGq_weightkv_weightConstrain mask to integer typesConstrain input and output to float tensors.MultiHeadAttentionkey_padding_maskweightmaskwindowOne sided attention windows length W, or half of total window lengthConstrain scores input and output types to float tensors.scores_outThe minimum NGram size for suffix matching.scorescur_tokensprev_suffix_match_idxThe maximum NGram size for suffix matching.src_tokensskipSkipSimplifiedLayerNormalizationThe NGram size.inv_std_varConstrain mean and inv_std_var to float tensors.input_skip_bias_sumis_bidirectionalDefault value is 0.bias_tableConstrain input and output types to float or half tensors.max_distanceMax distanceConstrain sequence_length to int tensors.RelativePositionBiasquery_lengthkey_lengthConstrain input and output float tensors types.baConstrain input and output integer tensors typesembedding_sum T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) oneX_bias = Identity (X)X_bias = Add (X, bias)RemovePaddingConstrain sequence_token_count and token_offset to integer typescumulated_seq_lenmax_seq_lenRestorePaddingConstrain token_offset to integer typesConstrain to integer types.suffix_match_idxWpred_tokenstokenssequence_token_counttoken_offsetGemmFastGelu̼+̼+C expected to have rank > but has rank Dimension mismatch in unification between expected to have rank Incompatible dimensions expected to have tensor or sparse tensor type: is nulltrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1apply softmax to elements for dimensions axis or higherConstrain input and output types to all numeric tensors and bool tensors.ratio(Optional) Seed to the random generator, if not specified we will auto generate one.residualThe inner-most 2 dimensions must have the same size (mat_w: != mat_h:TorchEmbeddingConstrain input and output types to all numeric tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.padding_idxscale_grad_by_freqD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cc CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) One CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) AlphaInput rank must be >= 2.Alpha value.AFailed to parse max_length or it is not positive integer scalarInputs 0 shall be 2 dimensionsFailed to parse num_return_sequences or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Incompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).The subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartsequencessequences_scoresMinimumber of tokens we keep per batch example in the output.All filtered values will be set to this float value.If 1 custom sampling logicPresence penalty for custom samplingIf set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.The value used to module the next token probabilities.Decoder subgraph to execute in a loop.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsSize of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapevocab_maskprefix_vocab_maskrepetition_penaltyGridConstrain output types to float tensors.Constrain input types to all tensor types.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.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.early stop or notno repeat ngrams sizeThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.model type: 0 for GPT-2; 1 for encoder decoder like T5The id of the end-of-sequence tokenThe id of the token that indicates decoding starts.The id of the padding tokenisinf_onlyConstrain output 'mask' types to uint32 tensors.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.isnan_onlyConstrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.Constrain input 'training_mode' types to boolean tensors.Three interpolation modes: bilinear (default), nearest and bicubic.If true, check only for Inf, -Inf.IsAllFiniteConstrain the output to a boolean tensor.If true, check only for NaN.Constrain input B data types as 16-bit integer tensorConstrain input A 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).Whether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationScalar multiplier for the product of the input tensors.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Input axis is invalid: activation_gammaan optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.inputs are expected to have tensor type and output type should not be null.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredInput/Output is a string tensorThe string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).ZSecond input does not have rank 2First input does not have rank 2ComplexMulConjComplexMulConstrain input and output types to float tensorsPadsConstrain input and output types to float/int tensors.Whether B should be transposedWhether A should be transposedfiltered_logitsConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Model type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartpresence_maskIrfftRfftConstrain input0 and output types to float tensorsnormalizedsignal_ndimConstrains input to only numeric types.rois input tensor has wrong dimensionfirst input tensor has wrong dimensionThe 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".roiscrop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionValue used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Three 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 arrayidxInput can be of any tensor type.countsxConstrain indice type to int32 or int64This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightConstrain input and output types to any tensor type.Constrain to tensor(float).Constrain to tensor(int32).Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Sequencesparse_tensor(uint64)sparse_tensor(int32)sparse_tensor(uint32)sparse_tensor(float)sparse_tensor(int64)sparse_tensor(double)If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.InvStdDevtype used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).EfficientNMS_TRTConstrain output Y and scale type to float tensors.boxesType of Mean and InvStdDev tensors.Constrain input X type to float tensors.Constrain mean and inv_std_var to be float tensors.Constrain output Y, scale and bias type to float tensors.InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)max_output_boxesBiased = Identity (Scaled)Biased = Add (Scaled, B2D)InvStdDev2D = Reciprocal (StdDev)Y = Reshape (Biased, XShape)Input 0 shall be 3 dimensionsAttribute 'max_output_boxes' must be >= 1.Attribute 'pooled_size' must be >= 1.pooled_sizeMeanOfSquare = ReduceMean (Square)Square = Mul (XU, XU)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean2D, Mean2D)X2D = Flatten (X)ReducedShape = Concat (PrefixShape, SuffixShape)Mean2D = ReduceMean (XU)XU = Cast (X2D)Scale2D = Flatten (Scale)NormalizedV = Cast (Normalized)B2D = Flatten (B)Scaled = Mul (NormalizedV, Scale2D)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)Normalized = Div (Deviation, StdDev)Deviation = Sub (XU, Mean2D)Constrain types to int tensors.Constrain types to float tensors.Epsilonbatch_indicescrop_sizeNumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)NumReducedAxes = Neg (Axis1D)Rank = Size (XShape)XShape = Shape (X)Axis1D = Constant()Zero1D = Constant()DLCpayload of the SNPE DLC file.spanMaximum relative distance, k.factorScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.notes(Optional) Some notes for the modelinputssnpe_version(Optional) SNPE version used to convert the model.target_device(Optional) Target device like CPU, DSP, etc.Pooled size.image_sizeDisentangledAttention_TRTPyramidROIAlign_TRTfeature_map_3Image size.patchesp2c_attentiondisentangled_attentionc2c_attentionc2p_attentionscore_activationActivation function to apply to the scores input.score_thresholdScore threshold value.Max detections to output.iou_thresholdplugin_versionVersion number of the TRT plugin.feature_map_1feature_map_2MultilevelCropAndResize_TRTfeature_map_0num_detectionsdetection_boxesanchorsEncoding type for the boxes or anchors inputs.background_classBox IOU threshold value.box_codingdetection_scoresBackground class ID.detection_classesConstrain output 'mask' types to bit-packed uint32 tensor.Constrain 'training_mode' to boolean tensor.outputsConstrain input and output types to uint8, uint16, float tensors. or UNDEFINED. Got: expected to have: does not specify a valid type. should be of integer type and specify a type.Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=Value of attribute not specifiedinputs are expected to have tensor type.Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.{name} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} Input data type does not match the expected data type. Current data type is Input data type does not match the expected data typeScale and Zero-point must be of rank 1Scale and Zero-point must be a scalarA_zero_point{additionalDocumentation}A_scalereducedConstrain the input to uint8.DequantizeBFPConstrain y to float and bfloat16.ReduceSumIntegerConstrain input types to 8 bit signed and unsigned tensors.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).Constrain input type to 8-bit integer tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Constrain y to uint8.Constrain the input to float and bfloat.block_dimEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.The datatype to dequantize to.QuantizeBFPConstrain shape and strides to uint64.Shape of quantized tensor must be 1D.x_scaleConstrain 'x', 'y_scale' to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.bfp_typeThe type of BFP - must match with the BFPType enumx_zero_pointConstrain 'x' and 'x_zero_point' to 8-bit integer tensors.C_scaleC_zero_pointB_scaleB_zero_pointy_scaley_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain input and output types to signed/unsigned int8 tensors.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Number of neurons in the hidden layerRCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Couple the input and forget gates if 1.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Y_zero_pointConstrain input and output types to 8 bit tensors.X_zero_pointY_scaleopset version of corresponding SoftMax.apply softmax to elements for dimensions axis,or all dims along with axis according to op-version , ]. Its actual value is: 'axis' must be in [data_scaleC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointadditionaxis must be in [-rank, rank-1]. input rank was multiplicationCoefficient of leakage.X_scalereduced_scaledata_zero_pointQLinearReduceMeanreduced_zero_pointb_zero_pointConstrain input A, b_scale and output Y data type as float tensor.MulIntegerConstrain output types to 32 bit tensors.b_scaleConstrain input A data type to 8-bit integer tensor.a_zero_pointC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.a_scaleConstrain output zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output type to float32 or 8 bit tensors.Constrain scale types to float tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input A and its zero point types to 8 bit tensors.input_scaleweight_scaleCorresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)z_scaleSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).conditionz_zero_pointaxis must be in [-rank, rank)Required attribute axis is missingWhich axis to concat onAll inputs to Concat must have same rankY_cConstrain weights types to 8 bit tensors.Constrain seq_lens to integer tensor.TVConstrain scale types to any float tensor type.T8initial_hinitial_csequence_lensY_hPcublasLt order of output matrixcublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.DequantizeWithOrderThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)Constrain Scale to float32 typesConstrain to float typesQuantizeWithOrdercublasLt order of matrix Y and optional matrix Corder_Border_YcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Border_AConstrain input and output types to float32 tensors.mask_index_outcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.beta_zero_pointlayernorm_outQscale_inputcublasLt order of output matrix.order_inputForder_outputgamma_scalebeta_scaleposition_embedding_scalesegment_embedding_scalesegment_embedding_zero_pointgamma_zero_pointword_embedding_zero_pointposition_embedding_zero_pointConstrain input and output types to int8 tensors.input_zero_pointweight_zero_pointword_embedding_scaleposition_embedding_quantword_embedding_quantbeta_quantgamma_quantcublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization tensor must be int8 tensors.quantization scale must be float tensors.QOrderedGeluorder_global_weightConstrain scales to float32scale_values_gemmscale_QKT_gemmV_biasscale_QKT_softmaxcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.order_Xscale_XQOrderedAttentionConstrain scales to float32 tensors.cublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.QOrderedLayerNormalizationscale_V_gemmscale_K_gemmQ_weightcublasLt order of global biasscale_Q_gemmscale_V_weightscale_K_weightK_biasQ_biasV_weightK_weightscale_Q_weightscale_Bscale_Yscale_AHidden layer sizes of Q, K, V paths in Attentionorder_weightcublasLt order of weight matrixscale_CQOrderedMatMulConstrain bias and scales to float32Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axisauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETThe shape of the convolution kernel. If not present, should be inferred from input W.Constrain bias type to 32-bit integer tensor.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsQLinearConvTransposenumber of groups input channels and output channels are divided into.ww_scaleConstrain output type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.w_zero_pointscale_qkv_gemmscale_global_weightscale_weightscale_biasQOrderedLongformerAttentionBe compatible with float version.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.scale_global_gemmscale_output̼+D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccGroupNormbias shall be 1 dimensionConstrain gamma and beta to float tensors.Constrain input X and output Y types to float tensors.BiasSplitGeluActivation after group normalization: 0 for None, 1 for SwishgroupsThe number of groups of channels. It should be a divisor of the number of channels CThe epsilon value to use to avoid division by zeroNhwcConvStride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that 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.The size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Works on NHWC layout or not? Default not.Whether to use ceil or floor (default) to compute the output shape.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.cconnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node num_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones. ImplicitInputs:onnxruntime::graph_utils::RemoveNode. Index: ExplicitInputs:onnxruntime::graph_utils::AddNodeInputonnxruntime::graph_utils::FindPathFailed since multiple edges matched:onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name: Implicit input name cannot be safely updated to Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameAttempting to get an input that does not exist.onnxruntime::graph_utils::AddInitializeronnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cc in one of the subgraphs.onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphAttempting to get index by a name which does not exist:for node: onnxruntime::graph_utils::GetIndexFromNameitr != node_args.end()onnxruntime::utils::SetNodeAttributeutils::HasName(attribute)D:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccAttributeProto must have a name.Attempting to load runtime optimization records for a previously loaded optimizer: runtime optimization record produced op id is null. onnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatonnxruntime::SaveRuntimeOptimizationRecordToOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cc_{delimiter must not be empty!delimiter.empty()onnxruntime::utils::SplitStringD:\a\_work\1\s\onnxruntime\core/common/string_utils.honnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringD:\a\_work\1\s\onnxruntime\core/graph/op_identifier.hInvalid OpIdentifier string: Failed to parse since_version from onnxruntime::fbs::utils::LoadOpIdentifierOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.ccinvalid string_view positionTENSORGRAPHINTSTRINGUNDEFINEDFLOATSPARSE_TENSORSGRAPHSSPARSE_TENSORSTRINGSTENSORSFLOATSINTSNull tensor in tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing string data for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null string attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatMissing indicies for sparse initializer: Missing dims for sparse initializer: onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveSparseInitializerOrtFormatonnxruntime::fbs::utils::SaveInitializerOrtFormatHS|TS|8S|Type() != nullptrD:\a\_work\1\s\onnxruntime\core\graph\function_utils.ccall_tensor_types_with_bfloatinTinonnxruntime::function_utils::IOTypeConstraintHelper::::operator ()domain_version != -1No opset registered for domain it != opset_imports.end() in function opset imports.onnxruntime::function_utils::Inliner::bindNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()D:\a\_work\1\s\onnxruntime\core\graph\function.ccinitializer != nullptronnxruntime::FunctionImpl::FunctionImpl is not found or is not constant initializer.The past input shall be 5 dimensionsInvalid bias shapeqkv_hidden_sizes should have 3 elementsbeta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionssegment_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.word_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.input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsdeltalimitConstrain input and output types.Can not get shape initializer data!Unsupported type:Unsupported non-raw-data data type!invalid scales valueD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cctensor rank too smallinvalid scales dimensionAWmemory_seq_lensConstrain seq_lens to integral tensors.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.MWQWonnxruntime::concurrency::ReadThreadAffinityConfigD:\a\_work\1\s\onnxruntime\core\util\thread_utils.ccProcessor id must consist of only digits: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit);, skip affinity settingFound invalid processor id in affinity string: std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0Invalid processor interval: processor_from <= processor_toProcessor id must start from 1: processor_from > 0 && processor_to > 0custom join thread function not setReceived null OrtThreadingOptionsactual_num_affinities == static_cast(options.thread_pool_size) - 1onnxruntime::concurrency::CreateThreadPoolHelper, thread_pool_size: Number of affinities does not equal to thread_pool_size minus one, affinities: Found out-of-range processor id in affinity string: Failed to read affinities from affinity stringSize of affinity string must be between 1 and Received invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringonnxruntime::math::NextPositionD:\a\_work\1\s\onnxruntime\core\util\math_cpu.ccdims[d_i] < d_max??rB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@Kbad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;Quant GEMM format: AIsSigned(), BIsSigned() is not supported on this deviceQLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!ElementSize must be power of 2 and less or equal than 16!    length overflowonnxruntime::ToUTF8StringD:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideStringdefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLoggeronnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccSUCCESSFAILNO_SUCHFILEGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]NO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILkernelbase.dllRaiseFailFastException%ws%hs(%d) tid(%x) %08X %ws Msg:[%ws] CallContext:[%hs] [%hs(%hs)] [%hs] ReturnNtReturnHrLogNtLogHrFailFast%hs(%u)\%hs!%p: %hs!%p: (caller: %p) ExceptiongenericsystemRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::WindowsEnv::GetCanonicalPathUNC\"" when trying to load " "LoadLibrary failed with error , errcode = , allocation_granularity = , mapped_offset = mapped offset must be a multiple of the allocation granularity\/DeleteFile() failed - path: onnxruntime::WindowsEnv::DeleteFolderonnxruntime::WindowsEnv::ReadFileIntoBufferoffset < 0length > buffer.size()SetFilePointerEx ReadFile fail: unexpected endonnxruntime::WindowsEnv::MapFileIntoMemoryopen file mapping fail, errcode = open file GetFileSizeEx Invalid fd was supplied: Received negative size from stat callfile_path == nullptr, group_id: SetThreadAffinityMask done for thread: . Specify the number of threads explicitly so the affinity is not set., error msg: , error code: , index: SetThreadAffinityMask failed for thread: - does not exist, processor Cannot set affinity for thread onnxruntime::WindowsThread::ThreadMain, then on , first on group Cannot set cross-group affinity for thread , mask: index >= 0onnxruntime::WindowsThread::WindowsThreadcustom_create_thread_fn returned invalid handle. doserrno: WindowThread:_beginthreadex failed with message: kernel32.dllSetThreadDescriptiononnxruntime error msg: unmap view of file failed. error code: onnxruntime::UnmapFileD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc/\Negative thread index is not allowed\*onnxruntime::LoopDirD:\a\_work\1\s\onnxruntime\core/platform/path_lib.hFailed to fetch cpu info on windows consist of logical processors: core core(s) from windows system:Found total FreeLibrary failed with error Kernel32.dllK32EnumProcessModules in library, error code: Failed to find symbol onnxruntime::WindowsEnv::FormatLibraryFileNameFailed to calculate byte size for saving cpu info on windowsonnxruntime::WindowsEnv::InitializeCpuInfounknown erroronnxruntime::`anonymous-namespace'::ParsePathRootD:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::Path::ParseFailed to parse path root: /\onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset", "block_size": ["thread_id": "], "core": DistributionDistributionEnqueueRunWait{"main_thread": {LogStart must pair with LogEnd!points_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartpoints_.empty()Profiler not started yetenabled_onnxruntime::concurrency::ThreadPoolProfiler::StopD:\a\_work\1\s\onnxruntime\core\common\threadpool.cc}}}, "sub_threads": {", "thread_pool_name": "unnamed_thread_pool!ps_n >= 0onnxruntime::concurrency::ThreadPool::ParallelForMore work items than threadsn <= num_threads_ + 1onnxruntime::concurrency::ThreadPoolTempl::RunInParallelD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectionWaitRevokeUnknownEvent"core": "num_run": ": {Nested parallelism not supported!current_parallel_section.has_value()onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection"pid" :{"cat" : ""dur" :"tid" :"ph" : "X","ts" :"args" : {"name" :"Maximum number of events reached, could not record profile event.Writing profiler data to file onnxruntime::profiling::Profiler::EndTimeAndRecordEvent",onnxruntime::profiling::Profiler::EndProfilingD:\a\_work\1\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::Startonnxruntime::profiling::Profiler::Initializesession_logger != nullptrSessionKernelNodeApi" : "" : }, } ] ||||illegal input path:unexpected failurepmam is not a valid day is not a valid year is not a valid date [] :, tensor_typemap_typesequence_typeNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull sequence type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormat is not supported currentlyNull map type info. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for is missing type info.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormat] for nowonnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveValueInfoOrtFormatWe do not support type [. Invalid ORT format model.Type:onnxruntime::fbs::utils::LoadValueInfoOrtFormatNull type info for onnxruntime::fbs::utils::LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.opset id is null. Invalid ORT format model. y|||)'s input is marked single but has an empty string in the graph)'s output ) has more outputs ( for operator ' appeared multiple times.Mismatched attribute type in 'Unrecognized attribute: not in allowed input sizes.) has input size not in allowed output sizes.) has output size ) in op definition.has output size ) has more inputs () than declared ( typestr: , has unsupported type: has unsupported type has inconsistent type Operator '' has been deprecated since version not in range [min=, max=Error parsing function body:Cannot find a function builder that satisfies the requested opset version: op_type = (inputs_.size() - 1) == iExtra unparsed input unexpected.ONNX Schema : failed validating the check: !(it.GetName().empty())(outputs_.size() - 1) == i' is expected to have field 'type_protos'' is expected to have field 'graphs'' is missing. has unknown expected typeRequired attribute ', opset_version = Duplicate type constraint name' is expected to have field 'sparse_tensor'' is expected to have field 't'' is expected to have field 'type_proto'' is expected to have field 'g'' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'tensors'' is expected to have field 'strings'optionalInvalid tensor data type opaquemapcomplex64DataTypeUtils::FromDataTypeString - Received invalid data type string complex128Invalid data type tensor(map(optional(Unsuported type proto value case.sparse_tensor(Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The output is a tensor of strings or integers. Its shape will be the same as the input shape.The input must be a tensor of strings or integers, either [N,C] or [C].Values greater than this are mapped to 1, others to 0.map(int64, string)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.map(int64, float)D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.The input must be a tensor of a numeric type. The output will be of the same tensor type.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]A collection of weights of the model(s).The output will be a tensor of strings or integers.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.A list of floats.A list of ints.A string.A list of strings. One and only one of 'value_*'s should be set.A float.An integer.Input's shape should be 1D or 2DInput type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setOnly one of values_*'s can be set in label encoder.The input type is a tensor of any shape.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.A value that needs replacing.Value(s) to change toValue(s) to change to.Label encoder has only one output.Label encoder has only one input.Input type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.The size of each input in the input listThe input type must be a tensor of a numeric type.The 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.map(int64, double)map(string, int64)map(string, double)map(string, float)A string vocabulary array.
One and only one of the vocabularies must be defined.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.An integer vocabulary array.
One and only one of the vocabularies must be defined.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceAn integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The 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.node id that this weight is for.The id of the tree that this node is in.The weight for the class in class_id.The index of the class list that each weight is for.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Popularity of each node, used for performance and may be omitted.Thresholds to do the splitting on for each node.Child node if expression is true.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.Child node if expression is false.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Tree id for each node.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe number of support vectors.Support vector coefficients.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.First set of probability coefficients.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.The input type must be a tensor of a numeric type, either [C] or [N,C].Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.The input must be a tensor of a numeric type, either [C] or [N,C].List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Weights of the intercepts, if used.Weights of the model(s).The total number of regression targets, 1 if not defined.One of 'MAX,' 'L1,' 'L2'The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The total number of targets.The weight for each targetDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'seq(map(string, float))The output will be a sequence of string or integer maps to float.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is falseChild node if expression is trueThe id of the tree that each node is in.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The index of the target that each weight is forThe node id of each weightThe output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.ccA list of labels.This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. 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.This number of op outputs should be 3 when Training_mode = True, but it is not. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).This number of op outputs should be 1 when Training_mode = False, but it is not.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain output y data type to 32-bit integer tensor.The pads attribute cannot be used simultaneously with auto_pad attributeStride along each spatial axis. If not present, the stride defaults to 1 along each axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input 'w'.number of groups input channels and output channels are divided into. default is 1.RoIs tensor must have 2 dimensionsAttribute pooled_shape must be specifiedAttribute pooled_shape has incorrect lengthMultiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.ROI pool output shape (height, width).'output_shape' must have same number of elements as the shape of input tensor X.p value of the Lp norm used to pool over the input data.MaxUnpool op must have either two or three inputs.Constrain index tensor to int64Attribute pads has incorrect size.Input tensor X must have at least 2 dimensions.Attribute kernel_shape has incorrect size.Attribute strides has incorrect size.'output_shape' must be rank 1 tensor.Attribute kernel_shape must be specified.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\defs.ccDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. IndicesAttribute dilations has incorrect sizeInput tensor must have at least 2 dimensionsAttribute kernel_shape has incorrect sizeAttribute strides has incorrect sizeAttribute pads has incorrect sizeAttribute kernel_shape must be specifiedConstrain input and output types to float and 8 bit tensors.Second input tensor has wrong dimensionThe starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Input shape must have either [C] or [1,C] dimensions where C > 01-D tensor of floatsInput is ether string UTF-8 or int32/int64Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1Scaling parameter.The number of channels to sum overThe exponent.Constrain input and output types to float tensors.Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative values) for attribute 'axis'Invalid value(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). training_mode of Dropout must be a scalar.Ratio of Dropout must be a scalar.The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } Constrain input to only numeric types.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.running_meanrunning_varConstrain mean and variance types to float tensors.Constrain scale and bias types to float tensors.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_meaninput_varBiasT = Cast (bias)ScaleT = Cast (scale)BiasReshaped = Reshape (BiasT, ScaleShape)ScaleReshaped = Reshape (ScaleT, ScaleShape)Biased = Add (Scaled, BiasReshaped)Scaled = Mul (ScaleReshaped, Normalized)The number of groups of channels. It should be a divisor of the number of channels `C`.Axes2X3D = Reshape(XReshaped, Shape3D)Square = Mul (X3D, X3D)Mean = ReduceMean (X3D, Axes2)SquareOfMean = Mul (Mean, Mean)MeanOfSquare = ReduceMean (Square, Axes2)ScaleShape = Constant ()Deviation = Sub (X3D, Mean)NumGroupsC = Shape (X)N = Shape (X)GroupSize = Div (C, NumGroups)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)InstanceShape = Shape (X)Shape3D = Constant ()XReshaped = Reshape (X, NewShape)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Scaled = Mul (NormalizedT, Scale2D)Type of Mean and InvStdDev. This also specifies stage one's computation precision.Constrain input types and output Y type to float tensors.num_groups%s:%u: %s: Assertion `%s` failed.BuildContextDependentFunctionBodyLayerNormalizationEpsilon = Cast (FloatEpsilon)FloatEpsilonMean2D = ReduceMean (XU, Axes_1)Axes_1 = Constant()NormalizedT = Cast (Normalized)MeanOfSquare = ReduceMean (Square, Axes_1)image_shapeblock_shapeConstrain input and output types to all numeric tensor types.sinceVersion == 17 || sinceVersion == 18Attribute pads must have an even size { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } input must have rank 3.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"List of stop words. If not set, no word would be removed from X.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseEnvironment 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. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } A list of integers, along which to reduce. The default is to 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.GroupNormalizationConstrain input/output to integer tensors.Constrain input to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input types to all numeric tensors.Constrain input/output to boolean tensors.Constrain output to boolean tensor.Constrain input to boolean tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccWhether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index). { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_square = Mul(data, data) sum_square = ReduceSum(data_square, axes) sum_square_dbl = Cast (sum_square) sqrt = Sqrt(sum_square_dbl) reduced = CastLike(sqrt, data) } 'axis' must be in [-rank(indices), rank(indices)-1]Keep the reduced dimension or not, default 1 means keep reduced dimension.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).sumD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.cc { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } optional(seq(tensor(int32)))optional(seq(tensor(int16)))optional(seq(tensor(float16)))optional(seq(tensor(int64)))optional(seq(tensor(double)))optional(seq(tensor(float)))optional(seq(tensor(bool)))optional(seq(tensor(string)))optional(seq(tensor(uint8)))optional(seq(tensor(uint32)))optional(seq(tensor(uint16)))optional(seq(tensor(int8)))optional(seq(tensor(uint64))) and Output expected to have tensor or sparse tensor typeaxis must be in [-rank, rank-1].indices must be in [-rank, rank-1].type: Input: does not match type of output: Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..All inputs to Concat must have same rank. Input Constrain output types to any tensor type.concat_result Value=(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.Constrain output to int64 tensor.Input tensor can be of arbitrary type. has rank Constrain output to int64 tensor, which should be a scalar though.Dimension could not be inferred: incompatible shapesInvalid Target shape product of 0. Product cannot be 0 in combination with -1(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.reshapedoutput = Cast (input)target_typeTarget shape may not have multiple -1 dimensions.Invalid dimension value: Invalid position of 0.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.ccoptional(tensor(complex128))optional(tensor(int64))optional(tensor(int32))optional(tensor(float))optional(tensor(float16))optional(tensor(string))optional(tensor(double))optional(tensor(complex64))optional(tensor(bool))optional(seq(tensor(complex128)))optional(seq(tensor(complex64)))optional(tensor(uint16))optional(tensor(uint8))optional(tensor(uint64))optional(tensor(uint32))optional(tensor(int16))optional(tensor(int8))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).squeezed'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).axis must be in [-r, r-1]Input and output types can be of any tensor type.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.data tensor must have rank >= 1transposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).updatessteps}, input shape = {Invalid attribute perm {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Attribute perm for Transpose has repeated value: Incorrect or missing input value for starts and endsSlice op must have either three, four or five inputs.Input steps has incorrect lengthInput axes has incorrect length) vs (Input axes has invalid dataInput rank for starts and ends should be the same: (Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps'step' cannot be 0 for SliceBoth 'split' input and 'num_outputs' attribute were givenInvalid value of attribute 'axis'. Rank=Mismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (Neither 'split' input nor 'num_outputs' attribute has been givenAttribute `num_outputs` value cannot be lower than 1OneHot node must have three inputs.Input 'depth' must have exactly one element.Input 'depth' must be a scalar or rank 1 tensor.Input 'values' must have exactly two elements.Input 'values' must be rank 1 tensor.(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).Constrain input and output types to all tensor, sequence, and optional types.Indices tensor must have rank >= 1(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain to boolean tensors.Constrain roi type to float or double.gridSupport padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.Constrain input `X` and output `Y` types to all tensor types.Constrain grid types to float tensors.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.roisizesConstrain input 'X' and output 'Y' to all tensor types. This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` .The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.repeatsConstrain repeat's type to int64 tensors.Two 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'.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64expandedList 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).Blocksize must be positiveBlocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalCenterCropPad op must have 2 inputs.) does not match the number of axes (`shape` only supports `int32_t` or `int64_t` inputsNumber of elements of input 'shape' (x_shape_alldims = Shape (input_data)axes_input = Constant ()Supported modes: `constant`(default), `reflect`, `edge`Pads has incorrect number of values. Expected 2 * constant_valueThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Repeated axis: 'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Too many axes provided values. Got values.inverse_indicesLast dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).Invalid value for attribute axis(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).Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.'sequence_lens' must have rank of 1'input' must have rank >= 2(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(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. Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. **History** - Version 16 adds bfloat16 to the types allowed (for the second and third parameter). depthConstrain to any tensor type.Constrain output types to boolean tensors.Constrain input types to float tensors.output_data = Slice (padded_input, start_dims, end_dims)input_dataIf provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.output_dataCenterCropPadx_shape_alldims2 = Shape (padded_input)padded_input = Pad (input_data, pads)x_shape2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)start_dims = Div (sh_diff, k2)sh_diff = Sub (x_shape2, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)end_dims = Add (start_dims, shape)x_shape = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)pad_amount = Sub(padded_sh, x_shape)padded_sh = Max(x_shape, shape)pad_amount_right = Sub(pad_amount, pad_amount_left)pad_amount_left = Div(pad_amount, k2)padded_input = Pad (input_data, pads, , axes_input)pads = Concat (pad_amount_left, pad_amount_right)OptionalHasElement is expected to have 1 output.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.Input type is null. Input must have Type information.OptionalGetElement must have an input element.Type of the element in the optional outputConstrain input type to all tensor and sequence types.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\defs.ccConstrain output type to all optional tensor or optional sequence types.OptionalHasElement is expected to have 0 or 1 input.Attribute 'type' should be a TypeProto and it should specify a type.Optional is expected to have an output.Optional is expected to have either an input or the type attribute set.Input type is null. Type information is expected for the input.Optional-type input must contain an element with type information.Constrain output type to all tensor or sequence types. broadcasting: (Wrong op_type name for running propagation: D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.ccInvalid rank for data_0List of tensors for Constrain input and output types to numeric tensors.output = Identity (input)output = Where (input_less_than_min, min, input)input_less_than_min = Less (input, min) { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } Value of alpha. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } Value of beta. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors.slopeMish 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. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Constrain input X and output types to float tensors. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.Constrain input and output types to all tensors.KValuesWhether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the elements in sorted order.K input must be a one-dimensional tensor of size 1.Axis has less than the requested k elements.K input must be of type int64. { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () exp_x_add_one = Add (exp_x, one) Y = Log (exp_x_add_one) } X_ReduceMax = ReduceMax (input)X_ReduceMax = ReduceMax (input, axes) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) output = Where (input_large_than_max, max, input)input_large_than_max = Less (max, input)output_large_than_max = Less (max, tmp)tmp = Where (input_less_than_min, min, input)output = Where (output_large_than_max, max, tmp)const_one_floatsqueeze_mask = Squeeze (mask, axes)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)const_one_casted = Cast (const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)input_gather_element = GatherElements (input, transform_targets) const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) const_zero_casted = Cast (const_zero_float)const_zero_floatinput_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)loss_NCdd = Neg (input_gather_element_transform)loss = ReduceSum (loss_Ndd)loss = ReduceMean (loss_Ndd)loss_unweighted = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)loss_Ndd = Mul (loss_unweighted, weight_gather)loss = Mul (loss_unweighted, weight_gather)const_ignore_index loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_zeroexpanded_target = Unsqueeze (target, axes)const_one input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) ignore_indexloss_Ndd = Squeeze (loss_N1dd, axes)loss = Squeeze (loss_N1dd, axes)If set to 1 will perform the sums in reverse direction.axis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain 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.Constrain input a and its zero point data type to 8-bit integer tensor.input and zero_point pair is expected to have same type.If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. HannA {name} window with length: size. The output has the shape: [size].Constrain output types to numeric tensors.Constrain the input size to int64_t.Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } dft_lengthsize input must be a scalar.Constrain scalar length types to int64_t.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. size input must be greater than 0.is_onesided and inverse attributes cannot be enabled at the same timeaxis attribute value is invalid for a tensor of rank If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.dft_length input must be a scalar.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index.labelsSoftmaxCrossEntropyLosslog_probType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.Shape3Dlog_prob = Identity (X_Log) X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) output = NegativeLogLikelihoodLoss (X_Log, labels)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.Rank of input does not match the equation indices.Einsum expression string.InputsType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.lossConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.NegativeLogLikelihoodLossConstrain target to integer types loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) weight_gather = Squeeze (weight_gather_temp_1, axes)Input and target dimension value mismatch.Target rank must be 1 less than the input rank.Weight rank must be 1.targetConstrain signal and output to float tensors.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.signalframe_stepframe_lengthConstrain to integer tensors.Constrain to any numerical types.Constrain to float tensorsframe_length input must be scalar.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!window input must have rank = 1.num_mel_binssample_ratelower_edge_hertzupper_edge_hertzBlackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } dft_length input must be scalar.num_mel_bins input must be scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.cc(Optional) The data type of the tensors in the output sequence. The default type is 'float'. is null. Type info is expected.Input type for input at index SequenceConstruct is expected to have at least 1 input.Attribute dtype should be of integer type and specify a type._cond_out_in_itercount_cond_in_cond_seqemptySequenceMap__seqlenExpected 1 or more inputs.Expected 1 or more outputs.Graph attribute inferencing returned type information for Invalid ``body`` argument. Expected a graph expected but not providedSequenceMap_loop_bodyInput 0 expected but not providedExpected a sequence type for input 0Which 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.Graph attribute inferencer for "body" not available outputs. Expected expected to have type info expected to be a sequence typeWhich 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.Constrain split size to integral tensor.Constrain output types to all tensor types.], Value=Invalid value of attribute 'axis'. Accepted range=[new_axis must be either 0 or 1Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Constrain output to integral tensor. It must be a scalar(tensor of empty shape). sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Input type for input at index 0 is null. Type info is expected.input_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence=Constrain position to integral tensor. It must be a scalar(tensor of empty shape).positionoutput_sequenceElement type of inputs are expected to be the same.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Constrain input types to any tensor type.additional_inputsThe graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.Constrain to any tensor or sequence type.SequenceMapout_sequenceConstrain input types to any sequence type. was Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output but subgraphs produce If node has condLoop 'body' subgraph scan outputs should all be tensors but output Number of scan output axes specified ( was not a tensor.Number of scan input axes specified () is not equal to number of scan outputs (Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Scan input was notoptional(tensor(bfloat16)) axis value ) is not equal to number of scan inputs (optional(seq(tensor(bfloat16)))An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor typesAn 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).initial_state_and_scan_inputsAn attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.final_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.v_initialtensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) typesGraph 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.Only boolD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.The input is not evenly splittablelength of each output. Values should be >= 0.'step' cannot be 0Target shape may not have multiple -1 dimensionsD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccInvalid position of 0Invalid Target shape product of 0This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Attribute 'scales' is required.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`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthConstrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearNumber of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.) is not equal to the existing rank value (Ranks inferred (Constrain tiles and axis's type to int64 tensors.tilesThe scale along height dimension. It takes value greater than or equal to 1.height_scaleThe scale along width dimension. It takes value greater than or equal to 1.width_scaleNew shapelegacy optimization attribute.One float, indicates the value to be filled, default is 0consumed_inputsWhich axis to split onlength of each outputoutputs...paddingsThree modes: constant(default), reflect, edgeList 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`.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Constrain input and output to only numeric types.rank must be greater than axisWhich axis to concat on. Default value is 1. 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).'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".Constrain input and output types to all tensor and sequence types.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.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Attribute value for pads is requiredAttribute pads has incorrect lengthWhich axis to split on. List of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.List of non-negative integers, indicate the dimensions to be insertedEnding indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]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.selected_indicesD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\defs.ccmax_output_boxes_per_classdefault 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.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.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).Input must be an optional-type value containing an element with type information.OptionalHasElement is expected to have 1 input.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccThe standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Lower boundary of the output values.The mean of the normal distribution.The shape of the output tensor.(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'.Input tensor must be 2-dimensionalConstrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.ccConstrain input types.Constrain output types to be numerics.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 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, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, 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.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.value_intvalue_intssparse_valuevalue_stringvalue_stringsvalue_floatvalue_floatsAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorInput to 'Range' op should be scalars (Tensor with only one element and shape empty)output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)Constrain output types to all numeric tensors and bool tensors.BernoulliAll inputs to 'Range' op must be of the same type { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Constrain input types to common numeric type tensors.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Input tensor must have rank 2Constrain output types to integral tensors.p value of the Lp norm used to pool over the input data, default is 2.0.Stride along each 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.Processed_STDStride along each spatial axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.EX_squaredX_squaredExponentX_RMSTDX_varianceE_XsquaredVarianceD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccIndicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain output mask types to boolean tensors.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.(float, default 0.5) the ratio of random dropoutConstrain mean and variance types to float tensors. It allows all float type for U.The ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.saved_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. saved_mean 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. varIf set to nonzero, run spatial batch normalization in test mode, default is 0.is_testIf true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The 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 shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size]. 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) D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.cc 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 A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.First input tensor must have rank 3One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The shape format of inputs X, initial_h 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]. 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) Loop 'body' subgraph outputs should all be tensors or sequences but output else= then=An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.All Tensor and Sequence typesMismatched type for output Mismatched tensor element type for output Int64 tensorD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\old.ccLoop 'body' subgraph outputs should all be tensors but output (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float or int32 tensor.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).Constrain input and output types to high-precision and 8 bit numeric tensors. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The type of the output tensor is integer.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices.loss_unweightedloss_sumloss_Nddweight_gatherexpanded_target_int64transform_targetsweight_gather_sumconst_zero_target_typed'shape' input must be 1D tensorloss_NCddloss_N1ddexpanded_targetinput_gather_elementD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\old.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Dimension on which to do the sort.Scalar 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.Whether C should be broadcastedInvalid value for attribute kNumber of top elements to retrieveMinimum value, under which element is replaced by minMaximum value, above which element is replaced by maxValue of beta default to 0.5Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Value of alpha default to 0.2If set, defines the broadcast dimensions. See doc for details.Coefficient of leakage default to 0.01.X_shapeX_LogX_LogSMX_LogSM_NCDPass 1 to enable broadcastingbroadcastDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. squeeze_maskconst_one_castedconst_zero_castedinput_gather_element_transformX_NCDX_NDCweight_gather_tempweight_gather_temp_1If set, defines the broadcast dimensions.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccEnable broadcastingConstrain input to integral tensors.Constrain input to float tensors.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\old.ccD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.ccD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.ccfowardThe sequence output for the hidden is optional if 0. Default 0.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\old.cc does not match existing output type of Input was expected to have tensor or sparse tensor type. Got Element type of tensor or sparse tensor input was unknowntarget map type missing value type.Element type of sequence input was unknownInput was expected to have sequence type. Got Output was expected to have tensor type. Got Input element type of target optional type missing element type.source optional type missing element type.target sequence type missing element type.source sequence type missing element type.source map type missing value type.Mismatched map tensor key type:target map type missing key type.source map type missing key type. target=Mismatch between number of source and target dimensions. Source= expected to have type but instead is nullValue type of map input Mismatched sparse tensor element type:Mismatched tensor element type:Mismatched type: source=Element type of sequence input expected to have sequence typeElement type of input unknownKey type of map input expected to have map typeElement type of optional input expected to have optional type expected to have tensor or sparse tensor type. Got: Input type was nullKey type of map input was unknownInput was expected to have map type. Got Element type of optional input was unknownInput was expected to have optional type. Got shape input must be 1D tensorInput was expected to have either tensor, sequence, optional or map type. Got Value type of map input was unknown does not match the actual sizeCannot parse data from external tensors. Please load external data into raw data for tensor: ParseData type mismatch for tensor: Data size mismatch. Tensor: expected size . Expected: Actual:The type of tensor: is undefined so it cannot be parsed. ==> Context: domaingraphsetting data_type field (tensor name: ) to UNDEFINED is not alloweddata_typeUnrecognized type value case (value_info name: int64_datastring_dataint32_datafloat_data' is required but missing.Field 'name' of '): value_typekey_typeelem_typeutf8str_to_wstring: string is too long for converting to wstring.value_info' is required to be non-empty.op_typeWarning: Model contains experimental ops:) should refer to attribute in parent node.Attribute (name: No opset import for domain 'NodeProto (name: , type: ) has zero input and zero output.) must have rank 1 or 2.) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0) should not contain more than one value field.type field and data field mismatch in attribute attr) has no index values.] out of range.) second dimension size does not match rank of tensor.) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor values () must have rank 1.sparse_tensor_proto] not in lexicographic sorted order.) has values, but NNZ is Sparse tensor () index value at position [] out of range [0, Sparse tensor indices (' instead of 'STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field.Unrecognized data_type (tensor name: values of data_type '' should be stored in field ') should be stored in , but it doesn't exist or is not accessible.) should be file inside the , but the 'TensorProto (tensor name: ) is 0-element but contains data!TensorProto ( tensor name: ) is stored externally but doesn't have a location.) is stored externally and should not have data field.uint64_datadouble_dataraw_data' points outside the directoryLocation of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: Data of TensorProto ( tensor name: ConstantFill' has been used as output names multiple times.Nodes in a graph must be topologically sorted, however input '' of node: in initializer but not in graph input initializer name is not uniqueTensor initializers must have a non-empty nameinitname: is not output of any previous nodes. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty nameOp registered for is deprecated in domain_version of No Op registered for with domain_version of Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.Bad node spec for node. Name: OpType: floatstype_protointgraphstensorsstringsinvalid stol argumentstol argument out of range[ParseError at position Error context: Unexpected attribute type.Unhandled type: %dError parsing TensorProto shape (expected numeric dimension).Expecting primitive type as map key type.Value expected but not found.Incomplete string literal.Identifier expected but not found.Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).Unexpected type.Unexpected type in sparse-tensor element type.(line: column: metadata_propsString value expected, but not found.Unexpected literal type.Integer value expected, but not found.Expected character opset_importir_versiontype_protossparse_tensorsdoc_stringmodel_versionproducer_versionproducer_nameData for input already exists. does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.Graph initializer names must appear after the actual inputs: were provided.The number of graph input cannot be smaller than the number of node inputCannot use the same name as both a subgraph initializer and subgraph input: (op_type:, node name: Cannot find missing input: in initializers. type is missing.Shape inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Cannot infer type and shape for node name Graph has inputs but were providedkey type mismatch from MapProto. existing=type case mismatch. existing= inferred=NOT_SET. No opset import for domain optype type case unsupported for symbolic shape inference. inferred=type case unsupported. existing=sparse_tensor_typeopaque_typeoptional_typeInferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (unk__Inferred elem type differs from existing elem type: (axes as an input and attribute cannot be specified at the same time.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Number of elements of input 'sizes' (Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".Unknown value for `keep_aspect_ratio_policy`: Either `sizes` or `scales` must be provided, but not both of themInput 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'Number of elements of input 'scales' () must be same as rank of input 'X' (Dimension value inferred () is not equal to the existing dim value (onnx.SparseTensorProtoonnx.TensorProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.NodeProtoonnx.ValueInfoProtoonnx.AttributeProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.ModelProtoonnx.TrainingInfoProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Optionalonnx.TypeProto.Maponnx.FunctionProtoonnx.OperatorSetIdProtoonnx.TypeProtoFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   SearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyprogram size pattern length DFA out of memory: SearchDFA inconsistencyRE2: invalid startpos, endpos pair. [Unexpected re_anchor value: bytemap range list count Invalid RE2: text size: endpos: startpos: Error compiling 'pattern too large - compile failedError reverse compiling 'Unknown encoding D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.cc': Error parsing 'unexpected errorinvalid escape sequenceinvalid character classinvalid character class rangeD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: no errorRegexp not destroyed.Stack not empty.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hWalk NULLinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledno argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatormissing ]missing )unexpected )trailing \8F~PE~hE~E~E~G~G~G~G~ G~HG~`G~xG~F~F~Missing case in Compiler: should never happenD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccCompiler::Copy called!No ranges in char classNdPcBad call to ParseState::ParsePerlFlagsLoNlMnMcLuLlLtLmRE2: unexpected op: Bad hex digit AnyConcat of unknown round: AddFoldedRange recurses too much.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccRepetitionWalker::ShortVisit calledUnexpected opcode in short circuit: context does not contain textBad args: nsubmatch= in stepUnhandled unhandled D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.cc in AddToThreadqFailed to analyze start state.DeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.unhandled opcode: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.cc|RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccUnexpected opcode: job_.size() = njob_ = GrowStack() failed: Cannot use SearchOnePass for unanchored matches.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccUnexpected opcode in IsMatch: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.cc SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is Case not handled in ComputeSimple: DW~TW~HW~TW~LW~W~PW~W~W~W~ W~W~(W~W~8W~W~V~U~V~U~W~XW~W~XW~V~W~V~W~V~hW~V~hW~`V~\W~pV~\W~V~W~V~W~ V~`W~0V~`W~@V~W~PV~W~U~pW~U~pW~V~W~V~W~U~U~U~U~U~W~U~W~pU~W~U~W~[:xdigit:][:^xdigit:]AZazAZ[:upper:][:^upper:][:word:][:^word:][:punct:][:^punct:][:space:][:^space:][:lower:][:^lower:][:print:][:^print:][:digit:][:^digit:][:graph:][:^graph:][:blank:][:^blank:][:cntrl:][:^cntrl:][:alpha:][:^alpha:][:ascii:][:^ascii:]\w\W[:alnum:][:^alnum:]\d\D\s\S0909az!/:@[`{~09AZaz ~09AZ__az!~ 09AFaf 09AZ__az~~P~~~~~~~~~@~~#~~~~~~~~~~~p~x~X~~~d~~~p~~~x~~~(~~x~4~~~@~~~H~~~}~~{~~ }~~~~~~~~~}~~}~0~P~}~H~}~~}~~}~~}~~H~}~~[P~S}~~}~~}~p~}~~P}~~`}~x~p}~x~x}~~}~~}~~(}~(~8}~~|~~|~~|~~ H~}~~ ȁ~ؖ~(~ԁ~~~~~`~!~~~~ ~~~ā~~ ~ l~X~x~~~,~~~,~ؿ~ 8~~~H~~`~`~`~ ~~~Ȏ~~~$~`~~P~ ~@~~Ȁ~h~؀~~x~~~~~~~H~\~~|~ `~~ d~0~"Ȅ~l~~0~~8~~H~x~T~8~~0?011111122*2G2P2P2`222223MMƤ(+6799wy@O(O  `nn3NTVg7Rk";Vo6NPUp  "CAZazptvwz}/1VYY` Jnoq/M$$((@X`jp 9 = = P P X a q     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ] ] ` a     : = = N N T V _ a z  023@F@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo1@Q`lnp xPmpt T3EL#MOZ} EHMPWYY[[]]_}q q   !!!! !!!!!!$!$!&!&!(!(!*!-!/!9!>@ACDF=Pptv!:AZf0484sz    ?AJVoqPpP=@ptvDPY`lA0000@ U X _ 0J'pz|  @n_n4Mh  89;>@DFFJPl!<Up4Vn!--  ....:.;.@.@.].].0000000012XXcc ooWW  ? ? 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