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LD$0HT$ H+EHL$pHt˵HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p膡tH~\HD$0HT$8HD$0HD$hHL$p͸j/LH[^HL$p31H^H1H]H1HA HA$ H]HҺ1HHHL$ LD$0HT$ H!DHL$pHtHHĀ[H\$WH0HHL$ HL$ II~HHH HHtHHh[H\$ Ht0H{HKtHKHt @UHHUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$ptH3}\HD$0HT$8HD$0HD$hHL$p-'h/LHY^HL$p蓼1Hd^HD1H6]HE1HA HA$ HR]H21HHHL$ LD$0HT$ HBHL$pHt!HHĀ[H\$WH0HHL$ HL$ II许HHH HHtHHȁ[H\$ Ht0H{HKt~HKHt @W~UHHJ~UH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pFtH{\HD$0HT$8HD$0HD$hHL$p荵g/LHMX^HL$p1H^H褵1H]H襶1HA HA$ H]H蒷1HHHL$ LD$0HT$ H@HL$pHt聱HHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$pFtHz\HD$0HT$8HD$0HD$hHL$p荴e/LHMW^HL$p1H^H褴1H]H襵1HA HA$ H]H蒶1HHHL$ LD$0HT$ H?HL$pHt聰HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pFtHy\HD$0HT$8HD$0HD$hHL$p荳e/LHMV^HL$p1H^H褳1H]H襴1HA HA$H]H蒵1HHHL$ LD$0HT$ H>HL$pHt聯HHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$pFtHx\HD$0HT$8HD$0HD$hHL$p荲c/LHMU^HL$p1H^H褲1H]H襳1HA HA$H]H蒴1HHHL$ LD$0HT$ H=HL$pHt聮HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pFtHw\HD$0HT$8HD$0HD$hHL$p荱c/LHMT^HL$p1H^H褱1H]H襲1HA H]H蜳1HHHL$ LD$0HT$ HHT$pHD$hHL$pFtHv\HD$0HT$8HD$0HD$hHL$p荰a/LHMS^HL$p1H^H褰1H]H襱1HA H]H蜲1HHHL$ LD$0HT$ H;HL$pHt苬HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pFtHu\HD$0HT$8HD$0HD$hHL$p药i/LHMR^HL$p1H^H褯1H]H襰1HA HA$ H]H蒱1HHHL$ LD$0HT$ H:HL$pHt聫HHĀ[H\$WH0HHL$ HL$ IIޱHHH HHtHH(z[H\$ Ht0H{HKtޫHKHt @vUHHvUH\$H3HHH0_@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p視tHs\HD$0HT$8HD$0HD$hHL$pg/LHP^HL$pS1H$^H1H]H1HA HA$ H]H1HHHL$ LD$0HT$ HA9HL$pHtHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p覕tHr\HD$0HT$8HD$0HD$hHL$pf/LHO^HL$pS1H$^H1H]H1HA HA$H]H1HHHL$ LD$0HT$ HA8HL$pHtHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p覔tHq\HD$0HT$8HD$0HD$hHL$pe/LHN^HL$pS1H$^H1H]H1HA H]H1HHHL$ LD$0HT$ HK7HL$pHtHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p覓tHp\HD$0HT$8HD$0HD$hHL$p\/LHM^HL$pS1HD^H1H]H1HA HA$ H]H1HHHL$ LD$0HT$ HA6HL$pHtHHĀ[H\$WH0HHL$ HL$ IIHHH HHtHHu[H\$ Ht0H{HKt>HKHt @rUHH rUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$ptHSo\HD$0HT$8HD$0HD$hHL$pMGZ/LH L^HL$p賮1H^Hd1HV]He1HA HA$ Hr]HR1HHHL$ LD$0HT$ H4HL$pHtAHHĀ[H\$WH0HHL$ HL$ II>HHH HHtHHs[H\$ Ht0H{HKt螥HKHt @wpUHHjpUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pftHm\HD$0HT$8HD$0HD$hHL$p譧`/LHmJ^HL$p1H^Hħ1H]HŨ1HA HA$ H]H販1HHHL$ LD$0HT$ H3HL$pHt衣HHĀ[H\$WH0HHL$ HL$ IInHHH HHtHHHr[H\$ Ht0H{HKtHKHt @nUHHnUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pƎtHl\HD$0HT$8HD$0HD$hHL$p `/LHH^HL$ps1Hd^H$1H]H%1HA HA$ H2]H1HHHL$ LD$0HT$ Ha1HL$pHtHHĀ[H\$WH0HHL$ HL$ II螫HHH HHtHHp[H\$ Ht0H{HKt^HKHt @7mUHH*mUH\$H3HHH0_@SHHHL$pHD$0HD$(H^HT$pHD$hHL$p&tHsj\HD$0HT$8HD$0HD$hHL$pmgV/LH-G^HL$pө1H^H脤1Hv]H腥1HA HA$ H]Hr1HHHL$ LD$0HT$ H/HL$pHtaHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&tHsi\HD$0HT$8HD$0HD$hHL$pmgT/LH-F^HL$pӨ1H^H脣1Hv]H腤1HA HA$ H]Hr1HHHL$ LD$0HT$ H.HL$pHtaHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&tHsh\HD$0HT$8HD$0HD$hHL$pmg[/LH-E^HL$pӧ1H^H脢1Hv]H腣1HA HA$ H]Hr1HHHL$ LD$0HT$ H-HL$pHtaHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p&tHsg\HD$0HT$8HD$0HD$hHL$pmg[/LH-D^HL$pӦ1H^H脡1Hv]H腢1HA HA$ H]Hr1HHHL$ LD$0HT$ H,HL$pHtaHHĀ[@SHHHL$pHD$0HD$(H^HT$pHD$hHL$p&tHsf\HD$0HT$8HD$0HD$hHL$pmgR/LH-C^HL$pӥ1H^H脠1Hv]H腡1HA HA$ H]Hr1HHHL$ LD$0HT$ H+HL$pHtaHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&tHse\HD$0HT$8HD$0HD$hHL$pmgP/LH-B^HL$pӤ1H^H脟1Hv]H腠1HA HA$ H]Hr1HHHL$ LD$0HT$ H*HL$pHtaHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&tHsd\HD$0HT$8HD$0HD$hHL$pmgW/LH-A^HL$pӣ1H^H脞1Hv]H腟1HA HA$ H]Hr1HHHL$ LD$0HT$ H)HL$pHtaHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p&tHsc\HD$0HT$8HD$0HD$hHL$pmgW/LH-@^HL$pӢ1H^H脝1Hv]H腞1HA HA$ H]Hr1HHHL$ LD$0HT$ H(HL$pHtaHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&tHsb\HD$0HT$8HD$0HD$hHL$pmgQ/LH-?^HL$pӡ1H^H脜1Hv]H腝1HA HA$ H]Hr1HHHL$ LD$0HT$ H'HL$pHtaHHĀ[H\$WH0HHL$ HL$ II΢HHH HHtHHg[H\$ Ht0H{HKt辘HKHt @cUHHcUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$p膃tH`\HD$0HT$8HD$0HD$hHL$p͚P/LH=^HL$p31H$^H1H]H1HA HA$ H]HҜ1HHHL$ LD$0HT$ H!&HL$pHtHHĀ[H\$WH0HHL$ HL$ IIHHH HHtHHhe[H\$ Ht0H{HKtHKHt @aUHHaUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$ptH3_\HD$0HT$8HD$0HD$hHL$p-'K/LH;^HL$p蓞1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ H$HL$pHt!HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$ptH3^\HD$0HT$8HD$0HD$hHL$p-'I/LH:^HL$p蓝1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ H#HL$pHt!HHĀ[@SHHHL$pHD$0HD$(H^HT$pHD$hHL$ptH3]\HD$0HT$8HD$0HD$hHL$p-'P/LH9^HL$p蓜1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ H"HL$pHt!HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p~tH3\\HD$0HT$8HD$0HD$hHL$p-'P/LH8^HL$p蓛1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ H!HL$pHt!HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p}tH3[\HD$0HT$8HD$0HD$hHL$p-'J/LH7^HL$p蓚1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ H HL$pHt!HHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$p|tH3Z\HD$0HT$8HD$0HD$hHL$p-'J/LH6^HL$p蓙1H^HD1H6]HE1HA HA$HR]H21HHHL$ LD$0HT$ HHL$pHt!HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$p{tH3Y\HD$0HT$8HD$0HD$hHL$p-'E/LH5^HL$p蓘1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pztH3X\HD$0HT$8HD$0HD$hHL$p-'C/LH4^HL$p蓗1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(H^HT$pHD$hHL$pytH3W\HD$0HT$8HD$0HD$hHL$p-'J/LH3^HL$p蓖1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pxtH3V\HD$0HT$8HD$0HD$hHL$p-'J/LH2^HL$p蓕1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pwtH3U\HD$0HT$8HD$0HD$hHL$p-'D/LH1^HL$p蓔1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$pvtH3T\HD$0HT$8HD$0HD$hHL$p-'D/LH0^HL$p蓓1H^HD1H6]HE1HA H\]H<1HHHL$ LD$0HT$ HHL$pHt+HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$putH3S\HD$0HT$8HD$0HD$hHL$p-'?/LH/^HL$p蓒1H4^HD1H6]HE1HA HA$ HR]H21HHHL$ LD$0HT$ HHL$pHt!HHĀ[H\$WH0HHL$ HL$ II.HHH HHtHHW[H\$ Ht0H{HKt~HKHt @WTUHHJTUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pFttHQ\HD$0HT$8HD$0HD$hHL$p荋HKHt @QUHH QUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pqtHSN\HD$0HT$8HD$0HD$hHL$pMG:/LH +^HL$p賍1HT^Hd1HV]He1HA HA$ Hr]HR1HHHL$ LD$0HT$ HHL$pHtAHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pptHSM\HD$0HT$8HD$0HD$hHL$pMG8/LH *^HL$p賌1HT^Hd1HV]He1HA HA$ Hr]HR1HHHL$ LD$0HT$ HHL$pHtAHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$potHSL\HD$0HT$8HD$0HD$hHL$pMG?/LH )^HL$p賋1HT^Hd1HV]He1HA HA$ Hr]HR1HHHL$ LD$0HT$ HHL$pHtAHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pntHSK\HD$0HT$8HD$0HD$hHL$pMG7/LH (^HL$p賊1HT^Hd1HV]He1HA HA$Hr]HR1HHHL$ LD$0HT$ HHL$pHtAHHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$pmtHSJ\HD$0HT$8HD$0HD$hHL$pMG5/LH '^HL$p賉1HT^Hd1HV]He1HA HA$Hr]HR1HHHL$ LD$0HT$ HHL$pHtAHHĀ[@SHHHL$pHD$0HD$(H>HT$pHD$hHL$pltHSI\HD$0HT$8HD$0HD$hHL$pMGHT$pHD$hHL$pitHSF\HD$0HT$8HD$0HD$hHL$pMG9/LH #^HL$p賅1HT^Hd1HV]He1HA H|]H\1HHHL$ LD$0HT$ H HL$pHtK|HHĀ[@SHHHL$pHD$0HD$(HHT$pHD$hHL$phtHSE\HD$0HT$8HD$0HD$hHL$pMG1/LH "^HL$p賄1H$^Hd1HV]He1HA HA$ Hr]HR1HHHL$ LD$0HT$ H HL$pHtA{HHĀ[H\$WH0HHL$ HL$ II讔HHH HHtHHI[H\$ Ht0H{HKt{HKHt @wFUHHjFUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pfftHC\HD$0HT$8HD$0HD$hHL$p}./LHm ^HL$p1H^H}1H]H~1HA HA$ HҪ]H1HHHL$ LD$0HT$ H HL$pHtyHHĀ[H\$WH0HHL$ HL$ IIΞHHH HHtHHHH[H\$ Ht0H{HKtyHKHt @DUHHDUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$pdtHB\HD$0HT$8HD$0HD$hHL$p |5/LH^HL$ps1H^H$|1H]H%}1HA HA$ H2]H~1HHHL$ LD$0HT$ HaHL$pHtxHHĀ[H\$WH0HHL$ HL$ IIHHH HHtHHF[H\$ Ht0H{HKt^xHKHt @7CUHH*CUH\$H3HHH0_@SHHHL$pHD$0HD$(HHT$pHD$hHL$p&ctHs@\HD$0HT$8HD$0HD$hHL$pmzg4/LH-^HL$p1HD^Hz1Hv]H{1HA HA$ H]Hr|1HHHL$ LD$0HT$ HHL$pHtavHHĀ[H\$WH0HHL$ HL$ IIHHH 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p\\pppp\\0qpqps0Q0q`qp`q@q/Zqppps0Qq0qpq@0q@1qp qps0Q-q*qp "q@`'q`aЮqpqps0Qqqpq@qqppqps0Q q0qp q@ q aQqp@qps0QMq0JqpAq@0GqPpppps0QPpppp@ pp@qp3qps0Q6q5qpp3q@ 5q pWq pRqps0QVq VqpRq@Tq`p0qpВqps0QЕqqpq@q`pqp@qps0Qq@qpq@qpP7qp3qps0Q6q5qpp3q@ 5qp@qp`qps0QPqqpq@qPp =qpP8qps0QrVrVrp>rdrp|r}r{rP|rPrrrrs ZrrrVr`r@sPs`spsssss0spssss0s`spss0s`s`ssеs?r@ssVrUnknown exceptionbad array new lengthstring too longC:\a\_work\_temp\protobuf\protobuf-21.12\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\a\_work\_temp\protobuf\protobuf-21.12\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 longC:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.C:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\a\_work\_temp\protobuf\protobuf-21.12\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 ,I|8I|@I|HI|Can't happenC:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899falsetrue\\x%02xty10invalid string position...\\?\\/INVALID_ARGUMENT:, 'map/set too longunordered_map/set too longinvalid hash bucket count /\ was false.Stacktrace: narrowing_errorCpuai.onnx.mlai.onnx.preview.trainingai.onnx.trainingVIWEFAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityC:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::IDataTransfer::CopyTensorAsync is not implementedC:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=C:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)ai.onnxCPUExecutionProviderTrying to get a Tensor, but got: OrtValue::GetIsTensor()OrtValue::GetMutableC:\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 overflowC:\a\_work\1\s\onnxruntime\core/common/safeint.hORT_LOAD_CONFIG_FROM_MODELelem_type_ != nullptrTensor sequence must contain only primitive typesC:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::GetAti < tensors_.size()IsSameDataType(tensor.Get())TensorSeq: tensor to be added has a different data type.onnxruntime::TensorSeq::Addtried creating tensor with negative value in shapeUnknown Exception, got size overflowNot able to find appropriate IDataTransfer to copy sparse datanot enough space: expected C:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cc`anonymous-namespace'::GetDataTransfer`anonymous-namespace'::ValidateFillInputArgsStrings can only reside in CPU memoryOrtApis::FillSparseTensorCootried Filling sparse tensor with negative value in values shapetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::FillSparseTensorCsrCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::FillSparseTensorBlockSparseOrtApis::UseCsrIndicesOrtApis::UseCooIndicesthe ort_value must contain a constructed tensorOrtApis::UseBlockSparseIndicesUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsRegisterCustomOpsUsingFunction: Registration function was not foundRegisterCustomOpsUsingFunction: Registration function name must be specified.onnxruntime_profile_EnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledNULL input supplied for input input name cannot be emptylengths allocation failedoutput name cannot be emptyOutput buffer allocation failedstring buffer allocation failedelement index is out of boundsinput array doesn't equal tensor sizeSparse Tensor does not contain sparse dataOrtValue should contain a Tensor or a Sparse Tensorshape is invalidThis API supports Tensors or SparseTensorsoffsets buffer is not equal to tensor sizeindex is out of boundsbuffer size is too small for string elementoutput buffer is too small. Use GetStringTensorDataLength.internal errorout of indexInput is not of one of the supported sequence types.index out of rangeinput array is too shortInput is not of type sequence or map.Expecting all elements to be tensors. Got: Input is not of one of the supported map types.OrtCreateValueImplSeqHelperin[idx]->IsTensor()Each element of the sequence should be either tensor or map.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported input typeAt least one element in the sequence is of a type different from others.Either the key tensor or the value tensor has NumDimensions > 1For map type num_values MUST be 2Key type is not supported yet.Key and value tensors have unequal number of elements.opaque(Number of values should be at least 1.Specified domain and type names combination does not refer to a registered opaque type,OrtApis::CreateOpaqueValueml_type != nullptrnon_tensor_base != nullptrOpaque type is not a non_tensor type!!!Invalid build with no providers availableOrtApis::GetOpaqueValueSpecified provider is not supported.location dimensions do not match shape sizethis API does not support stringsmax_meminvalid location rangeinitial_chunk_size_bytesarena_extend_strategyinitial_growth_chunk_size_bytesmax_dead_bytes_per_chunk1.15.1Invalid key found: ORT Build Info: git-branch=, git-commit-id=, build type=RelWithDebInfo, cmake cxx flags: /DWIN32 /D_WINDOWS /EHsc /EHsc /wd26812 /Qspectre /MP -DEIGEN_HAS_C99_MATHThe requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s Tensor type mismatch. T onnxruntime::Tensor::MutableDatautils::IsPrimitiveDataType(dtype_)Invalid index requested for map type.onnxruntime::Tensor::DataAsSpanvalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelper++index < c.size()Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscriptonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggerC:\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: Maximum string length for a provider options key/value is 1024.Provider options key/value cannot be emptyQNN execution provider is not supported in this build. XNNPACKSNPEAZUREUnknown provider name. Currently supported values are 'SNPE', 'XNNPACK', and 'AZURE'VitisAIonnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughNo requested allocator available::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input nameOutput buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution provider::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typeUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKernel::OrtKernelInfo cannot get a valid logger from its execution provider' in custom op 'Outputcustom op registered at runtimeC:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInputonnxruntime::CreateSchemaall typesOnly the last input to a custom op may be marked variadic.i == input_count - 1There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.1 == undefinedOnly the last output to a custom op may be marked variadic.i == output_count - 1 input to be of optional typestinput count does not matchonnxruntime::IsCompatiblecustom op schemas mismatch, expecting input to be of variadic typendth input to keep same arityoutput count does not match input to be of single type input to keep same homogeneity output to be of single type output to keep same homogeneity output to be of optional type output to be of variadic type output to keep same arityonnxruntime::CreateCustomRegistryTensor type mismatch.onnxruntime::Tensor::DataRaw) != new size (type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::ReshapeTensor size (shape_.Size() == new_shape.Size() is not present.onnxruntime::OpKernelContext::RequiredOutputC:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hRequired output at index output_ptronnxruntime::OpKernelContext::OutputPlease fetch output tensor with specified shape.p_ml_valueIsSameDataType(tensor)onnxruntime::LoadDynamicLibraryFromProviderC:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.ccProvider_SetHostonnxruntime::ProviderSharedLibrary::Unloadonnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::Ensureonnxruntime::ProviderLibrary::Unloadonnxruntime_providers_cuda.dllonnxruntime::ProviderLibrary::GetGetProvideronnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dllonnxruntime_providers_cann.dllonnxruntime_providers_rocm.dllonnxruntime::TryGetProviderInfo_CUDAonnxruntime_providers_tensorrt.dllonnxruntime_providers_migraphx.dllonnxruntime::TryGetProviderInfo_ROCMSessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared librarySessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_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_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.TensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryoneDNN execution provider is not enabled in this build.invalid unordered_map keyRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputCreate_State_Compute_Release_State_Unable to find schema for node. Domain:'' op_type:kernel already mapped to existing node, expect: matching node is missingC:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasarg_num < static_cast(input_count_)invalid arg_num.invalid node output countinvalid node input count: onnxruntime::standalone::CreateOpstandalone_Attribute type not supported yet.onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsonnxruntime::standalone::InvokeOpKernel definition was not found for node Domain:'Provided allocator is nullOrtMemoryInfo is nullEnv is nullProvided 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.OrtEnv::Releaseenv_ptr == p_instance_.get()C:\a\_work\1\s\onnxruntime\core\session\ort_env.cc it.second=C:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputonnxruntime::SyncProvidersmapped_feed_names_.size() == feed_names_.size()Size mismatch: index=mapped_output_names_.size() == output_names_.size()Size mismatchonnxruntime::IOBinding::SynchronizeOutputsonnxruntime::IOBinding::SynchronizeInputsp_provideronnxruntime::IOBinding::BindOutputImplbad castinvalid stoi argumentstoi argument out of range MemoryType: DeviceId:] OrtMemType: OrtAllocatorType:Device:[DeviceType:OrtMemoryInfo:[name: id:CUDAExecutionProviderDmlExecutionProvider has already been registered.C:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddProvider C:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoCached EP instance for graph replay is not set yet before calling ReplayGraph()onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraphIsGraphCaptured()C:\a\_work\1\s\onnxruntime\core/session/inference_session.honnxruntime::utils::GetElementTypeFromOptionalTensorIsOptionalTensor(type)Provided type is not an optional tensoronnxruntime::utils::GetElementTypeFromOptionalSeqTensorIsOptionalSeqTensor(type)Provided type is not an optional sequence tensorC:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.hMemcpyTransformerORTM] /[json.exception.session.use_env_allocatorssession.disable_quant_qdqsession.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.debug_layout_transformationsession.intra_op_thread_affinities value of 'save' is only valid when saving an ORT format model.save%Y-%m-%d_%H-%M-%S are '0' and '1'. The environment variable contained the value: Invalid value for applyC:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configThe only supported values for the environment variable offonis_model_proto_parsedModelProto needs to be parsed to check for ORT config within itstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()Flush-to-zero and denormal-as-zero are -Creating and using per session threadpools since use_per_session_threads_ is truegraph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()onnxruntime::InferenceSession::ConstructorCommonAffinity string must not be emptyDynamic block base set to session--intra-op-inter-opto.custom_join_thread_fncustom join thread function not set for intra op thread pool!to.affinity_str.empty()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 SEQUENTIALcustom join thread function not set for inter op thread poolCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: session_env.EnvCreatedWithGlobalThreadPools()onnxruntime::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::RegisterExecutionProviderParallel 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.It is enabled within session options which may result in lower performance.QDQ quantization is not supported while using the DML Execution Provider. Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsCan't register custom op domains with ORT for onnxruntime::InferenceSession::SaveToOrtFormatonnxruntime::InferenceSession::LoadWithLoaderThis session already contains a loaded model.Exception during loading: Failed to save ORT format model to file: Load model from failed:model_loading_uriEncountered unknown exception in LoadWithLoader()model_loading_arrayFailed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().model_loading_from_saved_protoModelProto 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.onnxruntime::InferenceSession::TransformGraph::::operator ()post_layout_transform_step_.onnxonnxruntime::InferenceSession::TransformGraph::::operator () bytes were able to be read.onnxruntime::LoadOrtModelBytesCastFloat16Transformeronnxruntime::InferenceSession::TransformGraphThis 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.). does not support TunableOp.onnxruntime::InferenceSession::SetTuningResultsCannot find execution provider , enable TunableOp for usingFailed to load TuningResults (index=). Reason: Invalid TuningResults (index=Unsupported device specified in the memory arena shrink list: Correctly set TuningResults for combination is not an arena based allocator: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: error message: The registered allocator for device-id Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunrun_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::AddPredefinedTransformersonnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5_.jsonlist too longother_erroronnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0|com.microsoftorg.pytorch.atencom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwcMismatch between source and target type. Source=[ShapeInferenceError] Unsupported Source/Target type= Target=tensor(uint64)tensor(int32)tensor(int64)tensor(float16)unknowntensor(uint32)tensor(uint8)tensor(float)tensor(double)tensor(bfloat16)seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))tensor(uint16)tensor(int8)tensor(int16)seq(tensor(uint8))seq(tensor(float))seq(tensor(double))tensor(string)tensor(bool)seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16))tensor(complex64)tensor(complex128)seq(tensor(string))seq(tensor(bool))Schema error: (domain: Trying to register schema with name known by the checker., but its domain is not line , but it is already registered from file ) from file version: in the inclusive range [, but its version is not in onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you Received 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.stringOnly 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.MemcpyFromHostTinputXException caught: intra-opinter-opC:\a\_work\1\s\onnxruntime\core\session\environment.ccMemcpyToHostoutputYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.valuetuning_resultsUnsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsC:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cc, column at line ort_configsession_optionsUnsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeSequential modeParallel modeSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Setting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: Setting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICvalidatorsJson 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.onnxruntime::SetEnableProfilingSetting enable_profiling to epresultsDid not find session options in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointra_op_num_threadsintra_op_num_threads option in the model file must be an integerFound session/run/environment configuration in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoORT config json from the model: The Model Proto hasn't been checked for the ORT config json.graph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integerFound tuning results in the model file to be used while loading the modelonnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadata' not foundkey 'Ignoring unsupported session option in ORT config: . Ignoring...Tuning results stored in the model file cannot be parsed. Error message: cannot use at() with cannot use key() for non-object iteratorsobjectarraybooleanbinaryinvalid map keynullcannot get valuediscardednumberout_of_rangetype_errorinvalid_iteratorparse errorparse_errorunexpected ; expected invalid BOM; must be 0xEF 0xBB 0xBF if givensyntax error while parsing - ; last read: 'null literalstring literalnumber literal'['invalid literaltrue literalfalse literal','end of input'[', '{', or a literal'{'']''}'':'invalid number; expected digit after exponent signinvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: missing closing quoteunknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid 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: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+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+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+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+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+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+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: ill-formed UTF-8 byteinvalid 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 \u001Fvector too longcannot compare iterators of different containersobject keyobject separatornumber overflow parsing 'iterator does not fit current valueiterator out of rangecannot use erase() with type must be string, but is type must be object, but is type must be number, but is type must be array, but is C:\a\_work\1\s\include\onnxruntime\core/graph/graph.h Max:Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplonnxruntime::GraphTransformer::RecurseC:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.htoidx < input_defs.size()onnxruntime::IsIsolatedFp16NodeOnCpuC:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.ccInsertedCast_cast node to cast from float16 to float32 on cpuCastInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()RemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplonnxruntime::InsertCastTransformer::ApplyImpldtypeACLExecutionProviderROCMExecutionProviderArmNNExecutionProvidersession.enable_quant_qdq_cleanupsession.disable_double_qdq_removeroptimization.enable_gelu_approximationQDQFinalCleanupTransformerBiasDropoutFusionAttentionFusionBiasSoftmaxFusionBiasGeluFusionsession.x64quantprecisionsession.qdqisint8allowedCommonSubexpressionEliminationCastEliminationConvMulFusionConvBNFusionDivDivMulFusionConstantSharingConvConvAddFusionDynamicQuantizeMatMulFusionDropoutExpandEliminationEmbedLayerNormFusionEliminateDropoutDoubleQDQPairsRemoverGeluFusionGeluApproximationGemmSumFusionGemmActivationFusionFastGeluFusionExpandGatherToSliceFusionGatherToSplitFusionEliminateIdentityLayerNormFusionIdentityGemmTransposeFusionGemmMatmulTransposeFusionMatMulScaleFusionAddNoopEliminationMatMulActivationFusionSimplifiedLayerNormFusionMatMulIntegerToFloatFusionMatMulAddFusionClipQuantRewriteAvx2WeightS8ToU8TransformerQDQPropagationTransformerClipMulSubWhereNotWhereFusionReshapeFusionFuseReluClipSkipLayerNormFusionRocmBlasAltImplReluQuantRewriteQDQS8ToU8TransformerQuickGeluFusionRelu_RuleBasedTransformerUnsqueezeUnsupported optimization level: LevelSliceEliminateSliceUnsqueezeEliminationTransposeOptimizeronnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesonnxruntime::optimizer_utils::GenerateTransformersonnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformerDequantizeLinear/duplicatedEnsureUniqueDQForNodeUnitonnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeAdded by onnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.ccx|VitisAIExecutionProviderMIGraphXExecutionProviderTensorrtExecutionProvideronnxruntime::Node::ForEachWithIndex' doesn't support memcpy onnxruntime::TransformerMemcpyImpl::ProcessDefsMemcpyExecution type 'C:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::MemcpyTransformer::ApplyImpldup_replacements.find(&arg) == dup_replacements.end()Copy from/to host memoryonnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()C:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::ApplyBad optional accessSqueezeGatherNhwcMaxPoolTransposeQLinearConvPermutation length does not match rank onnxruntime::ApiValueInfo::PermuteDimsperm.size() == gsl::narrow_cast(shape_proto->dim_size())size >= 0Failed to get size of TensorProtoonnxruntime::ApiTensor::Dataonnxruntime::ApiTensor::NumElements out of bounds for shape C:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\optimizer_api_impl.cc0 <= p && p_int < shape_proto->dim_size()Permutation entry onnxruntime::ApiGraph::TransposeInitializersuccessonnxruntime::ApiGraph::ReshapeInitializerFailed to find initializer to reshape with name node_arg_ != nullptrNo NodeArg found for name Failed to find initializer for name: onnxruntime::ApiGraph::GetValueInfo provided for op: Added in transpose optimizerdomain == kOnnxDomainTranspose optimizer is expected to add only onnx domain ops. Domain: Cannot reshape initializer to have different number of elements_outnew_num_elts == old_num_eltssince_version.has_value()Transpose Optimizer is adding an unexpected node: Existing destination type is not compatible with source type.const_transpose_optimizer domain not found in opset imports.onnxruntime::GetSinceVersionForNewOpAn entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.opset_import_iter != domain_to_version_map.end()linearmodeQLinearAveragePoolFusedConvonnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)ResizeMaxPoolConcatQLinearGlobalAveragePoolOptimization after layout transformation failed: channels_last)I||)I|| )I|| )I|p|)I|p| )I|p|)I|p|)I|p|)I||)I|| )I|| )I|| )I|| )I|p| )I|p| )I|p| | | | | onnxruntime::GraphTransformerManager::ApplyTransformersC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered Shapesplitonnxruntime::GatherToSplitFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.ccaxisonnxruntime::GatherToSliceFusion::ApplyImplRangenum_outputsSqueezeAxesInitializerSqueeze for Fused Gather nodesaxesSplit for Fused Gather nodesSplitSlice for Fused Gather nodesUnsqueezeAxesInitializerSliceAxesInitializerUnsqueeze for Fused Gather nodesUnsqueeze_range_input_defs.size() == 3unsqueeze_output_Could not find OrtValue with name 'ConstantFoldingcan't constant fold Could not find a CPU kernel and hence QuantizeLinear node 'onnxruntime::ConstantFolding::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.ccstartendUnsupported output type of fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported data type: mul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2div_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializerC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccConstant initializer NodeArg shape should not be null. NodeArg: shapeFused MatMul and Scale_FusedMatMulAndScaleFusedMatMulalphaonnxruntime::`anonymous-namespace'::GetOutputNodeMergesMatMulonnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::MatMulScaleFusion::ApplyImplonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Slice parameter is not expected. Input index:Start MatchGemmSubgraphSlice ends is less than INT_MAXExpected value:C:\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 bias is not constantGemm bias shape not expectedOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphReshapeOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathFaild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesCheckSliceParameters return falseconcat first input value is not -1Gemm bias shape is not expectedGemm weight is not constant initializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerPass MatchGemmSubgraphStart ValidateGemmInitializergather axis value not expectedgather input 1 value is not expectedMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowunidir mask is not constantonnxruntime::AttentionFusionHelper::ValidateUnidirMaskGemm weight shape is not expectedPass ValidateGemmInitializerCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Failed to find Softmax nodeOutput edge count not expected for Softmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphSoftmaxPass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2mask_mul const input not matchedPass MatchInputMaskSubgraphmask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1Failed to find path for maskOutput edge count not expected for mask nodesFailed to find reshape shape path 1Failed to find reshape shape path 2Failed to find shape pathequal const not matched.Failed to find mask pathwhere const not matched.Start MatchInputMaskSubgraphDistilBertEqualFailed to find path for present_v and past_vFailed to match v_concatFailed to find path for past_kFailed to find path for present_kStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphgather indices not matched.Pass MatchInputMaskSubgraphDistilBertpast_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1present_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpermpast_k_transpose perm attribute not matchedFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3onnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphq_reshape const not matchedqk_div const not matched.Start CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQPass CheckNodesInPathVreshape initializer value is not expectedhidden_size != num_heads * head_sizev_reshape initializer value is not expectedk_reshape const not matchedPass CheckNodesInPathKk_transpose has not perm attributek_transpose perm attribute not matchedStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKq_transpose perm attribute not matchedPass CheckNodesInPathQFaild to find path v to SplitCheckNodesInPathV return falseonnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulStart FuseGptAttentionMask_Int32Cast mask from int64 to int32opt_k_transpose perm attribute not matchedFailed to find path for kCheckNodesInPathQ returns falseUsing transpose optimized patternFailed to find path for qq and v are not from same Split nodeMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLunidirectionalmask_filter_valueAttentionnum_headsMatchPastSubgraph returns falseFused Attention subgraphs k and v are not from same Split nodeCheckNodesInPathK returns falseMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplonnxruntime::ConvertMaskToInt32C:\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 shapeepsilonbn_scale_tensor_protoconv_B_tensor_protoConvBnFusion_W_bn_var_tensor_protoconv_W_tensor_protobn_B_tensor_protobn_mean_tensor_protoonnxruntime::ConvBNFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccConvBnFusion_BN_B_BatchNormalizationonnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_quantqdq_s8_to_u8_zp_conversionC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccLeakyReluAveragePool1DQArgMaxdropDQdropSplitQDQQDQSelectorActionTransformer*DQ2DQSigmoidGlobalAveragePool|onnxruntime::NodesToOptimize::GetNodeindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)C:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hTanhoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Failed to get Clip min/max constants.activationC:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesactivation != nullptrExpected activation node.HardSigmoidConvActivationFusionConvAddReluConvActonnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"Expected Conv then Add.activation_paramsbetaactivation__FusedActivationFusedMatMulActivation with activation C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cconnxruntime::MatMulActivationFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination_onnxruntime::QuickGeluFusion::ApplyImplQuickGeluC:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.ccC:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of minFuseReluClip__min_zero_constantBiasGeluGeluonnxruntime::GeluApproximation::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationFastGeluTotal Gelu Approximation (FastGelu) node count: |X|x|allowzeroFused reshape node: Total fused reshape node count: startsendsConcatTrainingCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraphonnxruntime::ReshapeFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccFused 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() == 0graph.RemoveNode(sum_node.Index())transAtransBnew_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccnew_gemm_output_defs.size() == 1Sumother_sum_input != nullptronnxruntime::GemmSumFusion::SatisfyCondition and Invalid node index node != nullptronnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndC:\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::TryCreateFromNodeToOutput at NodeArg "", index: node ("Inserting Q/DQ pair between _pre_qInserted by QDQPropagationTransformerAt least one graph node must be specified in the propagation edge.src_node || dst_nodeonnxruntime::`anonymous-namespace'::InsertQDQPairC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cc".onnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::QDQPropagationTransformer::ApplyImpl_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_Int32Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.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 shapeCast Input from int64 to int32_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputC:\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::MatchInputToConcatSubgraphThe parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.position_embeddingsmask_indexfused EmbedLayerNorm subgraphs EmbedLayerNormalizationNonZeroConstantOfShapeOutput 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. Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplWord embedding shape not expected.onnxruntime::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.YieldOponnxruntime::DynamicQuantizeMatMulFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeLinearDynamicQuantizeMatMulC:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerwhich does not equal the specified override of Conflicting free dimension overrides.' with a fixed dimension size onnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input 'Invalid free dimension override.FreeDimensionOverrideTransformeronnxruntime::MatMulAddFusion::ApplyImplfused Matmul and Add C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.ccgemmSeluEluFusedGemmfused Gemm fused SoftsignSoftplusParametricSoftplusScaledTanhonnxruntime::GemmActivationFusion::ApplyImplThresholdedReluwith activation C:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GetQConstantLowerUpperUnexpected data type for QuantizeLinear input y_zero_point of C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesNhwcFusedConvonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesC:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.ccNhwcFusedConvActConvAddActConvAddActivationFusionMatMulIntegerQLinearMatMulQAttentionQGemmonnxruntime::TryConvertDynamicQuantizeLSTMC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.ccweight_zp_s8_2_u8onnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplDynamicQuantizeLSTMUnable transforming DynamicQuantizeLSTM operator, int8 overflow might impact precision ! cannot locate recurrence tensor of const int8 type, unable to locate recurrence tensor or its zero point value,Fused Gemm with Transpose_transformedonnxruntime::GetTransposePermsC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cctranspose_node.InputDefs().size() == 1MatMul_With_TransposetransBatchBfused MatMul and Transpose cast != nullptronnxruntime::ReorderCastAndTranspose!node_consumers.empty()onnxruntime::UpdateConsumerCountonnxruntime::MatmulTransposeFusion::ApplyImpltransBatchACreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesdeque too longonnxruntime::ConvMulFusion::ApplyConvMulFusion_W_ConvMulFusion_Mul_B_C:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_proto__backwardpassonnxruntime::RocmBlasAltImpl::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.ccSimplifiedLayerNormalizationstash_typeonnxruntime::SimplifiedLayerNormFusion::ApplyImplonnxruntime::LayerNormFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccPowfused LayerNorm subgraphs ReduceMeanSqrt |X| h|_FloatCast Input to floatfused SkipLayerNorm subgraphs SkipLayerNormalizationonnxruntime::SkipLayerNormFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cc |X|x|ReorderInputreorderoutput_channels <= nchwc_output_channelsBuffer overflowgroupVALIDreshape_nchwcC:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformerImpl::TransformConvkernel_shapedilationsstridespadsSAME_LOWERSAME_UPPERNOTSETauto_padnearest_modehalf_pixelalign_cornersasymmetricGlobalMaxPoolscalesUpsamplefloor_bn_nchwcspatialcoordinate_transformation_modenearestchannelsReorderOutputonnxruntime::NchwcTransformer::ApplyImplaxis is not in valid range [-C:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1C:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImplis_inner_broadcastBiasSoftmax into softmax(input + bias)@|p|DoubleQDQRemoved_onnxruntime::CommonSubexpressionElimination::ApplyImpl[] because it's the graph's output.representative.output_index != kInvalidOutputIndexC:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccNot eliminating output of node C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cconnxruntime::`anonymous-namespace'::CleanUpNodeSequenceCleaning up back-to-back nodes: " and onnxruntime::QDQFinalCleanupTransformer::ApplyImpl with name ""Notonnxruntime::TransposeOptimizer::ApplyImplTranspose optimizer failed: C:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer\ort_transpose_optimizer.cconnxruntime::NhwcTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccNhwcTransformerT1BitmaskDropoutBiasDropoutBitmaskBiasDropoutfused Add-Dropout-(Add) for onnxruntime::BiasDropoutFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.cconnxruntime::MatMulIntegerToFloatFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeC:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplonnxruntime::BiasGeluFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputC:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccGPT2Gelufused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImpl |X|x|X|onnxruntime::ConstantSharing::ApplyImplTotal shared scalar initializer count: C:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.ccortshared_  C:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_B_add_B_tensor_protoConvAddFusion_Add_B_q_or_dq_input_defs.size() >= 2C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointReduceSumkeepdimsstorage_ordernoop_with_empty_axesHardSwishCeluCosSinIsNaNIsInfSignShrinkFloorCeilRoundNegLogExpAbsReciprocalXorOrPReluModMaxAtanhAndMinAtanAcosAcoshAsinhSinhTanAsinCoshReduceSumSquareReduceProdReduceL2ReduceL1ReduceLogSumExpReduceLogSumReduceMinReduceMaxMeanLessOrEqualPadCastLikeGreaterBitShiftGreaterOrEqualLessInstanceNormalizationUnsupported ONNX opset: LpPoolConvTransposecom.microsoft.QLinearMulcom.microsoft.QLinearAddcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearSigmoidcom.microsoft.QLinearReduceMeancom.microsoft.QLinearConcatcom.microsoft.QLinearLeakyReluTileArgMinLogSoftmaxHardmaxSpaceToDepthDepthToSpaceGlobalLpPoolMaxUnpoolGridSampleLRNC:\a\_work\1\s\onnxruntime\core\optimizer\initializer.cconnxruntime::Initializer::Initializer!model_path.IsEmpty()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.utils::HasDataType(tensor_proto)Initializer must have a datatypeonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncecalled_ == 1scalers.size() == 1 || scalers.size() == num_blocksaxis >= 0Axis must be non-negativeInvalid other(scalers) sizeonnxruntime::Initializer::scale_by_axisonnxruntime::Initializer::subsize() == other.size()onnxruntime::Initializer::divonnxruntime::Initializer::muldata_type() == other.data_type()Expecting the same data typeExpecting the same sizeonnxruntime::Initializer::addonnxruntime::Tensor::MutableDataAsSpanRandomUniformRandomNormalmaxonnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()C:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccMultinomialUnexpected data type for Clip input of RandomUniformLikeRandomNormalLike@^}P^}P_}h_}_}onnxruntime::TryCreateKernelTried to allocate without valid type information, ort_value index=allocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoC:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()C:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMatched inserted_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::ApplySelectorsAndActionsinit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aopsetQLinearinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8generated at runtimeFailed to remove node.Failed to set node op schema.onnxruntime::ReplaceWithNew::RunForSaveonnxruntime::ReplaceWithNew::Runonnxruntime::CreateReplacementNodeC:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::MergeIntoTarget::RunNode index value is too large to save to ORT format model: C:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cconnxruntime::`anonymous-namespace'::MoveInputOutputImplIndex out of rangeonnxruntime::MoveInputOutputonnxruntime::NodesToOptimizeIndicesBuilder::Buildtarget_node != NodesToOptimizeIndices::kEmptyNodeIndexA target node must be set.onnxruntime::GetNodesToOptimizeIndices::::operator ()node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::QDQ::ValidateNodeGroupDQNodesQDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.ccFlattenQDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: AzureExecutionProviderCANNExecutionProviderXnnpackExecutionProviderTvmExecutionProviderJsExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderQNNExecutionProviderNnapiExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderprovider.name.size() <= kMaxExecutionProviderNameLenMake the EP: name shorteronnxruntime::GetAvailableExecutionProviderNames::::operator ()C:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.cc|p||@|j}k}Xj}|j}j}pj}j}X|(||j}8j} j}j}hN|onnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsC:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsv >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: Method IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 0onnxruntime::IncrementIndexAndComputeOffsetSetupC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cc(local_source >= source) && (local_source < source + num_blocks * blocksize)onnxruntime::DoTransposeImplTranspose not implemented for empty tensors.num_axes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)Transpose of element size not supported in this build. Size=onnxruntime::DoTransposeEltWise(local_source >= source) && (local_source < source + num_blocks)Mismatched data types between input and output Tensors. input_tensor_ptr != nullptronnxruntime::Transpose::Compute(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)onnxruntime::TypedDoTransposeEltWisecenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputscenter_point_boxonnxruntime::NonMaxSuppression::Computeoutput != nullptrMissing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK()onnxruntime::GatherBase::GatherBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hTindonnxruntime::Gather::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccGather Tind type not supported in this build. must be within the inclusive range [indices element out of data bounds, idx=Missing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hX != nullptronnxruntime::UnsqueezeBase::PrepareComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAxes input is nullaxes_tensor != nullptrAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::Computecubicinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.hantialiaswhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)info.GetAttrs("scales", scales_).IsOK()stretchkeep_aspect_ratio_policyextrapolation_value model uses the deprecated attributeyet this opset `tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floorcubic_coeff_ahalf_pixel_symmetric] is not supportted!coordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceilceilnearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.Resize operatorUpsample 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 '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.exclude_outside 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 or (default) or . It can only be mode attribute is onnxruntime::UpsampleBase::StringToUpsampleModenot_largernot_smaller] is not supported!keep_aspect_ratio of [onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicypytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizeonnxruntime::UpsampleBase::ParseScalesDataall values in axes should be less than rank of the datatypeOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()onnxruntime::Optional::OptionalC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.hOptionalHasElementOptionalGetElementonnxruntime::PropagateInputOrtValueToFirstOutputC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableonnxruntime::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::ComputeVOOptionalBonnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })If 'num_outputs' is specified, the 'split' input should not be provided.C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0The split tensor must be a vector tensor.Cannot split using values in 'split' attribute. Axis=onnxruntime::SplitImpl::Computesplit_tensor->Shape().NumDimensions() == 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.ccInvalid num_outputs value of . Size of dimension being split is Axis= NumOutputs= Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape= Input shape= Num entries in 'split' (must equal number of outputs) was dst.DataType() == src.DataType()src and dst types must matchC:\a\_work\1\s\onnxruntime\core/framework/copy.honnxruntime::DispatchStridedCopyUnsupported input data type of copy shape must have non-negative sizeonnxruntime::StridedCopytotal_num_elements_to_copy >= 0onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()last >= firstonnxruntime::StridedCopy::::operator ()counter.current_offset == lastdst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()ScatterElementsScatterCPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string 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: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string 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'.data type is different from updates type vs Indices and updates must have the same rank. Input rank=Indices vs updates dimensions differs at position= is greater than input dim=Indices must have the same rank as Input. Indices rank=onnxruntime::Scatter,class std::allocator > > >::ScatterreductionC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.ccnoneIndices dim= at pos=Indices type is not supported., data shape: last dimension of indices must not be larger than rank of input tensorupdates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 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: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: string 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: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'., indices shape: ScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget,class std::allocator > >::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::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()invalid indice found, indice = Invalid 'pads' attribute valuereflectconstantInvalid 'mode' attribute valueedgeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.honnxruntime::PadBase::PadBaseonnxruntime::SliceSkips::SliceSkipsdims.size() == extents.size() && dims.size() >= steps.size()Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceIteratorBase::InitCannot use 'edge' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::PadValueFromFloatpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]Axes tensor should be a 1D tensor onnxruntime::Pad::ComputePads tensor size should be equal to twice the input dimension count axes_tensor_dims.size() == 1onnxruntime::PadImpldata_rank > 0data_rank * 2 == pads.size()'pads' has wrong number of valuesInput tensor has no dimensionsonnxruntime::PadInputWithDimValueOfZero by axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. C:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Init is invalid.onnxruntime::BroadcastIterator::Appendlargest <= 1Can broadcast 0 by 0 or 1. InputBroadcaster can only start at span boundary!onnxruntime::Broadcaster::Broadcasteronnxruntime::InputBroadcaster::AdvanceByoffset % span_size_ == 0Invalid start/ending offset [) for tensor of length:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenBroadcast Output range [) are not at boundary of span with size:onnxruntime::TensorAllocator::TensorAllocatorstart_offset % span_size == 0 && real_end % span_size == 0BitwiseNotBitwiseAndBitwiseXorBitwiseOrMust have 1 or more inputsUnsupported X type: onnxruntime::Min_6::ComputeinputCount >= 1All inputs must have the same shapeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccinput_size < std::numeric_limits::max()C:\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 >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::Max_6::Computedata_n.Shape() == shapedata_1.Shape() == shapeonnxruntime::Mean_6::Compute!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.Tensor with shape information must be 1 dimensional.onnxruntime::ExpandBroadcastLooperonnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1fmod must have value either 0 or 1fmodonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)onnxruntime::mod_internal::CallModImpl::operator ()fmod attribute must be true for floating point typesonnxruntime::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::BitShift::Compute::::operator ()cur_out == end_outUnsupported Y type: onnxruntime::BitShift::Compute::::operator ()onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.helper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.onnxruntime::BroadcastLooperinput_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computeonnxruntime::BitShift::Compute::::operator ()cur1 == end1onnxruntime::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 >::ElementWiseKernelonnxruntime::BitShift::BitShiftdirectionRIGHTLEFTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOCol2Imonnxruntime::Col2Im::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.ccsize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.dilations_.size() == image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.strides_.size() == image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.pads_.size() == 2 * image_dim_numberpads_.empty()C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.hdilations_.empty()strides_.empty()onnxruntime::Col2Im::Col2Imonnxruntime::PoolAttributes::PoolAttributesC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()count_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modePad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0dilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapeUnknown AutoPadType Stringonnxruntime::StringToAutoPadType`InlinedVector::at(size_type)` failed bounds checkinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizepinfo.GetAttr("p", &p_).IsOK()Unsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsInput dimension cannot be less than 3.onnxruntime::PoolBase::Computeonnxruntime::PoolProcessContext::initC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hkernel_shape num_dims is not compatible with X num_dims.IC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.onnxruntime::Pool::Computeonnxruntime::LpPoolV18::LpPoolV18C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.hUnsupported kernel dimension : Unsupported pooling size : onnxruntime::LpPoolV18::Computeonnxruntime::MaxPoolV8::ComputeImplonnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyC:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cclast_loop_red_size > 0must be overloaded.onnxruntime::ValidateMustBeOverloadedlast_loop_size > 0projected_index.size() > 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateKeepDimsAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1onnxruntime::ValidateFastReduceKROutput size mismatch.Only works on matrices with two dimensions.fast_shape.size() == 2fast_shape[1] == output.Shape().Size()Only works on matrices with three dimensions.fast_shape[0] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceRKRfast_shape.size() == 3onnxruntime::ValidateFastReduceKRKonnxruntime::ValidateNoTransposeReduceonnxruntime::ValidateCommonFastReduceReduction on all axes, output size should be 1.count == 1C:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.hselect_last_indexinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseUSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroinverseonesideddft_length must be a scalar value.STFTDFTT2Unsupported 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.dft_length must be greater than zero.C:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.cconnxruntime::discrete_fourier_transformUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::STFT::Computeonnxruntime::DFT::Computeframe_length == window_lengthIf both frame_length and window are set, then the size of the window must be equal to the frame_length.onnxruntime::short_time_fourier_transformsignal_components == 1 || signal_components == 2window_size < signal_sizeEnsure that the dft size is smaller than the signal.ratio input should have a single value.signal shape must end in 1 (real) or 2 (real, imaginary).C:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.honnxruntime::signal::get_scalar_value_from_tensortensor->Shape().Size() == 1onnxruntime::dft_bluestein_z_chirponnxruntime::bit_reverseUnsupported bit size.@ `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_datatypeHammingWindowHannWindowMelWeightMatrixT3BlackmanWindowonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.upper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.C:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cconnxruntime::CreateMelWeightMatrix::operator ()lowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.C:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.h but expected scan_input_axesscan_output_directionsscan_input_directionsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.gsl::narrow_cast(output_axes_.size()) == num_scan_outputsSubgraph SessionState was not found for 'body' attribute.onnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_CreateFeedsFetchesManager must be called prior to execution of graph.info.GetAttr("body", &proto).IsOK()bodyinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()num_scan_inputsC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Initoutput_mlvalueOutput OrtValue has not been created for loop state variable output onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::ScanImpl::ExecuteScanonnxruntime::ScanImpl::TransposeOutput Expected dimensions or more but input had shape of onnxruntime::ScanImpl::Initializesession_state but input '' dimension has length of Invalid scan input:Invalid value in scan_input_axes for input of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was Subgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputonnxruntime::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 >::ComputeNo attribute with name:''is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'unknown kernel typeC:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ElementWiseRangedTransform::Createonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelgammaC:\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::InitC:\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 >::ElementWiseKernel does not match input batch size Grid batch size grid_dims[0] == Nonnxruntime::GridSample::ComputeOnly 4-D tensor is supportedC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cc, expect 2Last dimension of grid: grid_dims[3] == 2bilinearmode "mode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"onnxruntime::GridSample::GridSampleC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.hzerospadding_modebicubic" not supported, expect bilinear, nearest or bicubicpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"borderreflection" not supported, expect zeros, border or reflectionpadding_mode "RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptronnxruntime::RoiAlignBase::RoiAlignBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthavg specified. It should be either avg or maxInvalid mode of value will be different.and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign The existing summation for max mode and sampling ratios besides 1 is incorrect sampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaleNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 1 has unknown shape: onnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::Execute was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output All scan outputs MUST be tensorslast_outputs[j + 1].IsTensor()onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::LoopImpl::ConcatenateLoopOutputGraph in 'body' attribute of Loop should have static_cast(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.ccLoop inputs. Found: Got: Expected:Inconsistent shape in loop output for output. onnxruntime::Loop::Init but has outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputsIf outputs. outputs which doesn't match the subgraph's 'If' node has info.GetAttr("then_branch", &proto).IsOK()onnxruntime::If::Initelse_branchinfo.GetAttr("else_branch", &proto).IsOK()num_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Info::InfoC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.ccthen_branch' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::InitializeFailed to create output tensor for info == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::ComputeOnly tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for If output onnxruntime::IfImpl::ExecuteUnable to get an allocatoronnxruntime::IdentityOp<1>::Computeonnxruntime::IdentityOp<0>::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hbad allocationInvalid input scale: NumDimensions() != dimension != Invalid input scale: 0th dimension != !is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))Invalid number of outputs for BN trainingonnxruntime::BatchNorm::ComputeInvalid input mean: 0th dimension != Invalid input mean: NumDimensions() != Invalid input var: NumDimensions() != Invalid input mean: Invalid input B: NumDimensions() != Invalid input scale: Invalid input B: Invalid input B: 0th dimension != C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::ComputeInvalid input var: Invalid input var: 0th dimension != momentumtraining_modeis_spatial_Training mode only supports spatial BNonnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormbidirectionalreverseforwardC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::ComputeRNNAffinelayoutclipactivation_betaactivation_alphainfo.GetAttrs("activations", activations_).IsOK()activationsonnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hBatchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.allowed_activations.find(activations_[direction]) != allowed_activations.end()layout_ == 0info.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizeRNN op: Invalid activation attribute - activations_.size() == static_cast(num_directions)info.GetAttr("direction", &direction).IsOK()C:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of 'C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast(num_directions_) * 3tanhonnxruntime::DeepCpuLstmOp::PrePackLSTMclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0sigmoidinput_forget'. Must be one of 'forward', 'reverse', or 'bidirectional'.LSTM operator does not support double yetC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeGRUactivation_func_names.size() == static_cast(num_directions_) * 2Invalid data type for GRU operator of GRU operator does not support double yetonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_Can not get unpacked span from prepacked weightsA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endlda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::SafeRawConstPointercur + size <= endonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())size != 0 && (input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:gsl::narrow_cast(input_shape.Size()) == sizeA dimension cannot be less than -1, got The dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1, requested shape:i < input_shape.NumDimensions()onnxruntime::ReshapeHelper::ReshapeHelperrequested_shape[i] >= -1At most one dimension can be -1.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hshapeTensor->Shape().NumDimensions() == 1A shape tensor must be a vector tensor.C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape::Computeonnxruntime::Reshape_1::Reshape_1Attribute shape is not set.C:\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 2C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.ccCumSumAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`Axis tensor must be provided to the CumSum oponnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.ccCannot apply CumSum operator on a scalaronnxruntime::CumSum<__int64>::Computestarts.size()=exclusivedims.size() == steps.size()onnxruntime::WritableSliceIterator::Initdims.size() == extents_.size()steps.size()=onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::Softmax::ComputeImplOpset13C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13. N=Hardmax inputs N, D and N * D must be < C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=onnxruntime::Hardmax::Computeindices tensor data type not supportedonnxruntime::GatherND::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrGatherNDindicesbatch_dimsindices tensor must has rank larger than 0invalid index found, index = onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computefailed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccX input is required!Null input ptronnxruntime::DepthToSpace::DepthToSpaceDCRDepthToSpace 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_sizeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceDepth ops require a 4-D input. Provided rank: info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseblocksizeAttribute blocksize is not set.Unsupported input type in SpaceToDepth op: onnxruntime::DepthToSpace::Computeonnxruntime::SpaceToDepth::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccinput count mismatchUnsupported input type in DepthToSpace op: GatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElementsC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsGatherElements op: Out of range value in index tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_implGatherElements op: Unsupported tensor type, size:onnxruntime::GetIndexC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::ComputeTile doesn't have an implementation yet for the type: 'repeat' input tensor must have the same length as the 'input' tensor'repeat' input tensor must be 1 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1Dimension of input must be 1 instead of . shape=onnxruntime::Squeeze::Compute'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()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesC:\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 attributeonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of onnxruntime::SliceBase::ComputeCannot slice scalarsData 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::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeCannot concatenate scalarsinput != nullptrC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputenew_axisonnxruntime::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: got: onnxruntime::Dropout::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeX and mask should have the same shapeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeseed0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MeanVarianceNormalization CPU EP only supports NHW and NCHW reduction for axes attribute.onnxruntime::MeanVarianceNormalization_1::MeanVarianceNormalization_1C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hacross_channelsinfo.GetAttr("across_channels", &across_channels_).IsOK()X->Shape().NumDimensions() == 4onnxruntime::LRN::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRNC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()sizebeta_ > 0.0fbiasonnxruntime::Flatten::Computeonnxruntime::Flatten::FlattenC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQC:\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)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::ComputeSizeleft.NumDimensions() == 2 || left.NumDimensions() == 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.honnxruntime::GemmHelper::GemmHelperonnxruntime::GemmBase::GemmBaseinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.h N:right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: M_ >= 0 && K_ > 0 && N_ >= 0Gemm: Invalid bias shape for broadcastonnxruntime::GemmBroadcastBiasc_shape != nullptrc_shape is required if c_data is providedleft_num_dims and right_num_dims must be >= 1C:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computenum_dims_with_pad - 2 != num_output_dimsTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueMatMul dimension mismatchleft operand cannot broadcast on dim left_num_dims > 2 && left_num_dims == right_num_dimsnum_dims_with_pad != num_output_dimsright operand cannot broadcast on dim num_dims_with_pad - 1 != num_output_dimsM_ == 1 && N_ == 1 was falseonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computemin_ <= max_C:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Baseonnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()min should be a scalar.onnxruntime::Clip::ComputeImpl::operator ()min->Shape().IsScalar()max should be a scalar.C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccinvalid stod argumentstod argument out of rangeinvalid stoll argumentinvalid stoull argumentstoll argument out of rangestoull argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorAttribute to is not set.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::Cast::Castsnprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1-INFNaN%.8gINFsnprintf_result > 0snprintf() failed with return value: Failed to write value with snprintf().onnxruntime::`anonymous-namespace'::CastToStringinvalid expand shapeEinsum op: An implementation for the input type C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetonnxruntime::Einsum::EinsumC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()There was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeEinsumEinsum op: There must be atleast one input->equationInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsOneHotInvalid argument for depth; it's not a scalar.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::ComputeDepth is negative.onnxruntime::OneHotOp::Computesortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()TopKonnxruntime::TopkOpset9ConstructorCommonC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cckop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommononnxruntime::TopkOpset11ConstructorCommonk_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()] should not be greater than specified axis dim value [input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1k argument [input count mismatch, expected 1 input - the tensor to be processedoutput count mismatch, expected 2 outputs to be present for TopK operatorvalue of k must not be negativeUniqueUnsupported tensor type of start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!onnxruntime::GetSeqIdxC:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLengthSequenceAtonnxruntime::SequenceAt::ComputeSequenceEmpty) specified for sequence of size (Invalid sequence index (Sonnxruntime::GetSplitSizesInputInvalid data type for split tensor onnxruntime::GetScalarSplitInputViolation of the requirment that all input tensors must have the same data type.SplitToSequencenum_inputs >= 1onnxruntime::SequenceConstruct::Compute) != split_dim_size (split_size_sum ( yetSplitToSequence operator does not support SequenceInsertonnxruntime::CloneTensorUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSequenceEraseSequenceConstruct), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (split_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; })C:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeGot nullptr for sequence input.ConcatFromSequenceauto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes kernel_shape: W: C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::ConvAttributesdim_size > 0C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapeoutput_shapeoutput_paddingInvalid input shape: filter number not equal to input channel number. filter_number: num_input_channels: group count is <= 0 X: kernel_shape is not compatible with W shape. channels_last: kernel_shape num_dims is not compatible with W num_dims. group: X num_dims does not match W num_dims.C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.Input channels C is not equal to kernel channels * group. C: kernel channels: onnxruntime::ComputePadAndOutputShapeoutput and sum shape must matchC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::ComputeNot enough elements in kernel shape. Expected: Not enough elements in strides. Expected: Output channels M is not divisible by group. M: onnxruntime::ConvAttributes::InferPadsAndOutputShapeNot enough elements in pads. Expected: Not enough elements in dilations. Expected: info.GetAttrs("kernel_shape", kernel_shape_).IsOK()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolstrides_.size() == kernel_shape_.size()output_shape is smaller than minimum required. output_shape:onnxruntime::MaxUnpool::Compute 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.C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccDetC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::ComputeMatrix dimensions are not equal. Square matrix is expectedCompressC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()x_ptr != nullptronnxruntime::DynamicQuantizeLinear::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.cconnxruntime::ReverseSequenceOp::ReverseSequenceOpC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axis. Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of Unknown tensor type of onnxruntime::ReverseSequenceOp::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:Invalid sequence length: . Value must be in range [0,Failed to obtain detect_negativeFailed to obtain detect_positiveonnxruntime::IsInf::IsInfC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativedetect_positiveT4QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)onnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccprepacked_buffers[0].get() == nullptronnxruntime::QLinearConv::UseSharedPrePackedBuffersW_zero_point_data[i] == W_zero_point_valueonnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::ComputeOffsetIsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be sameQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeOffsetQLinearConv : result zero point must be a scalar or 1D tensor of size 1QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)ConvIntegerMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::Computeonnxruntime::MatMulComputeHelper::Compute::::operator ()Per-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::MatMulInteger::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccMatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1*en-USC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccattribute is_case_sensitive is not setis_case_sensitive:Please, install necessary language-pack-XX and configure localesConversion Erroronnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name:Conversion ErrorStringNormalizerInput contains invalid utf8 charsInput dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charsp.secondSingle dimension value must be greater than 0wstr != wconv_errorEmpty stopwords not allowedstopwordsDuplicate stopwords not allowed!sw.empty()NONEUPPERlocaleattribute case_change_action has invalid valuecase_change_actiononnxruntime::StringNormalizer::StringNormalizerLOWERattribute case_change_action is not setbad conversionUpsample: input/output value's dimension mismatchResize: input/output value's dimension mismatchUpsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensiononnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeInput dim is zero but required output dim is non-zero. Upsample: input/output value is nullptrResize: input/output value is nullptr Input dim value: Output dim value: Cannot scale 0 by any factor to generate a non-zero value. Dimension: Resize: input tensor's rank does not match the output tensor's rank.axes should be less than output_dims.size()onnxruntime::UpsampleBase::ParseSizesDataonnxruntime::Upsample::ComputeEither scales or sizes MUST be provided as input.Only one of scales or sizes must be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeInvalid roi input index.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseCompute: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 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.Upsample: unexpected modeResize: unexpected modeUpsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.4-D input with innermost scale (usually channel of NHWC) as 1.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.onnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.onnxruntime::UpsampleNearest is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizerTFmode is requiredTfIdfVectorizerimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: pool_strings must not be empty if specifiedpool_stringsweightsstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; }) but ngram_indexes size: must be of equal sizeNon-empty ngram_indexes is requiredngram_indexesNegative ngram_indexes values are not allowedstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: Non-empty ngram_counts is requiredngram_counts <= status.IsOK() && !impl_->ngram_counts_.empty()max_skip_count is requiredmax_skip_countimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: >= max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: min_gram_length is requiredmin_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: Input shape must have either [C] or [B,C] dimensions with B > 0.(items % ngram_size == 0)n-gram counts out of bounds for -gramsNumber of items must compose whole end_idx >= start_idx && end_idx <= total_itemspool_int64s!pool_strings.empty()status.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not provided id: p.first->second->id_ == 0Duplicate ngram detected, size: onnxruntime::ngram_details::PopulateGramsop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()lambdop_kernel_info.GetAttr("bias", &bias_temp).IsOK()C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::ShrinkEyeLikeEyeLike : Input tensor dimension is not 2Must have a single dimension of 1t_proto_p->dims()[0] == 1t_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseC:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensionMust have a valid input shape.onnxruntime::ConstantOfShapeBase >::PrepareComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: onnxruntime::`anonymous-namespace'::ConstantOfShape::Computeutils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoonnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: !utils::HasExternalData(t_proto)Unsupported value attribute datatype: ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.C:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executep_mlvalueInvalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::AllocateOutputTensors did not match batch size of sequence_lens length of Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::ValidateInput has batch size of but onnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initializedirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::InitC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccScan<8> spec does not support transpose of output. This should never be called.roi_batch_id < batch_sizeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0R->Shape()[1] == 5onnxruntime::RoiPool::ComputeMaxRoiPoolinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0pooled_height_ > 0pooled_width_ > 0C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2info.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolpooled_shapeLpNormalizationonnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNormC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()Mismatch between input data and B: size of B != input channel count Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Invalid input scale: number of dimensions is not 1: vs. Invalid input data: number of dimensions is less than 3: onnxruntime::InstanceNorm::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::InstanceNormC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).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::RandomNormalC:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hmeaninfo.GetAttr("mean", &mean_).IsOK()num_samples is < 1Invalid data type of batch_size is < 1num_classes is < 1onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::ComputeONNX_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()Output type not supported in this build: onnxruntime::MultinomialComputeonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsSUMSOFTMAX_ZEROSOFTMAXLOGISTICTreeEnsembleRegressorclass_weights_as_tensornodes_values_as_tensornodes_hitrates_as_tensortarget_weightsbase_values_as_tensortarget_nodeidstarget_treeidstarget_idsnodes_valuesnodes_truenodeidsnodes_treeidsnodes_nodeidsnodes_modesnodes_missing_value_tracks_truenodes_hitratesnodes_featureidsnodes_falsenodeidsbase_valuesInput shape needs to be at least a single dimension.onnxruntime::ml::detail::TreeEnsembleCommon::Initbase_values_as_tensor-onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorNode in tree is already there.nodes_modes.size() < std::numeric_limits::max()base_values.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()target_class_ids.size() == target_class_treeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_featureids.size()n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble.aggregate_functionn_targetspost_transformtarget_weightstarget_weights_as_tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMax::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggOne path in the graph requests feature but input tensor has features.onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggTreeEnsemble only works on 1D, 2D tensors.falsenode_ids[i] != itruenode_ids[i] != iTarget is missing for leaf (weights). (falsenode).A node cannot point to itself: Unable to find node (truenode).(coor.node_id >= 0 && coor.node_id < n_nodes_)onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionTreeEnsembleClassifierclasslabels_int64sbase_values_as_tensorclasslabels_stringsclass_weightsclass_nodeidsclass_treeidsclass_idsX dims is empty.onnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeclasslabels_int64sonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScorespredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::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() == 1LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()default_stringC:\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)onnxruntime::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. 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,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2Input features_per_batch[] != number of classlabels[Zipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countC:\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.ZipMapSVMRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramskernel_typePOLYLINEARUnexpected mode:C:\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_supportsSVMClassifiervectors_per_classprob_aprob_bonnxruntime::ml::SVMClassifier::SVMClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0onnxruntime::ml::SVMClassifier::ComputeUnsupported data type of C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hscores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of ScaleroffsetEmpty scale in attributes!scale_.empty()onnxruntime::ml::ScalerOp::ScalerOpC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (Scale size: (scale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpInvalid argument: input has empty dimensions.) or 1Either both scale and offset can be of feature size (OneHotEncoderUnknown Category and zeros = 0.cats_int64scats_stringsOne and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpC:\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::NormalizerC:\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::LinearRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierC:\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::CastInputToFloatimputed_value_floatsImputerreplaced_value_floatimputed_value_int64sonnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccMust provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Invalid typeEmpty input dimensions.Empty value of imputed values.inputdimensionsstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer) does not match number of inputdimensions values (FeatureVectorizerinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs (C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::ComputeInvalid input type:DictVectorizerint64_vocabularystring_vocabularyonnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()C:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMappernum_entries == int_categories.size()info.GetAttrs("cats_int64s", int_categories).IsOK()Input of string must have output of int64CategoryMapperInput of int64 must have output of string stof argument out of rangeinvalid stof argument%fmax_mapinfo.GetAttr("map_form", &attr).IsOK()max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()CastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0Invalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of 0.fTO_STRINGTO_FLOAT Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64onnxruntime::ml::MakeCastInvalid CAST_TO value of SPARSEDENSEInvalid PACK_MAP value of Expected DENSE or SPARSEcast_toonnxruntime::ml::MakePackonnxruntime::ml::CastMap::CastMapinfo.GetAttr("cast_to", &attr).IsOK()map_formC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hcur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.cconnxruntime::ml::CastMap::ComputeImpl is NaNInput data with index: BinarizerthresholdArrayFeatureExtractorInvalid argument: X input has empty dimensions.) >=Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] (op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LayerNormImpl::LayerNormImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.ccSize of X.shape()[axis:] == onnxruntime::`anonymous-namespace'::ComputeImpl and bias size of . Size of scale and bias (if provided) must match this. Got scale size of inputs but Scan was only given onnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::Info::InfoC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.ccThe subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsdirections.size() == num_entriesonnxruntime::scan::detail::ReadDirections' was Number of entries in 'valid did not.'. 0 == forward. 1 == reverse.Invalid values in 'onnxruntime::scan::detail::CreateFeedsFetchesManagerSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::AllocateOutputMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::Initializeonnxruntime::scan::detail::LoopStateVariable::Next is not compatible with If shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_Failed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferonnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_Unsupported typeInput W must have shape {Input R must have shape {Input X must have 3 dimensions only. Actual:}. Actual:Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {Input B must have shape {Input sequence_lens must have shape {leakyreluthresholdedreluaffinereluelusoftsignscaledtanhhardsigmoidonnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)A + (M * K) <= A_endweights.quant_para_Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInput initial_c must have shape {Input P must have shape {onnxruntime::LSTMBase::ComputeImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < onnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputLeft shape: Left shape override: C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cconnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessleft.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Right shape: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_rank == right_rankRanks of pair-wise operands must be equal. onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputcandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputEinsum op: Input shapes do not alignonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: candidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Found a '.' not part of an ellipsis in the output subscript providedInputs have ellipses in them but the provided output subscript does not contain an ellipsis...dim_iter == rankOutput subscript contains letters not seen in the inputsOutput subscript contains repeated lettersFound '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the inputC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.cconnxruntime::EinsumComputePreprocessor::RunEllipsis must indicate a fixed number of dimensions across all inputsEinsum subscripts string contains too many subscript labels when compared to the rank of the inputFound a '.' not part of an ellipsis in input: Number of subscripts in the input equation does not match number of input tensorsAnother operand has a dim value of in the same dimensionThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Found '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: The broadcasted dimensions of the inputs are incompatibleonnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsnum_broadcasted_indices < num_of_ellipsis_dims_Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyoutput.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapeEinsum op: Unsupported data type for Diagonal onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsinput_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalrank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Cannot parse the diagonal elements along dims for input shape Length of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::IsTransposeRequiredinput_rank == permutation.size()The rank of the input must match permutation size for TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::TransposeData types of the inputs must match for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Only 1 batch dimension is allowed for MatMulonnxruntime::EinsumOp::MatMulinput_1.DataType() == input_2.DataType()input_shape_1_override[2] == input_shape_2_override[1]Incompatible matrix dimensions for matMulinput_shape_1_override[0] == input_shape_2_override[0]Batch dimension should match for MatMul;Einsum op: Exception during MatMul operation: Inverseinput is expected to have 3 or 2 dimensions, got beta is expected to have 1 dimension, got Last dimension of gamma and input does not matchgamma is expected to have 1 dimension, got skip is expected to have same shape as inputLast dimension of bias and input does not matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormepsilon_ >= 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccScaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("alpha", &alpha_).IsOK()C:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler::ImageScalerC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()DynamicSliceAttribute border needs to be specified with four border elements, got input dimensions instead) needs to be greater than or equal to the leftBorder (Input's width () + scale_[0] () + scale_[1] () + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder (Croponnxruntime::contrib::Crop::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.hmin_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0min_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()onnxruntime::contrib::BifurcationDetector::BifurcationDetectorC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hBifurcationDetectormax_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_dataonnxruntime::contrib::BifurcationDetector::Computepred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)ngram_sizeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computescores_dims.size() == 2scores_dims[0] == batch_sizeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0token_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()TidNGramRepeatBlockonnxruntime::contrib::BiasGelu::Computeuse_approximationC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::ComputeCDistonnxruntime::contrib::CDist::CDistInput shape dimensions mismatch:The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDisteuclideansqeuclideaninfo.GetAttr("metric", &metric).IsOK()metricCropAndResizeNull crop_size_ptrNumber of dimensions for crop size should be exactly 1onnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskTransposeMatMulpositiveMurmurHash3sizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccInvalid assumption of output element sizekeysonnxruntime::contrib::MurmurHash3::ComputeCurrently support only COO and CSR(x64) formatsExpecting the same number NNZ == size of Inner indicesOuter size must be M + 1Expecting COO 2-D indices shapeExpecting 2xValues == indices vs inner_B: Can not multiply A and B as inner dimension does not match. inner_A: onnxruntime::contrib::SparseToDenseMatMul::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccSparseToDenseMatMulCurrently supporting only 2-D matricesCOO m index: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()COO k index: is out of bounds of out_left: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator () is out of bounds of lhs_right: COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()embedding_sizeconv_window_sizechar_embedding_size conv filter size: embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Char embedding size: char_embedding_size attribute: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute .Conv filter size does not match embedding_size attribute. conv kernal size 1: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbeddingChar embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeInput string contains invalid utf8 charsCan 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()Input dimensions are either [C] or [N][C] allowedtensor(string) expected as inputInput string contains invalid utf8 chars: Match contains invalid utf8 chars: attribute pad_value is not setmincharnumC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerTokenizermarkExpecting a non-empty tokenexp!tokenexp.empty()tokenexpEither one of the separators OR tokenexp attributes required but none is setmincharnum_ > 0separatorsattribute mincharnum is not setattribute mincharnum must have a positive valueAttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpAttention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got: found!], while Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention v weight shape error! Expected:{, aw_attn_size}. Got:Attention query layer weight shape error! Expected:{}. Got: Attention memory layer weight shape error! Expected:{}, Got: onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplparameters_.model_type == IGenerationParameters::kModelTypeGptSampling is requiredNode input should be a scalar. Got shape of gpt_subgraph_ == nullptrinfo.GetAttr("decoder", &proto).IsOK()decoderinit_decoderinfo.GetAttr("encoder", &proto).IsOK()encoderC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.cconnxruntime::contrib::transformers::Sampling::Initinit_run_decoder_session_statedecoder_feeds_fetches_manager_onnxruntime::contrib::transformers::Sampling::Computedecoder_session_stateSubgraph SessionState was not found for 'decoder' attribute.Not Implementedinit_run_gpt_subgraph_ == nullptronnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGptC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGptcuda_device_arch_ >= 530Decoder masked self attention can only be used on GPU cards of compute capability 5.3 or higher. This card has compute capability init_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_decoder_feeds_fetches_manager_onnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsmin_lengthmax_lengthC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.hInput 'prefix_vocab_mask' is expected to be 2 dimensions, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got Input 'presence_mask' shape[1] shall be vocab_size, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got input_ids and prefix_vocab_mask must have the same batch_sizeMultiHeadAttentionqkv_hidden_sizesdo_rotaryC:\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_bufferonnxruntime::contrib::MultiHeadAttention::MultiHeadAttentionInput 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 0 should be batch_size, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'query' is expected to have 3 or 5 dimensions, got onnxruntime::contrib::MultiHeadAttention::ComputePacked KV not implemented for CPUPacked QKV of shape (B, L, N, 3, H) not implemented for CPU(key != nullptr && value != nullptr) || (past_key != nullptr && past_value != nullptr)C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.cc4D mask in attention cpu kernel is not supportedInput 'key' is expected to have 3, 4, or 5 dimensions, got Input 'query' is expected to have 3 dimensions when key is given, got Input 'past_key' and 'past_value' shall be both present or both absentpast_sequence_length tensor must be of one element when past_present_share_buffer is setInput 'past_value' dimension 3 should be same as head_size, got Input 'past_key' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length)Input 'past_value' dimension 1 should be same as number of heads, got Input 'bias' is expected to have 1 dimension, got Expect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kvInput 'query' is expected to have 3 or 5 dimensions when key is empty, got Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyExpect 'value' be none when 'key' has packed kv format.Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvInput 'query' and 'key' shall have same dim 2 (hidden_size)Input 'query' and 'key' shall have same dim 0 (batch size)Input 'relative_position_bias' dimension 0 should be batch_size or 1, got Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'query' and 'value' shall have same dim 0 (batch_size)Input 'value' is expected to have 3 or 4 dimensions, got Input 'key_padding_mask' shape shall be (batch_size) or (batch_size, kv_sequence_length)'bias' is not allowed for packed kv. onnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AddBiasReshapeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttentionInput 'relative_position_bias' dimension 3 should be same as total_sequence_length, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got onnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGptonnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGptonnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.cconnxruntime::contrib::transformers::GreedySearch::InitGreedySearchonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()C:\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 []. Axis is onnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeModel type is not supported.Decoder masked multihead attention can only be used on GPU cards of compute capability 5.3 or higher. This card has compute capability onnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5C:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5onnxruntime::contrib::transformers::BeamSearchGpt::BeamSearchGptC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchGpt::BeamSearchGptt5_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::ComputeSubgraph SessionState was not found for 'encoder' attribute.encoder_session_stateencoder_feeds_fetches_manager_BeamSearchparameters_.model_type == IGenerationParameters::kModelTypeGpt || parameters_.model_type == IGenerationParameters::kModelTypeT5 || parameters_.model_type == IGenerationParameters::kModelTypeWhisperonnxruntime::contrib::transformers::BeamSearch::InitC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::InitializeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.hparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchT5::Executeonnxruntime::contrib::transformers::BeamSearchT5::Executenum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.onnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::Initializeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::Attention::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.ccSampleOpC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::ComputeC:\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 : scale of y must be null or a scalar or 1D tensor of size 1y_scale == nullptr || IsScalarOr1ElementVector(y_scale)TATBTCTYZTYQGemm : zero point of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QGemm : scale of input b must be a scalar or 1D tensor of size 1 or Nb_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : zero point and scale of input b should have same shape sizeb_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)onnxruntime::contrib::QEmbedLayerNorm::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.ccPosition embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 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 1onnxruntime::contrib::NhwcMaxPool::Computeinput_shape.Size() > 0 || N == 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cconnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonMatmulInteger : b zero point is not validC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)IsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::MatMulIntegerToFloat::ComputeInput } for per-tensor/layer quantization or shape {, 4*W_zero_point must have shape {W_scaleR_zero_pointWeight zero point must be zeroR_scaleonnxruntime::contrib::DynamicQuantizeLSTM::PrePack} for per-channel quantization. Actual:C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.ccWeight point must be constantDynamicQuantizeLSTM : Recurrentonnxruntime::contrib::QAttention::Computeinput scale must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.QLinearAddQLinearMulMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenC:\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)MatmulInteger : 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 A_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)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)onnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedtensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)opset must be existed in attributes of QlinearSoftmaxC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.cconnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13onnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxQLinearSoftmaxQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1QLinearLeakyReluQLinearSigmoidIsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::QLinearAveragePool::ComputeImplInput x_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if giventensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinear Pooling unsupported pooling size!IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if given onnxruntime::contrib::QLinearWhere::ComputeInput scale is not float for quantized input y @ 5Input scale is not float for quantized input x @ 2tensor_x_scale->IsDataType()tensor_z_scale->IsDataType()Wrong input type encountered for zero point of quantized input @tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7There must be input_def_count == kExpected_input_count inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)Wrong input type encountered for zero point input def of x, y, ztensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()onnxruntime::contrib::QLinearWhere::QLinearWhereC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.ccQLinearWhereonnxruntime::contrib::QLinearConcat::ComputeInput scale is not float for quantized input @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()input_count_x3 >= 3 && input_count_x3 % 3 == 0QLinearConcatEach input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @Wrong input type encountered for zero point input def @onnxruntime::contrib::QLinearConcat::QLinearConcatC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.ccIsScalarOr1ElementVector(tensor_y_zero_point)C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)onnxruntime::contrib::QLinearGlobalAveragePool::Computeonnxruntime::contrib::MatMulInteger16::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptrpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcConv::NchwcConvchannels_ > 0invalid channel countscales_.size() == 4onnxruntime::contrib::NchwcUpsample::NchwcUpsampleinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinfo.GetAttr("channels", &channels_).IsOK()C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::contrib::ReorderInput::ReorderInputchannels_ <= X_shape[1]onnxruntime::contrib::ReorderOutput::Compute(channels % 4) == 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccUnsupported convolution size.(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)X_shape.NumDimensions() == 4onnxruntime::contrib::NchwcConv::ComputeUnsupported transformation mode '' for NCHWc Upsampleinfo.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1onnxruntime::contrib::ReorderInput::ComputeX_rank == 4Unsupported mode 'transformation_mode_ == TransformationMode::ASYMMETRIConnxruntime::contrib::NchwcUpsample::ComputeX_shape.size() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::contrib::NchwcPoolBase::NchwcPooln_elements > 0'.C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cconnxruntime::ml::GetNumberOfElementsAttrsOrDefaultn_dims == 1' has one dimension but is empty.proto.data_type() == proto_typeUnexpected type ((for attribute 'Attribute '' must be a vector.Attribute:'' is specified but is empty.onnxruntime::ml::GetVectorAttrsOrDefaultInput 1 is expected to have 1 dimensions, got Input 0 is expected to have 1 or more dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 rows[.. row[ [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::PrepareMemoryonnxruntime::contrib::BahdanauAttention::BahdanauAttention!normalize_not support normalize yet.prefix.size() == narrow(prefix_length)onnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::ProcessC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.ccSeed must be >= 0seed >= 0onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.ccmin_tokens_to_keeppresence_penaltycustomvocab_sizeno_repeat_ngram_sizetemperaturetop_pfilter_valuemodel_typeeos_token_idpad_token_iddecoder_start_token_idposition_idssubgraph input 1 shall be named as position_ids, got: attention_masksubgraph input 2 shall be named as attention_mask, got: onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)input_idssubgraph input 0 shall be named as input_ids, got: onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.ccinput_ids_shape.NumDimensions() == 2Invalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).Setup must be called before CreateInitialFeedssession_state_ != nullptrsubgraph input 0 (input_ids) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typesubgraph output 0 (logits) shall be float or float16 data typepresent_0subgraph input 1 shall be named as present_0, got: subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headlogitssubgraph output 0 shall be named as logits, got: past_0subgraph input 3 shall be named as past_0, got: subgraph past state is expected to have 5 dimension, got subgraph past state dimension 0 shall have length of 2subgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputBeamSearch op: An implementation for the input type onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitslast_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedselement_type == DataTypeImpl::GetType()onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputslogits_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferis_kv_cacheinput_features_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsonnxruntime::contrib::SamplingCpuHelper::SampleC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.hInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of when past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Input 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2bias_dims[0]= v_hidden_size= k_hidden_size= q_hidden_size=qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameExpect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.ccInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Attention cannot have both past and relative_position_biasonnxruntime::contrib::transformers::Subgraph::GetParameterssubgraph output present_key_self_0 is expected to have 4 dimension, got past_sequence_lengthbeam_widthonnxruntime::contrib::transformers::Subgraph::SetupC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.ccDecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentioncontext != nullptrrepetition_penalty shall be greater than 0, got max_length <= kMaxSequenceLengthrepetition_penalty > 0.0fmax_length () shall be greater than input sequence length () shall be no more than max_length > sequence_lengthC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.cconnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputsdims.size() == 2input_ids shall have 2 dimensions. Got @num_beams >= num_return_sequencesnum_return_sequences (num_return_sequences shall be a positive integer, got num_beams >= 1 && num_beams <= kMaxNumBeams) shall be be no more than num_beams (num_return_sequences >= 1input_features shall have 3 dimensions. Got onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsnum_beams shall be a positive integer no more than dims.size() == 3C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccearly_stoppingactivation_params count mismatchunimplemented activation: beta is expected to have size of beta is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1gamma is expected to have size of gamma is expected to have 1 dimensions, got word_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got position_ids's first dimension shall be 1 or batch_sizeinput_ids and position_ids shall have same sequence_lengthInput 0 and 7 (mask) shall have same shapeInput 0 and 1 shall have same shapenext_scores.size() == next_indices.size()onnxruntime::contrib::transformers::BeamSearchScorer::Processbeam_hyp.Size() >= gsl::narrow_cast(num_beams_)Batch can only be done if all beams have been generatednext_beam_scores_.empty()C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.ccnext_scores.size() == next_tokens.size()onnxruntime::contrib::transformers::BeamSearchScorer::Initializeonnxruntime::contrib::transformers::BeamHypotheses::Outputtop_k <= Size()output_sequences != nullptrhypothesis_buffer_offset_ <= hypothesis_buffer_length_beam_idx == num_beams_onnxruntime::contrib::transformers::BeamSearchScorer::Finalizesequences != nullptrdecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 0 shall be named as input_ids, got: number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder_attention_masknumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:decoder_masked_attention shall use with past_present_share_buffernumber of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:kFirstPastInputIndex currently only supports 2 or 3encoder_hidden_statesC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.cconnxruntime::contrib::transformers::T5DecoderSubgraph::ValidateInvalid hidden_states expension: has_hidden_state_ == falsedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typeonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesonnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validateexpect 3 inputs, got:expect >=6 outputs, got:C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.ccencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0encoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsencoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder_input_idsnumber of outputs expected to be 2 + 4 * layers, got:encoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph outputs 1, 2, ... shall have same data typeonnxruntime::contrib::transformers::T5EncoderSubgraph::ValidateC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.cccpu_allocator shouldn't be nullptrencoder subgraph input 0 (encoder_input_ids) shall have int32 typeonnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsonnxruntime::contrib::QlinearBuildLookupTableC:\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 X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1OrtTypeInfo::FromTypeProtoThe type is not tensor, sparse tensor, sequence, map or optional typeThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeThis TypeProto does not have ValueCase setC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccTensor types should have been handled alreadytensor_data_type != nullptrOrtTypeInfo::FromOrtValueOrtValue is TensorSequence type but has no element Tensor DataType.OpenVINO_GPUDMLCudaPinnedCudaonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\allocator.ccSpecified device is not supported.Unsupported indices_format passed`anonymous-namespace'::GetIndicesTensorOrtApis::GetTensorTypeAndShapeC:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccthe ort_value must contain a constructed tensor or sparse tensorArgument is not a tensorTensor type is undefinedOrtSequenceTypeInfo::FromTypeProtoC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.cctype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypeOrtOptionalTypeInfo::FromTypePrototype_proto is not of optional typeC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kMapTypeOrtMapTypeInfo::FromTypePrototype_proto is not of type map!onnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowonnxruntime::Tensor::Tensorp_type != nullptrtensor failed memory size calculationshape.Size() must >=0C:\a\_work\1\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::CalculateTensorStorageSizestrides.empty()Strided tensor is supported for training only for now.dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::CPUDataTransfer::CopyTensorsrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopySparseTensorsC:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopyTensorsonnxruntime::IDataTransfer::CopyTensoronnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::data_types_internal::SequenceTypeHelper::Setelem_proto != nullptrC:\a\_work\1\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::MapTypeHelper::Setvalue_proto != nullptrexpected a registered ONNX typeint8doubleboolfloat(null)onnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContainerNot implementedonnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->optional_type())onnxruntime::OptionalTypeBase::IsCompatibleMLDataType for: is not currently registered or supportedsparse tensor type tensor type is not supported(unknown type)bfloat16float16uint64int64uint32int32uint16int16uint8thisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeproto != nullptrOnly ONNX MLDataType can be registeredC:\a\_work\1\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitiononnxruntime::SparseTensor::UseCsrIndicesThis method does not expect allocator to be setOuter index count must be rows + 1 or zero. Got: rows: Expecting inner index size: the same as values size: Inner and Outer indices must either be both zero or non-zeroonnxruntime::SparseTensor::ValidateCsrIndicesdense shape must 2-D. Got: format_data_.size() == 2UExpecting two indices. Got: onnxruntime::SparseTensor::AsCsrFormat() == SparseFormat::kCsrcMust contain Csr format. Contains: allocator_ != nullptrThis method should follow a call to constructor that supplies the allocatoronnxruntime::SparseTensor::UseBlockSparseIndicesExpecting fully sparse tensors to have indices shape {0}Expecting fully sparse tensors to have value shape {0}Expecting index blocks: to be equal to values blocks: Indices shape must have dim[0] == 2Expecting indices to have 2-D shape . Got: onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got:Expecting one index. Got: onnxruntime::SparseTensor::AsBlockSparseFormat() == SparseFormat::kBlockSparseMust contain BlockSparse format. Got: onnxruntime::SparseTensor::MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataUse MakeCsrStringsMust contain Coo format. Got: SparseTensor Allocation failed for size: onnxruntime::SparseTensor::AllocateBufferValues size must be less than total buffer size: this tensor already has populated sparse_indicesonnxruntime::SparseTensor::GetSparseTensorFromOrtValuethe ort_value must contain a constructed sparse tensorC:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cconnxruntime::`anonymous-namespace'::CopyDataMust have the same size. Got src_size: dst_size: Format() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooStringsExpecting data type to be set as stringonnxruntime::SparseTensor::MakeCooDataUse MakeCooStringsNot expecting an allocator setonnxruntime::SparseTensor::UseCooIndicesSparse format must not be set. Already contains format: onnxruntime::SparseTensor::GetCooIndexDimsvalues_count == index_sizeIndex size: must be equal to or twice the values size: format_data_.size() == 1UExpecting to contain one index, got: onnxruntime::SparseTensor::AsCooFormat() == SparseFormat::kCooMust have the same shapeSrc and Dst must be of the same typeX-device copy of strings not supportedDestination must have a CPU allocator setDestination should be emptyThis instance should not be emptyonnxruntime::SparseTensor::CopyUnable to find a data transfer for copying from device type: to device type: onnxruntime::SparseTensor::MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataUse MakeBlockSparseStrings}{onnxruntime::TensorShape::Slicedimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.onnxruntime::TensorShape::SizeFromDimensionC:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimensiondimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.C:\a\_work\1\s\onnxruntime\core\framework\config_options.cconnxruntime::ConfigOptions::AddConfigEntryConfig with key [] already exists with value []. It will be overwrittenConfig value is longer than maximum length: Config key is empty or longer than maximum length 128onnxruntime::SessionOptions::AddExternalInitializersinit_num == values.size()Expecting same size spansAn OrtValue for this name has already been added: C:\a\_work\1\s\onnxruntime\core\framework\session_options.cconnxruntime::SessionOptions::AddInitializerBuffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.Received nullptr for OrtValueReceived nullptr for nameAttribute: expected to be of type: but is of type: No attribute with name: is defined.No attribute with this name is defined.Attribute name and type don't matchRequested attribute: is expected to have type: onnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_C:\a\_work\1\s\onnxruntime\core/framework/node_index_info.honnxruntime::OpKernelContext::GetOrCreateOutputMLValueCPU allocator not foundTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Invalid arg_num of . Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullC:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was null type: */_ORT_MEM_ADDR_/* are out of bounds or can not be read in full. given file_length: size to read: offset: External initializer: TensorProtoToTensor() tensor shape mismatch! can not be writen into Tensor type TensorProto type onnxruntime::utils::TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated buffer, Got The preallocated buffer is too small. Requires , external_data.length: external data size mismatch. Computed size: TensorProto: `anonymous-namespace'::ReadExternalDataForTensornullptr == p_dataonnxruntime::utils::UnpackTensorWithExternalDataImpl) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protodata overflowonnxruntime::utils::GetFileContentutils::HasExternalData(tensor_proto)onnxruntime::utils::GetExtDataFromTensorProtoGetFileLength for UnpackTensor: the pre-allocated size does not match the raw data size, expected Tensor does not have external data to read from.`anonymous-namespace'::GetExternalDataInfoC:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccExternal data type cannot be UNDEFINED or STRING.Element_size of: Unsupported sparse tensor data type of Must have a valid data typeHasDataType(dense_proto)onnxruntime::utils::DenseTensorToSparseTensorProto data_type: onnxruntime::utils::UnpackInitializerDataUnsupported type: onnxruntime::utils::TensorProtoToMLValueonnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Unsupported attribute value type of Sparse Indices raw data size does not match expected.onnxruntime::utils::CopySparseDataSparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::SparseTensorProtoToDenseTensorProto is not supported.Invalid TensorProtoinput_offset >= 0 && output_offset >= 0onnxruntime::KernelDefBuilder::VariadicAliasC:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.cc kernel_end_version: kernel start version: node_version: Version mismatch. However the types are incompatible. (node_version: in the supported version rangeKernel found kernel Encountered following errors: ( kernel is not supported in and type ( domain (Op with name (Kernel not found However the types are incompatible.Op type (This op has been implemented only for the following types (), but the node in the model has the following type (onnxruntime::`anonymous-namespace'::MatchKernelDefTypesC:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.ccstatic_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptrkernel def can't be NULL: Conflicting with a registered kernel with op versions.Failed to add kernel for capable of executing this node because the CPU execution path is deemed faster than overhead involved with execution on other EPs ORT optimization- Force fallback to CPU execution for node: Candidate for fallback CPU execution: onnxruntime::GetCpuPreferredNodes::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cckernel_info != nullptronnxruntime::GetCpuPreferredNodes Existing allocator: device:Duplicate allocator for OrtMemType:onnxruntime::AllocatorManager::InsertAllocatorReceived invalid value of arena_extend_strategy onnxruntime::CreateAllocatorC:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cc New allocator: IExecutionProvider constructor must be called with true for use_metadef_id_creatorIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by onnxruntime::IExecutionProvider::GenerateMetaDefIdmetadef_id_generator_C:\a\_work\1\s\onnxruntime\core\framework\execution_provider.cconnxruntime::IExecutionProvider::InsertAllocatorCurrently do not support dims higher than 2 dimensions: onnxruntime::sparse_utils::DenseTensorToSparseCsrUnable to convert strings tensor to a sparse tensor that not on CPUUnsupported element size: C:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccInput must be of CSR formatSupport 2-D matrices onlyExpecting inner indices to be same as nnz. Got: Unable to convert strings tensor to a sparse tensor that is not on CPUonnxruntime::sparse_utils::SparseCsrToDenseTensorinner_num == src.Values().Shape().Size()outer_num == (rows + 1)Outer indices must be M + 1. Got: Expecting indices to be equal the number of values or be twice as manyInput must be of COO format > dense_size: onnxruntime::sparse_utils::SparseCooToDenseTensoronnxruntime::sparse_utils::DenseTensorToSparseCooInvalid index: Tensor size mismatchdata_transfer registered is nullptr.There's no data transfer registered for copying tensors from to C:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopyTensorsonnxruntime::DataTransferManager::CopySparseTensorsSNPEExecutionProviderUnsupported OrtValue type.invalid allocator.allocator != nullptrFailed to find allocator for device C:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::BatchOrCopyMLValueonnxruntime::utils::FindDeviceForValueUnsupported OrtValue type to copy between device.InternalTestingExecutionProvideronnxruntime::utils::ExecuteGraphImplonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphonnxruntime::utils::CalculateStaticCopyInfoForFeedexec_plan_ptronnxruntime::utils::InitializeFeedFetchCopyInfoonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::FinalizeCopyInfoForFeedsfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::CopyInputsAcrossDevicescopy_info.size() == num_feeds, this is not supported yet.feed_streams.size() == num_feedsSubgraph has nodes running on device: while parent graph node running on device: onnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::UpdateWithParentStreamFailed to find args for kernel type string ''. If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.Kernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string.onnxruntime::KernelTypeStrResolver::RegisterOpSchemaonnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()onnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaOp schema must be available.onnxruntime::KernelTypeStrResolver::SaveToOrtFormatonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasop_kernel_type_str_args entry is null.op_kernel_type_str_args is null.kernel_type_str_args is null.op_id is null.kernel_type_str is null.kernel_type_str_args entry is null.Failed to find op_id: Invalid ORT format model.C:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.cconnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrargs entry is null.args is null.Duplicate entry for op id: Duplicate entry for kernel type str: onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrxK| has already been loaded.Caught exception while loading custom ops with message: onnxruntime::ExLibLoader::LoadExternalLibA dso with name onnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: Unloading DSO C:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoaderC:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::SetupAllocatorsonnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingonnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.onnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()').onnxruntime::SessionState::CreateKernelsfor node '' ('onnxruntime::SessionState::PruneRemovableAttributesfailed at retrieving the removable attributes'), among attributes: session.disable_prepackingsession.node_partition_config_fileAllocator already registered for . Ignoring allocator from onnxruntime::SessionState::FinalizeSessionStateRerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulatoronnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::AccumulateAllNestedSubgraphsInfo Attribute:onnxruntime::SessionState::FinalizeSessionStateImpl' OpType: Index:entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'p_op_kernelnode_index_info_.has_value()SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::CreateSubgraphSessionStateCould not find an implementation for ) node with name 'onnxruntime::VerifyEachNodeIsAssignedToAnEponnxruntime::VerifyEachNodeIsAssignedToAnEpImpl]. Number of nodes: Node placements Node(s) placed on [ All nodes 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. Using cached version of pre-packed weight for constant initializer: used in the node: (Unable to write the provided PrePackedWeights instance into the container is used by node ) and node Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input:output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for onnxruntime::SessionState::AddOutputNameToNodeInfoMappingEntry exists in node for attribute onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()removed removable attributes duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::KernelUseSharedPrePackedBuffersThe kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsallocator_for_caching.get() != nullptr+ doesn't have an implementation that can cache computed pre-packed weightsonnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()The op type of a node cannot be emptyweights_to_be_filled_in.buffers_.size() > 0 which is of op type: !op_type.empty()onnxruntime::DeviceStreamCollectionImpl::CleanUpC:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.cconnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamstream_idx < num_streams_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::GetStreambad variant access in KernelRegistryManagerThe node is not placed on any Execution Provider. Failed to find kernel for found duplicated provider (node onnxruntime::utils::GetMLDataTypeC:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccnullptr != type_protoktsronnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolverC:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.Error mapping feeds: Error mapping output names: onnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsC:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksprovider_type must be specified.!provider_type_.empty()Create state function failed. Return value:API VERSION cannot be used with this model due to its ONNX opset not being supported by the layout transformer.onnxruntime::GetCapabilityForEPonnxruntime::KernelLookup::KernelLookupC:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.h, but was not selected was inserted using the NHWC format as requested by C:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc 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.onnxruntime::PartitionOnnxFormatModelImplThe Function Style fusion is deprecated. with domain:Node 'onnxruntime::FunctionKernel::CreateC:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::PartitionOnnxFormatModelonnxruntime::PartitionOrtFormatModelImpl did not return correct number of compiled functionsonnxruntime::InlineNodesNo provider specified.onnxruntime::GraphPartitioner::Partitionsingle_node_compute_func should have 1 element.onnxruntime::PartitionOrtFormatModelX|Could not find Region for onnxruntime::BFCArena::RegionManager::RegionForentry != regions_.end()onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion initial_growth_chunk_size_bytes: max_dead_bytes_per_chunk: arena_extend_strategy: memory limit: onnxruntime::BFCArena::BFCArenaC:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc with following configs: initial_chunk_size_bytes: Creating BFCArena for BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) bins of max chunk size Creating onnxruntime::BFCArena::AllocationRegion::AllocationRegionC:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.h0 == memory_size % kMinAllocationSizep_int < base_int + memory_size_Could not find Region for: p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBin!c->in_use()onnxruntime::BFCArena::Coalescec->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesce!chunk->in_use()onnxruntime::BFCArena::FindChunkPtr The total allocated bytes is now bytes. !c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use() && c1->stream == c2->stream BFC Arena shrunk by onnxruntime::BFCArena::Shrinkonnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::Mergec2->prev == h1 size: Reserving memory in BFCArena for h != kInvalidChunkHandleonnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::AllocateRawInternal (actual) rounded_bytes:tried to allocate 0 bytesExtending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes= (requested) num_bytes: . bin_num:h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)Incorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator ()cudaMallochipMallocFailed to allocate memory for requested buffer of size bytes. is smaller than requested bytes of Available memory of Total allocated bytes: Allocated memory at Extended allocation by onnxruntime::BFCArena::Extend. ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\a\_work\1\s\onnxruntime\core/framework/execution_frame.hoffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperInvalid allocation kind: for output We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.onnxruntime::ExecutionFrame::VerifyOutputSizesonnxruntime::ExecutionFrame::ReleaseMLValueImpl does not match actual shape of Expected shape from model of TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocate failed: size= 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 actual size is: Tensor shape cannot contain any negative valueonnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperFailed to get allocator for , block in memory pattern size is: For ort_value with index: onnxruntime::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.alloc && alloc.get() != nullptr Requested shape:OrtValue shape verification failed. Current shape:node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameshape && 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:Could not find OrtValue with idx 'allocator is nullptrC:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccnode_index_info and ort_value_idx_map are out of sync and cannot be usedallocator.get() != nullptronnxruntime::AsStreamBasedAllocatorTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::TraceFreeonnxruntime::NodeIndexInfo::Init::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.C:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Create. Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:onnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeInvalid dim0_offset of onnxruntime::OrtValueTensorSlicer::Iterator::Iteratorort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was onnxruntime::LibraryHandles::UnloadLibrariesC:\a\_work\1\s\onnxruntime\core\framework\library_handles.ccC:\a\_work\1\s\onnxruntime\core\framework\endian_utils.cconnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatch failedlocationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infomodel format error!checksumlengthparsing model format error! Missing 'location'entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.":[]}C:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalC:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SessionScope::SessionScopeBegin execution{"_fence_beforeop_nameSequentialExecutor::Executeonnxruntime::ExecuteThePlanNumber of streams: output_sizeparameter_sizeactivation_sizenode_index_kernel_timethread_scheduling_statsoutput_type_shapeinput_type_shape' Status Message: onnxruntime::ExecuteKernelAsync Kernel Support is not implemented yet._fence_afterstream launch kernel with idx Non-zero status code returned while running node. Name:'onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan!IsNonTensor(*node_output)Only tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::ComputeValueLocation::::operator ()found_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::ComputeReuseCountCan not find the execution provider onnxruntime::PlannerImpl::ComputeValueLocationp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defonnxruntime::PlannerImpl::GeneratePlanForWeightsHelperspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()onnxruntime::PlannerImpl::GetLocationForNodeInputonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream reused by as inputonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()invalid value index: against size onnxruntime::PlannerImpl::IndexC:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccn >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()reused != reused_foronnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlanCan not find the node onnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseonnxruntime::PlannerImpl::Reuse : onnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:": { {}onnxruntime::IGraphPartitioner::CreateGraphPartitionerCaught exception when reading partition config file: onnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception during saving DeviceBasedPartitioner config: Use DeviceBasedPartition as defaultcannot use insert() with onnxruntime::PlannerImpl::CreatePlantrigger_point_it != node_to_trigger_points.end()execution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()onnxruntime::PlannerImpl::BuildExecutionPlaniter != node_stream_map.end()Failed to find node "" in node-stream mapDeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::InitializePartitioner type is not DeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::PartitionGraphnode_names_by_stream_.size() == device_types_.size()Number of streams does not equal to number of device types!devicesstreamsinvalid UTF-8 byte at index : 0x\u%04x�incomplete UTF-8 string; last byte: 0xcannot use operator[] with a numeric argument with [ []":, {"bytes":["subtype": ], "bytes": [null}],"subtype":cannot use operator[] with a string argument with 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 ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsstring tensor is not supported for copying between allocatorsFailed to copy tensor to ) is different from what is supplied () because the ORT planned memory location device [Memory] SessionStateInitializer statically allocates Skipping entry for missing optional value at idx Using user supplied initializer with name ( failed.OrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for C:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.ccDeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::DeserializeTensorProtosession.use_device_allocator_for_initializersFailed memory size calculationonnxruntime::session_state_utils::ExtDataTensorProtoToTensoronnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node. input with name SubgraphDeserialize tensor Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()GraphUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorC:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccC:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error. is not foundMem pattern for initializer Failed to get allocator for location: duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanonnxruntime::TensorAllocatorWithMemPattern::Trace' is not foundWeight buffer for initializer '' failedGet preallocated buffer for initializer 'onnxruntime::FuncManager::GetFuncsC:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cc already exist.func info for node: Can't use func with null ptr not found.!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationC:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.hcurrent <= buffer_size_v >= 0onnxruntime::StreamExecutionContext::CountDownBarrier::SetC:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.hsince == endonnxruntime::RunSinceonnxruntime::StreamExecutionContext::RecycleNodeInputs releasedort value Exiting due to terminate flag being set to true.idx < device_stream_map_->NumStreams()onnxruntime::StreamExecutionContext::GetDeviceStreamC:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.ccframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()wait_handle_WaitOnEPStep.wait_handle is nullC:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cconnxruntime::WaitOnEPStep::ExecuteSet a barrier with id: , count: ActivateNotificationStep: activate notification with id: onnxruntime::ActivateNotificationStep::ExecuteTriggerDownstreamStep: trigger downstream of trigger point: WaitOnEPStep: wait on notification with id: wait on Notification with id: activate notification with index Launch kernel with node id: [TypeInferenceError] ConstantNoOpC:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hTypeProto must have shape for this to runonnxruntime::utils::GetShapeshape != nullptrALLOW_RELEASED_ONNX_OPSET_ONLYDuplicate constant node sparse initializer name: '' Model is invalid.utils::HasName(sparse_tensor)Sparse initializer must have a name. This model is invalidgraph_proto cannot be nullThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graphgraph_proto != nullptror with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This is an invalid model. Tensor does not have type information.Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter . Please, fix your model.Duplicate sparse_tensor_initializer: 'Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializerfbs_attr cannot be nullonnxruntime::Node::LoadFromOrtFormatNode::LoadFromOrtFormat, input_arg_counts is missingSerialization error. Graph attribute was serialized without Graph instancenode_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()LoadNodeArgsFromOrtFormat: Node [], could not find NodeArg input edgesonnxruntime::Node::LoadEdgesFromOrtFormatoutput edgesonnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Node::LoadEdgesFromOrtFormat, edge is missing for input index: is not the same as this node's index:Optional Type mismatch. Expected: Removable attributes were removed before the conversion is started.Type mismatch. Current= Input= . Got: onnxruntime::NodeArg::UpdateTypeAndShapedoes not have the graph for key Removable attributes were removed before the node is saved.fbs_node_arg_names cannot be nullonnxruntime::Node::SaveToOrtFormat] op_type [onnxruntime::Node::ToProtoSerialization of fused function body is not currently supported, Node [Output: , or optional typed entitiesSource and target must both be tensorsonnxruntime::MergeShapeInfoError merging shape info for output. 'Tensor element type mismatch. C:\a\_work\1\s\onnxruntime\core\graph\graph.cc. Falling back to lenient merge. , or sparse tensors' source: target:) in node () is invalid.' of input parameter () of operator () does not have type information set by parent node.Node (This is an invalid model. Node () input arg (onnxruntime::Graph::InferAndVerifyTypeMatchType Error: Type parameter () output arg () type inference failed in node (This is an invalid model. Type Error: Type ') of Optype () bound to different types ( inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Size mismatch validating subgraph inputs. Got inputs but subgraph has Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.No Graph instance was found for attribute in node onnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()) Op () Subgraph input missing type.Node:UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesInvalid source node arg slot specified when removing edge.onnxruntime::Graph::RemoveEdgeArgument mismatch when removing edge.Invalid destination node arg slot specified when removing edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.Invalid node indexes specified when removing edge.Argument type mismatch when adding edge.Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.onnxruntime::Graph::KahnsTopologicalSortSome nodes are not included in the topological sort, graph have a cycle. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::BuildConnectionsThis is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=onnxruntime::Graph::InitializeStateFromModelFileGraphProtograph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()node_argGraph ctor should have created NodeArg for initializer. Missing: appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Graph state to be loaded into must be empty.Initializer This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: two nodes with same node name (onnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Subgraph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.Failed to convert dense initializer to sparseonnxruntime::Graph::ToGraphProtoCan't remove node as it still has output edges.onnxruntime::Graph::RemoveNodenode->GetOutputEdgesCount() == 0initializer_node_arg != nullptrCannot find NodeArgs for ['. It is not used by any node and should be removed from the model.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'was added but does not exist. onnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name 'Replacement tensor's dimensions do not match.Trying to replace non-external initializer with external datagraph_proto_ is not in sync with name_to_initial_tensor_Replacement tensor's data type does not match.!foundgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::ReplaceInitializedTensorImplFailed to find existing initializer with name _token_onnxruntime::Graph::SaveToOrtFormatonnxruntime::Graph::InjectExternalInitializedTensorsexisting_entry != mutable_initializers.pointer_end()Replaced external initializer: onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::PerformTypeAndShapeInferencingShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::Graph::Resolve) attribute () is required but not specified.onnxruntime::Graph::InitInputsInitializersOutputsError: Duplicate definition-site for (sparse_tensor_names_ not in sync with name_to_initial_tensor_onnxruntime::Graph::AddInitializedTensoronnxruntime::Graph::RemoveInitializedTensorsparse_tensor_names_.count(tensor_name) == 0 but different TensorProto.outer_scope_node_args_consumed.empty()existing->second == &tensorAddInitializedTensor already has tensor with name This is an invalid model. Model input () does not have type information.' has element type but usage of initializer in graph expects ) of node () does not match expected type (Type Error: Type () of output arg (onnxruntime::Graph::VerifyNodeAndOpMatchThis is an invalid model. In Node, Fatal error: ) is not a registered function/op does not match. Type Error: Data in initializer ', Error Type Error: Shape of initializer index < data_.size()Node index is out of rangeNode is missing. Invalid ORT format model.NodeEdge is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Sparse Initializer tensor is missing. Invalid ORT format model.) : (onnxruntime::Graph::SetInputs""("onnxruntime::Graph::InlineFunctioninput->Exists()Input to set must exist.onnxruntime::Graph::LoadFromOrtFormatonnxruntime::Graph::LoadFromOrtFormat::::operator ()NodeArg Name is missing. Invalid ORT format model.) -> (nodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.nullptr != func_meta_defonnxruntime::Graph::AllocateNode'. It is no longer used by any node.Removing initializer 'Removing NodeArg 'outer_scope_node_arg != nullptrdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::FinalizeFuseSubGraphcallnode.TryGetFunctionProto(inlined_fp)Node has no function body and cannot be inlined. OpType:onnxruntime::Graph::CreateFusedSubGraphNodeSetOpSchemaFromRegistryForNode(fused_node)Schema was not found for fused node. Domain:C:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::ConstPointerContainer > >::atabsl::container_internal::raw_hash_map<>::at, but it its domain is notknown by the checker., but it its version is higherthan the operator set version onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalC:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::GraphViewerToProtoC:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.ccC:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GraphViewernodeargMismatch between Graph and IndexedSubGraph. Input not found:graph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GetNodesInTopologicalOrderMismatch between Graph and IndexedSubGraph. Node not found: Mismatch between Graph and IndexedSubGraph. Output not found:Invalid ExecutionOrdernodeProtobuf parsing failed. less than 0.Failed to load model with error: onnxruntime::Model::LoadNo graph was found in the protobuf.onnxruntime::Model::SaveToOrtFormatonnxruntime::Model::LoadFromOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::Save is less than 0.Protobuf serialization failed. 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::ModelModelProto does not have a graph. is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset onnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset model may run depending upon legacy support of some older opset version operators.Unsupported model IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.C:\a\_work\1\s\onnxruntime\core\graph\model.cc, max supported IR version: Missing model IR version. are '0' and '1'. The environment variable contained the value: C:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.honnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetonnxruntime::SaveModelGraph is null. Invalid ORT format model. failed. File doesn't existsystem error number Load model gOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.Negative values are not allowed in a shape specification expected to have tensor type should specify a shape expected to have tensor or sparse typeAttribute Output Constrain input and output types to all tensor types.dataGivenTensorFillvaluesextra_shapeConstrain indices to integer typesinput_as_shapeBias applied to each channel, same size as C.The scale to apply.Constrain input and output types to float tensors.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccThreshold valueA 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Value of alphaValue of beta'Scale' must contain exactly 2 values - (height, width)) + bottom_border (Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Scaling valueIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.) + scale[1] () needs to be greater than or equal to the left_border () + scale[0] () needs to be greater than or equal to the top_border () + right_border (hidden_prevdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnithiddenseq_lengthsgatesAttribute expected to have tensor or sparse tensor typePackedAttentionkeyqueryCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)cache_indirectionInputs 0 shall be 2 dimensionsConstrain mask index to integer typesC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccpresentcumulative_sequence_lengthtoken_offsetInvalid bias shapeqkv_hidden_sizes should have 3 elementsCustom scale will be used if specified. Default value is 1/sqrt(head_size)Whether to use rotary position embedding. Default value is 0.The value to be filled in the attention mask. Default value is -10000.0fHidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Number of attention headsWhether every token can only attend to previous tokens. Default value is 0.relative_position_biaspastMError unexpected extra input in node: = Constant()Error parsing node:The past input shall be 5 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensionskey and value cache shall be 4 dimensionsinput shall be 3 dimensionsuse_paststatic_kvkey_cachevalue_cachenew_key_cachenew_value_cachehas_key_padding_maskhas_layer_stateConstrain to integer typesglobalglobal_biasGmaskglobal_weightkv_weightq_weightLongformerAttentionConstrain input and output to float tensors.Constrain mask to integer typeskey_padding_maskweightwindowOne sided attention windows length W, or half of total window lengthpresent_keypast_valuepast_keypresent_valueskipConstrain sequence_length to int tensors.RelativePositionBiasConstrain mean and inv_std_var to float tensors.input_skip_bias_suminv_std_varmax_distanceDefault value is 0.Max distanceConstrain input and output types to float or half tensors.key_lengthquery_lengthbias_tableis_bidirectionalConstrain input and output integer tensors typesConstrain input and output float tensors types.embedding_sumX_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) oneX_bias = Add (X, bias)bamask_index_typeThe epsilon value to use to avoid division by zero.The mask index tensor type for shape inference (0: None, 1: 1D mask_index)DecoderAttentionConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.segment_embeddingposition_embeddingword_embeddingsegment_idsrel_posquery_biasquery_layerRestorePaddingGatedRelativePositionBiaseco_acumulated_seq_lensequence_token_countConstrain token_offset to integer typesRemovePaddingmax_seq_lenConstrain sequence_token_count and token_offset to integer typespred_tokensprev_suffix_match_idxcur_tokenssrc_tokensGemmFastGeluWsuffix_match_idxConstrain to integer types.tokensThe NGram size.SkipSimplifiedLayerNormalizationThe maximum NGram size for suffix matching.The minimum NGram size for suffix matching.scores_outConstrain scores input and output types to float tensors.scores̼+̼+C is nullIncompatible dimensions expected to have tensor or sparse tensor type: Dimension mismatch in unification between expected to have rank > but has rank expected to have rank The inner-most 2 dimensions must have the same size (mat_w:Input rank must be >= 2. != mat_h:TorchEmbeddingConstrain input and output types to all numeric tensors.scale_grad_by_freqpadding_idxC:\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) OneFailed to parse num_return_sequences or it is not positive integer scalar CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) Alpha value.AlphaAFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarIncompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensions in whisper graphPads has incorrect number of valuesInput tensors of wrong rank (0).'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64FDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsvocab_maskrepetition_penaltyConstrain input types to all tensor types.GridIf 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.Three interpolation modes: bilinear (default), nearest and bicubic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.early stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like T5The id of the token that indicates decoding starts.no repeat ngrams sizeThe id of the end-of-sequence tokenThe id of the padding tokenConstrain output types to float tensors.isinf_onlyisnan_onlyConstrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.Constrain the output to a boolean tensor.If true, check only for Inf, -Inf.If true, check only for NaN.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.IsAllFinitetrue 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.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.ratioresidual(Optional) Seed to the random generator, if not specified we will auto generate one.activation_gammaInput axis is invalid: Scalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Whether A should be transposedWhether B should be transposedConstrain input and output types to float/int tensors.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Input/Output is a string tensorPadsConstrain input and output types to float tensorsComplexMulComplexMulConjRfftIrfftFirst input does not have rank 2Second input does not have rank 2Zpresence_maskModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartPresence penalty for custom samplingIf 1 custom sampling logicAll filtered values will be set to this float value.Minimumber of tokens we keep per batch example in the output.signal_ndimnormalizedConstrain input0 and output types to float tensorsConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.filtered_logitsConstrain to float tensors.sequences_scoressequencesprefix_vocab_maskThe value used to module the next token probabilities.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.Constrain input type to float or int tensors.'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Constrain to tensor(int32).Constrain to tensor(float).SequencexThree 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 arrayboth data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensorSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Constrain indice type to int32 or int64Constrain input and output types to any tensor type.sparse_tensor(float)activation_axisWhether 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 multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationsparse_tensor(uint32)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(double)sparse_tensor(int64)Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredinputs are expected to have tensor type and output type should not be null.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorBiased = Add (Scaled, B2D)Biased = Identity (Scaled)Scaled = Mul (NormalizedV, Scale2D)B2D = Flatten (B)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)Attribute 'max_output_boxes' must be >= 1.max_output_boxesMean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)NumReducedAxes = Neg (Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)PrefixShape = Slice (XShape, Zero1D, Axis1D)NumReducedAxes = Sub (Rank, Axis1D)Zero1D = Constant()Axis1D = Constant()VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)XU = Cast (X2D)Mean2D = ReduceMean (XU)batch_indicesroisThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. XShape = Shape (X)Rank = Size (XShape)EpsilonConstrain types to float tensors.Constrain types to int tensors.crop_sizeThe 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".countsInput can be of any tensor type.idxbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionfirst input tensor has wrong dimensionrois input tensor has wrong dimensionConstrains input to only numeric types.feature_map_3feature_map_2feature_map_1feature_map_0c2c_attentionPyramidROIAlign_TRTDisentangledAttention_TRTimage_sizePooled size.patchesImage size.iou_thresholdMax detections to output.box_codingBox IOU threshold value.background_classEncoding type for the boxes or anchors inputs.detection_classesBackground class ID.MultilevelCropAndResize_TRTScore threshold value.score_thresholdActivation function to apply to the scores input.score_activationVersion number of the TRT plugin.plugin_versionboxesConstrain output Y and scale type to float tensors.EfficientNMS_TRTConstrain output Y, scale and bias type to float tensors.Constrain mean and inv_std_var to be float tensors.detection_scoresdetection_boxesnum_detectionsanchorsThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput 0 shall be 3 dimensionspooled_sizeAttribute 'pooled_size' must be >= 1.Constrain input X type to float tensors.Type of Mean and InvStdDev tensors.InvStdDevoutputsinputs(Optional) Some notes for the modelnotes(Optional) Target device like CPU, DSP, etc.target_deviceConstrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.Constrain input and output types to uint8, uint16, float tensors.Maximum relative distance, k.spandisentangled_attentionp2c_attentionc2p_attention(Optional) SNPE version used to convert the model.snpe_versionpayload of the SNPE DLC file.DLCScaling 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.factor̼+ expected to have: or UNDEFINED. Got: Dimension=Can't merge shape info. Both source and target dimension have values but they differ. Source= should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute A_scale{additionalDocumentation} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}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.inputs are expected to have tensor type.B_zero_pointB_scaleA_zero_pointScale and Zero-point must be a scalarScale and Zero-point must be of rank 1Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).reducedb_scaleConstrain output types to 32 bit tensors.MulIntegerConstrain input types to 8 bit signed and unsigned tensors.ReduceSumIntegerShape of quantized tensor must be 1D.Constrain shape and strides to uint64.QuantizeBFPConstrain the input to float and bfloat.Constrain y to uint8.Constrain y to float and bfloat16.DequantizeBFPConstrain the input to uint8.The datatype to dequantize to.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.x_zero_pointx_scaleEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.block_dimThe type of BFP - must match with the BFPType enumbfp_typeConstrain 'y', 'x_scale' to float 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.Constrain input and output types to 8 bit signed and unsigned tensors.C_zero_pointC_scaleConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'x', 'y_scale' to float tensors.y_zero_pointy_scaleA list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Constrain input and output types to signed/unsigned int8 tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.sequence_lensR'axis' must be in []. Its actual value is: , Constrain input and output types to 8 bit tensors.Y_zero_pointapply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.reduced_zero_pointQLinearReduceMeandata_zero_pointreduced_scaledata_scaleY_scaleX_zero_pointX_scaleCoefficient of leakage.a_scaleConstrain input B data type to 8-bit integer tensor.Constrain input A, b_scale and output Y data type as float tensor.b_zero_pointmultiplicationaxis must be in [-rank, rank-1]. input rank was additionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointa_zero_pointConstrain input A data type to 8-bit integer tensor.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)Constrain output type to float32 or 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.weight_zero_pointinput_zero_pointweight_scaleinput_scalez_zero_pointz_scaleConstrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain scale types to float tensors.T8All inputs to Concat must have same rankWhich axis to concat onRequired attribute axis is missingaxis must be in [-rank, rank)conditionSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Constrain scale types to any float tensor type.TVPinitial_cinitial_hConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Y_cY_hcublasLt order of matrix Border_BcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_ADequantizeWithOrdercublasLt order of output matrixThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)scale_Bscale_AcublasLt order of matrix Y and optional matrix Corder_Yorder_outputorder_inputcublasLt order of output matrix.cublasLt 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.mask_index_outConstrain input and output types to float32 tensors.cublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.Constrain Scale to float32 typesQuantizeWithOrderQConstrain to float typesscale_inputposition_embedding_zero_pointword_embedding_zero_pointbeta_scalegamma_scalelayernorm_outbeta_zero_pointgamma_zero_pointsegment_embedding_zero_pointgamma_quantbeta_quantword_embedding_quantposition_embedding_quantConstrain input and output types to int8 tensors.segment_embedding_scaleposition_embedding_scaleword_embedding_scaleConstrain scales to float32QOrderedGelucublasLt 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.scale_biasscale_weightorder_global_weightcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.order_XQOrderedAttentionQOrderedLayerNormalizationConstrain scales to float32 tensors.scale_values_gemmquantization tensor must be int8 tensors.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization scale must be float tensors.scale_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale_Q_weightscale_K_weightV_weightQ_weightK_weightscale_V_gemmscale_QKT_softmaxscale_QKT_gemmK_biasV_biasscale_V_weightQ_biasQOrderedMatMulscale_CConstrain bias and scales to float32scale_Yscale_Q_gemmscale_K_gemmorder_weightcublasLt order of global biasHidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixQLinearConvTransposePadding for the beginning and ending along each spatial axisnumber of groups input channels and output channels are divided into.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETw_zero_pointw_scalewAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.The shape of the convolution kernel. If not present, should be inferred from input W.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsConstrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter type to 8-bit integer tensor.scale_outputBe compatible with float version.scale_global_gemmscale_global_weightscale_qkv_gemmweight and zero_point pair is expected to have same type.QOrderedLongformerAttentioninput and zero_point pair is expected to have be same type.Constrain input X and output Y types to float tensors.Activation after group normalization: 0 for None, 1 for SwishThe number of groups of channels. It should be a divisor of the number of channels Cgroupsbias shall be 1 dimensionC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccConstrain gamma and beta to float tensors.GroupNormThe epsilon value to use to avoid division by zeroBiasAddBiasSplitGeluNhwcConvdilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.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.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccWorks on NHWC layout or not? Default not.The size of the kernel along each axis.Whether to use ceil or floor (default) to compute the output shape.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.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Failed since multiple edges matched:onnxruntime::graph_utils::FindPathonnxruntime::graph_utils::AddNodeInputonnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Attempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()index >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameonnxruntime::graph_utils::AddInitializerAttempting to get an input that does not exist.Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInput ExplicitInputs:. Index:onnxruntime::graph_utils::RemoveNode ImplicitInputs:onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph in one of the subgraphs.C:\a\_work\1\s\onnxruntime\core\graph\graph_utils.ccstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0itr != node_args.end()onnxruntime::graph_utils::GetIndexFromNamefor node: Attempting to get index by a name which does not exist:Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used output cannot be safely updated to Implicit input name C:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccutils::HasName(attribute)onnxruntime::utils::SetNodeAttributeAttributeProto must have a name.onnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatAttempting to load runtime optimization records for a previously loaded optimizer: runtime optimization record produced op idonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatonnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormat is null. onnxruntime::SaveRuntimeOptimizationRecordToOrtFormatC:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.ccxonnxruntime::utils::SplitStringC:\a\_work\1\s\onnxruntime\core/common/string_utils.hdelimiter must not be empty!delimiter.empty()invalid string_view positionC:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.cconnxruntime::fbs::utils::LoadOpIdentifierOrtFormatFailed to parse since_version from onnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringC:\a\_work\1\s\onnxruntime\core/graph/op_identifier.hInvalid OpIdentifier string: INTSFLOATSGRAPHTENSORSTRINGINTFLOATUNDEFINEDSPARSE_TENSORSSPARSE_TENSORGRAPHSTENSORSSTRINGSMissing dims for sparse initializer: Missing indicies for sparse initializer: Missing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatNull strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dimensions for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatonnxruntime::fbs::utils::SaveSparseInitializerOrtFormatC:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveInitializerOrtFormatNull tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.@"4"0"(" """""x"p"`"P"TAggregatedTypesall_tensor_types_with_bfloatC:\a\_work\1\s\onnxruntime\core\graph\function_utils.cconnxruntime::function_utils::IOTypeConstraintHelper::::operator ()it != opset_imports.end()No opset registered for domain in function opset imports.onnxruntime::function_utils::CreateSchemainput_arg->Type() != nullptr is not found in model local functionsToutoutTininstatic_cast(i) < node_op_schema->inputs().size()Too many inputs for op domain_version != -1onnx_func_protoThe given function name: , domain: onnxruntime::function_utils::Inliner::bindactuals.size() <= formals.size()Number of actual parameters cannot exceed number of formal parametersC:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::FunctionImpl::FunctionImplinitializer != nullptr is not found or is not constant initializer.Inputs 0 shall be 3 dimensionsbeta should have 1 dimension, 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.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.word_embedding should have 2 dimensions and dimension size is known.segment_ids input shall be 2 dimensionsinput_ids shall be 2 dimensionsConstrain input and output types.Unsupported type:Can not get shape initializer data!deltalimitUnsupported non-raw-data data type!C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueinvalid scales dimensiontensor rank too smallAWmemory_seq_lensConstrain seq_lens to integral tensors.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.MWQWSize of affinity string must be between 1 and Failed to read affinities from affinity stringFound out-of-range processor id in affinity string: Found invalid processor id in affinity string: , skip affinity settingprocessor_id > 0std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_from <= processor_toInvalid processor interval: Received null affinity_stringReceived invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionscustom join thread function not setonnxruntime::concurrency::CreateThreadPoolHelperactual_num_affinities == static_cast(options.thread_pool_size) - 1Number of affinities does not equal to thread_pool_size minus one, affinities: , thread_pool_size: processor_from > 0 && processor_to > 0Processor id must start from 1: C:\a\_work\1\s\onnxruntime\core\util\thread_utils.cconnxruntime::concurrency::ReadThreadAffinityConfigstd::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must consist of only digits: ;C:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPositiondims[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;bad mlas activation kind) is not supported on this device), BIsSigned(Quant GEMM format: AIsSigned(QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!ElementSize must be power of 2 and less or equal than 16!    onnxruntime::ToWideStringlength overflowC:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToUTF8StringOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::Defaultonnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. onnxruntimeISink must be provided.C:\a\_work\1\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManageronnxruntime::common::Status::Statuscode != static_cast(common::OK)SystemErrorC:\a\_work\1\s\onnxruntime\core\common\status.cc[ONNXRuntimeError]MODEL_LOADEDINVALID_PROTOBUFINVALID_GRAPHNOT_IMPLEMENTEDGENERAL ERROREP_FAILFAILSUCCESSNO_MODELNO_SUCHFILERUNTIME_EXCEPTIONENGINE_ERRORC:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDir\*onnxruntime::WindowsEnv::InitializeCpuInfoFailed to calculate byte size for saving cpu info on windows consist of logical processors: Failed to fetch cpu info on windows core(s) from windows system:core Found total LoadLibrary failed with error "Kernel32.dllFreeLibrary failed with error in library, error code: K32EnumProcessModulesonnxruntime::WindowsEnv::FormatLibraryFileNameFailed to find symbol onnxruntime::WindowsEnv::DeleteFolderDeleteFile() failed - path: GetFinalPathNameByHandle() failed: RemoveDirectory() failed - path: UNC\onnxruntime::WindowsEnv::GetCanonicalPath" when trying to load ""unknown error%hs!%p: %hs(%u)\%hs!%p: %hs(%d) tid(%x) %08X %ws(caller: %p) Msg:[%ws] [%hs(%hs)] CallContext:[%hs] ReturnNtExceptionLogNtReturnHrFailFastLogHrgenericsystemonnxruntime::WindowsEnv::MapFileIntoMemory fail: unexpected end , errcode = open file mapping , mapped_offset = , allocation_granularity = \/mapped offset must be a multiple of the allocation granularityReceived negative size from stat callonnxruntime::WindowsEnv::ReadFileIntoBufferfile_path == nullptrlength > buffer.size()offset < 0ReadFile SetFilePointerEx SetThreadAffinityMask failed for thread: , index: fail, errcode = - GetFileSizeEx open file Invalid fd was supplied: Cannot set cross-group affinity for thread , first on group , group_id: , mask: . Specify the number of threads explicitly so the affinity is not set.SetThreadAffinityMask done for thread: , error code: , error msg: SetThreadDescriptionkernel32.dll does not existonnxruntimeCannot set affinity for thread , processor , then on onnxruntime::WindowsThread::ThreadMain/\C:\a\_work\1\s\onnxruntime\core\platform\windows\env.ccindex >= 0Negative thread index is not allowedcustom_create_thread_fn returned invalid handle.onnxruntime::WindowsThread::WindowsThreadWindowThread:_beginthreadex failed with message: doserrno: error msg: onnxruntime::UnmapFileunmap view of file failed. error code: [%hs] RaiseFailFastExceptionkernelbase.dll%wsFailed to parse path root: C:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRootonnxruntime::Path::Parse/\n <= num_threads_ + 1More work items than threadsC:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallelonnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection": {"num_run": !current_parallel_section.has_value()Nested parallelism not supported!ps_onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectiononnxruntime::concurrency::ThreadPool::ParallelForn >= 0Distribution], "core": RunDistributionEnqueueWaitRevokeWait"core": UnknownEvent!points_.empty()LogStart must pair with LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetpoints_.empty()"thread_id": "", "block_size": [}}C:\a\_work\1\s\onnxruntime\core\common\threadpool.cc", }, "sub_threads": {{"main_thread": {"thread_pool_name": "Profiler not started yetunnamed_thread_poolonnxruntime::concurrency::ThreadPoolProfiler::Stopenabled_"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::EndProfilingC:\a\_work\1\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::Startonnxruntime::profiling::Profiler::Initializesession_logger != nullptrNodeSessionApiKernel" : "" : }, } ] OOOOillegal input path:unexpected failure: [] , pm is not a valid year is not a valid dateam is not a valid daysequence_typemap_typetensor_typeModel must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatNull type info for onnxruntime::fbs::utils::LoadValueInfoOrtFormatopset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull sequence type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.Type:. Invalid ORT format model.Null map type info. Invalid ORT format model. is not supported currentlyNull entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorShapeOrtFormatdim_param value with no name. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatonnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::SaveTypeInfoOrtFormat] for nowonnxruntime::fbs::utils::SaveMapTypeOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for We do not support type [onnxruntime::fbs::utils::SaveValueInfoOrtFormatonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatC:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cc\~PPPRequired attribute ' has unknown expected type' is missing.Cannot find a function builder that satisfies the requested opset version: op_type = Error parsing function body:Duplicate type constraint name, opset_version = ' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'g'' is expected to have field 'type_proto'' is expected to have field 'graphs'' is expected to have field 'type_protos'' is expected to have field 'strings'' is expected to have field 'tensors'' appeared multiple times. for operator ) has more outputs ()'s output ' is expected to have field 't'' is expected to have field 'sparse_tensor'Unrecognized attribute: Mismatched attribute type in 'has output size ) in op definition.) has output size not in allowed output sizes. is marked single but has an empty string in the graph)'s input ) than declared () has more inputs (' has been deprecated since version Operator ' has inconsistent type has unsupported type ) has input size not in allowed input sizes., max= not in range [min=, has unsupported type: typestr: : failed validating the check: ONNX Schema Extra unparsed input unexpected.(inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())tensor(Invalid data type complex128float8e4m3fnDataTypeUtils::FromDataTypeString - Received invalid data type string 'complex64float8e5m2fnuzfloat8e4m3fnuzfloat8e5m2sparse_tensor(Unsuported type proto value case.optional(map(mapopaqueInvalid tensor data type optionalmap(int64, float)The output is a 1-D tensor of string, float, or integer.The input must be an integer map to either string or float.map(int64, string)If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.The input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type or string. The output will be of the same tensor type.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\defs.ccA collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Input's shape should be 1D or 2DAn integer.A float.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]Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.The input type is a tensor of any shape.A list of strings. One and only one of 'value_*'s should be set.A string.A list of ints.A list of floats.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setLabel encoder has only one input.Label encoder has only one output.Only one of values_*'s can be set in label encoder.Input type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change to.Value(s) to change toA value that needs replacing.An integer 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.A string vocabulary array.
One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type.The size of each input in the input listThe input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, float)map(string, double)map(string, int64)map(int64, double)The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.Child node if expression is true.Child node if expression is false.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'node id that this weight is for.The index of the class list that each weight is for.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Feature id for each node.Thresholds to do the splitting on for each node.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Flag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsThe 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 kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.The input must be a tensor of a numeric type, either [C] or [N,C].Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.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.One of 'MAX,' 'L1,' 'L2'List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.Exactly one of 'cats_*' attributes must be provided.The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The total number of regression targets, 1 if not defined.Weights of the model(s).Weights of the intercepts, if used.The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(string, float))seq(map(int64, float))The id of the tree that each node is in.The node id of each weightChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The index of the target that each weight is forThe weight for each targetBase values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueOnly one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.cc does not match type of output: Input: type: indices must be in [-rank, rank-1].axis must be in [-rank, rank-1]. expected to have tensor or sparse tensor type and Output optional(tensor(bool))optional(tensor(complex64))optional(tensor(double))optional(tensor(string))optional(tensor(complex128))optional(tensor(int8))optional(tensor(int16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(float16))optional(tensor(float))optional(tensor(int32))optional(tensor(int64))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(tensor(uint8))optional(tensor(uint16))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(int64)))optional(seq(tensor(float16)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))tensor(float8e5m2fnuz)tensor(float8e4m3fnuz)tensor(float8e5m2)optional(seq(tensor(uint8)))tensor(float8e4m3fn)Attribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been given) and the split dimension of the input (Mismatch between the sum of 'split' (Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). Value=concat_resultConstrain output types to any tensor type.) and outputs (Mismatch between number of splits (Invalid value of attribute 'axis'. Rank=Both 'split' input and 'num_outputs' attribute were givenConstrain output to int64 tensor, which should be a scalar though. has rank All inputs to Concat must have same rank. Input Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..reshapedInput tensor can be of arbitrary type.Constrain output to int64 tensor.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.Invalid position of 0.Invalid dimension value: Target shape may not have multiple -1 dimensions.(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.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesoutput = Cast (input)Constrain output types. Casting to complex is not supported.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.cctarget_typeThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain input types. Casting from complex is not supported.saturateDCR (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).Blocks of [blocksize, blocksize] are moved.Blocksize must be positiveInput tensor must be 4-dimensionalsqueezedList 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).data tensor must have rank >= 1axis must be in [-r, r-1]Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Input and output types can be of any tensor type.updatesType 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.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.}, input shape = {Attribute perm for Transpose has repeated value: Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposed) vs (Input rank for starts and ends should be the same: (Invalid attribute perm {steps'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsNumber of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.Input axes has incorrect lengthInput steps has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsConstrain output types to boolean tensors.Constrain input types to float tensors. 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 positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.depth(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Constrain input to only numeric types.Constrain to any tensor type.OneHot node must have three inputs.Constrain to boolean tensors.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Constrain input and output types to all tensor, sequence, and optional types.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Indices tensor must have rank >= 1'axis' must be in [-rank(indices), rank(indices)-1]Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.sizesConstrain roi type to float or double.Constrain input `X` and output `Y` types to all tensor types.Constrain grid types to float tensors.gridIf set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.roi This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .Constrain input 'X' and output 'Y' to all tensor types.The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).Constrain repeat's type to int64 tensors.repeatsThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)input_dataoutput_data = Slice (padded_input, start_dims, end_dims)CenterCropPadoutput_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.x_shape2 = Gather (x_shape_alldims2, axes_input)x_shape2 = Shape (padded_input)padded_input = Pad (input_data, pads)x_shape_alldims2 = Shape (padded_input)end_dims = Add (start_dims, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)sh_diff = Sub (x_shape2, shape)start_dims = Div (sh_diff, k2)padded_sh = Max(x_shape, shape)pad_amount = Sub(padded_sh, x_shape)x_shape = Gather (x_shape_alldims, axes_input)x_shape = Shape (input_data)pads = Concat (pad_amount_left, pad_amount_right)padded_input = Pad (input_data, pads, , axes_input)pad_amount_left = Div(pad_amount, k2)pad_amount_right = Sub(pad_amount, pad_amount_left)`shape` only supports `int32_t` or `int64_t` inputs) does not match the number of axes (CenterCropPad op must have 2 inputs.axes_input = Constant ()x_shape_alldims = Shape (input_data)Number of elements of input 'shape' (The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` Supported modes: `constant`(default), `reflect`, `edge`, `wrap`(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).inverse_indices(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).Constrain input and output types to all tensor types (including bfloat).'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.Axis Unexpected axis value: is referred to more than once.. Expected range [Only boolC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccv_initialcondGraph 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)) types up to IRv9.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.optional(tensor(float8e5m2fnuz))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e4m3fn))optional(tensor(bfloat16))optional(tensor(float8e4m3fn))seq(tensor(float8e5m2fnuz))optional(seq(tensor(bfloat16)))An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.final_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv9.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.initial_state_and_scan_inputsConstrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } 'y_scale' determines the output type.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.(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 'y_zero_point' and 'y' to 8-bit integer/float tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.ccThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float, float16, bfloat16 or int32 tensor.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.IndicesConstrain index tensor to int64The 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. Attribute pads has incorrect size.Attribute strides has incorrect size.MaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Constrain input and output types to float and 8 bit tensors.Attribute pads has incorrect sizeSecond input tensor has wrong dimensionC:\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.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.Input tensor must have at least 2 dimensionsauto_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.Attribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeThis 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. running_meaninput_varConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.running_varIf set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.This number of op outputs should be 1 when Training_mode = False, but it is not.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).input_meanStride along each spatial axis. Default is 1 along each axis.DeformConvoffset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis. 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. This number of op outputs should be 3 when Training_mode = True, but it is not.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.Number of groups of offset. C must be divisible by offset_group. Default is 1.Dilation value along each spatial axis of the kernel. Default is 1 along each axis.Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.The pads attribute cannot be used simultaneously with auto_pad attributedilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.The shape of the convolution kernel. If not present, should be inferred from input 'w'.p value of the Lp norm used to pool over the input data.Attribute pooled_shape must be specifiedROI pool output shape (height, width).RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pads must have an even sizeinput must have rank 3. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } 1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.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.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.List of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Input shape must have either [C] or [1,C] dimensions where C > 0list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)Input is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=11-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Scaling parameter.The exponent.The number of channels to sum overngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.) for attribute 'axis'Invalid value( { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.The axis on which to apply normalization, -1 mean last axis.GroupNormalizationBiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)ScaleShape = Constant ()ScaleT = Cast (scale)Biased = Add (Scaled, BiasReshaped)The number of groups of channels. It should be a divisor of the number of channels `C`.BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Axes2Mean = ReduceMean (X3D, Axes2)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)NumGroupsGroupSize = Div (C, NumGroups)num_groupsC = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (X, NewShape)N = Shape (X)InstanceShape = Shape (X)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)Constrain input types and output Y type to float tensors.%s:%u: %s: Assertion `%s` failed.FloatEpsilonsinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalizationMean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)Epsilon = Cast (FloatEpsilon)Axes_1 = Constant()image_shapeConstrain input and output types to all numeric tensor types.block_shapeOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.value_stringvalue_stringsAttribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.sparse_valuevalue_floatvalue_floatsvalue_intvalue_intsAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorX_random = RandomUniformLike (input)X_greater = Greater (X_random, input) { 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) }> } 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.All inputs to 'Range' op must be of the same typeConstrain output types to integral tensors.Constrain input types to common numeric type tensors.Output type must be int32 or int64Input tensor must have rank 2Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The mean of the normal distribution.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Lower boundary of the output values.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.The shape of the output tensor.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Input tensor must be 2-dimensionalConstrain input types.Constrain output types to be numerics.(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'.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.ccThe 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.(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 float32The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)Constrain output types to all numeric tensors and bool tensors.BernoulliC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccConstrain input to boolean tensor.Constrain output to boolean tensor.Constrain input/output to integer tensors. { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input to integer tensors.Constrain input and output types to integer tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input types to all numeric tensors.Constrain input/output to boolean tensors.Constrain input types to all (non-complex) tensors.Whether 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_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension. { 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) } sum { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.cc { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } Type of the element in the optional outputOptional is expected to have either an input or the type attribute set.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\defs.ccConstrain output type to all optional tensor or optional sequence types.Constrain input type to all tensor and sequence types.Input type is null. Type information is expected for the input.Attribute 'type' should be a TypeProto and it should specify a type.Optional is expected to have an output.Constrain output type to all tensor or sequence types.Optional-type input must contain an element with type information.Input type is null. Input must have Type information.OptionalHasElement is expected to have 1 output.OptionalHasElement is expected to have 0 or 1 input.OptionalGetElement must have an input element.Constrain output to a boolean tensor.Constrain input type to optional tensor and optional sequence types. 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. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) { 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) } Mish { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Constrain input X and output types to float tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } Constrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.Invalid rank for broadcasting: (Wrong op_type name for running propagation: Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.ccDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).ValuesWhether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.K input must be of type int64.K input must be a one-dimensional tensor of size 1.KAxis has less than the requested k elements. { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } 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_ReduceMax = ReduceMax (input) 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 (output_large_than_max, max, tmp)output_large_than_max = Less (max, tmp)tmp = Where (input_less_than_min, min, input)output = Where (input_large_than_max, max, input)Constrain input and output types to numeric tensors.data_0input_large_than_max = Less (max, input)output = Where (input_less_than_min, min, input)input_less_than_min = Less (input, min)output = Identity (input)Value of beta.Value of alpha.List of tensors for { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } { 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) } Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } slopeignore_indexexpanded_target = Unsqueeze (target, axes)const_oneconst_zeroloss = ReduceMean (loss_Ndd)loss_Ndd = Squeeze (loss_N1dd, axes)loss = Squeeze (loss_N1dd, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) 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.If set to 1 will perform the sums in reverse direction.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 output Y data type as 32-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.input and zero_point pair is expected to have same type.Constrain input and output types to all tensors.labelsSoftmaxCrossEntropyLosslog_prob 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) Shape3DType 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.output = NegativeLogLikelihoodLoss (X_Log, labels)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)log_prob = Identity (X_Log)Rank of input does not match the equation indices.Ellipsis represents incompatible dimensions.Constrain input and output types to all numerical tensor types.InputsEinsum expression string.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.lossNumber of input tensors does not match the operands in the equation.NegativeLogLikelihoodLossConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Weight rank must be 1.Input and target dimension value mismatch.Target rank must be 1 less than the input rank. loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) targetweight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)const_one_casted = Cast (const_one_float)const_one_floatweight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp = Gather (weight, transform_targets)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)const_zero_casted = Cast (const_zero_float)const_zero_floatinput_gather_element = GatherElements (input, transform_targets)squeeze_mask = Squeeze (mask, axes)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)loss_NCdd = Neg (input_gather_element_transform)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss = Mul (loss_unweighted, weight_gather)loss_unweighted = Squeeze (loss_N1dd, axes)weight_gather = Gather (weight, target)loss = ReduceSum (loss_Ndd) 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_ignore_index loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss_Ndd = Mul (loss_unweighted, weight_gather)Constrain signal and output to float tensors.frame_stepsignalframe_lengthConstrain to any numerical types.Constrain to float tensorsConstrain to integer 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.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.frame_length input must be scalar.sample_rateupper_edge_hertzlower_edge_hertznum_mel_bins input must be scalar. { 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) } Blackmannum_mel_binsThe 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. dft_length input must be scalar.Constrain the input size to int64_t.HannA {name} window with length: size. The output has the shape: [size]. { 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) } 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) } Constrain output types to numeric tensors.Constrain scalar length types to int64_t.dft_lengthIf 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. 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.size input must be a scalar.dft_length input must be a scalar.axis attribute value is invalid for a tensor of rank is_onesided and inverse attributes cannot be enabled at the same timeWhether 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.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.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.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".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".'axes' attribute must not contain any duplicates 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)` .values in 'axes' are beyond the bounds of the computed output shapeType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.length of each output. Values should be >= 0.The input is not evenly splittable'step' cannot be 0Input axes has invalid dataC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccInvalid Target shape product of 0Invalid position of 0Target shape may not have multiple -1 dimensionsThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Ending indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Attribute axes has incorrect lengthWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Attribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'Ranks inferred (Incorrect or missing attribute value for starts and endsTwo interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.width_scale) is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.tilesThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.One float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgepaddingsList 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`.consumed_inputslegacy optimization attribute.New shaperank must be greater than axisWhich axis to concat on. Default value is 1.length of each outputWhich axis to split onoutputs...Constrain input and output to only numeric types.constant_valueConstrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported. 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).Supported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` 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`.Attribute pads has incorrect lengthAttribute value for pads is requiredConstrain input and output types to all tensor and sequence types.One float, indicates the value to be filled.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.'axis' must be in [-rank(indices)-1, rank(indices)]Which axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Input type for input at index 0 is null. Type info is expected.Constrain output to integral tensor. It must be a scalar(tensor of empty shape).input_sequenceConstrain position to integral tensor. It must be a scalar(tensor of empty shape).positionConstrain input types to any tensor type.output_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.ccElement type of inputs are expected to be the same.Input type for input at index is null. Type info is expected.SequenceConstruct is expected to have at least 1 input.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.out_sequenceadditional_inputsSequenceMapConstrain to any tensor or sequence type.Constrain input types to any sequence type.SequenceMap__in_cond_out_cond_inThe 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._seqempty_cond_seqlenInput 0 expected but not providedExpected 1 or more outputs.Expected 1 or more inputs.Invalid ``body`` argument. Expected a graph_itercountSequenceMap_loop_body expected but not providedExpected a sequence type for input 0 expected to have type infoInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Graph attribute inferencing returned type information for outputs. Expected Graph attribute inferencer for "body" not available expected to be a sequence typeKeep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output types to all tensor types.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1 sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain split size to integral tensor.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= else=If node has but subgraphs produce then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Mismatched type for output then=Loop 'body' subgraph outputs should all be tensors but output was All Tensor typesAn optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.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.) is not equal to number of scan outputs (Number of scan input axes specified () is not equal to number of scan inputs ( axis value Int64 tensorNumber of scan output axes specified (All Tensor types up to IRv4.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\old.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.Scan 'body' subgraph outputs should all be tensors but output was notScan input was not a tensor.Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output All Tensor and Sequence typesmax_output_boxes_per_classInteger 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_indicesC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\defs.ccAllowed 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).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. The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.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.default 1; Pooled output Y's width.default 1; Pooled output Y's height.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccOptionalHasElement is expected to have 1 input.Input must be an optional-type value containing an element with type information.varsaved_meanFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The epsilon value to use to avoid division by zero, default is 1e-5f.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Dilation value along each spatial axis of filter.The storage order of the tensor. 0 is row major, and 1 is column major.Stride along each axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.VarianceE_XsquaredX_squaredEX_squaredStride along each spatial axis.Processed_STDX_varianceSTDC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccX_RMExponentIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropoutConstrain output mask types to boolean tensors.The ratio of random dropout 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_varConstrain mean and variance types to float tensors. It allows all float type for U. 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. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.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].One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.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.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.ccThe shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].First input tensor must have rank 3Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x' to float or int32 tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.ccC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\old.ccConstrain input and output types to high-precision and 8 bit numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). 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.The axis in which to compute the arg indices.Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01. 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. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeX_LogX_shapeIf set, defines the broadcast dimensions. See doc for details.broadcastPass 1 to enable broadcastingweight_gather_temp_1weight_gather_tempconst_one_castedX_LogSM_NCDX_LogSMX_NDCX_NCDexpanded_target_int64const_zero_target_typedweight_gather_sumloss_sumsqueeze_maskinput_gather_element_transformconst_zero_castedtransform_targetsloss_NCddinput_gather_elementexpanded_targetloss_unweightedweight_gatherloss_Nddloss_N1dd'shape' input must be 1D tensorDescribes 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).C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\old.ccNumber of top elements to retrieveInvalid value for attribute kDimension on which to do the sort.Maximum value, above which element is replaced by maxScalar multiplier for input tensor C, the default value is 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedMinimum value, under which element is replaced by minValue of alpha default to 0.2Value of beta default to 0.5Constrain input to float tensors.If set, defines the broadcast dimensions.Constrain input to integral tensors.Enable broadcastingC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\old.ccOne of the attributes 'value' or 'sparse_value' must be specified for a Constant node.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.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.cc Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\old.ccfowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.Mismatched type:Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type. expected to have type but instead is nullMismatch between number of source and target dimensions. Source= target= source=Element type of optional input expected to have map typeKey type of map input Value type of map input Element type of input expected to have sequence typeElement type of sequence input expected to have optional typeInput type was null expected to have tensor or sparse tensor type. Got: unknownInput was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensorElement type of optional input was unknownInput was expected to have map type. Got Key type of map input was unknownValue type of map input was unknownOutput was expected to have tensor type. Got Input was expected to have sequence type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Input element type of target map type missing key type.Mismatched map tensor key type:source map type missing value type.target map type missing value type.target sequence type missing element type.source optional type missing element type.target optional type missing element type.source map type missing key type.Data size mismatch. Tensor: load external data into raw data for tensor: Cannot parse data from external tensors. Please does not match the actual size expected size The type of tensor: Actual:. Expected:ParseData type mismatch for tensor: is undefined so it cannot be parsed. ==> Context: domaingraphelem_typekey_typevalue_type): ' of 'nameField '' is required but missing.' is required to be non-empty.value_infoutf8str_to_wstring: string is too long for converting to wstring.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values (] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor ( values, but NNZ is ) has ' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: ) should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: ' instead of ') is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!TensorProto (tensor name: , but the ') should be file inside the , but it doesn't exist or is not accessible.) should be stored in Data of TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: Location of external TensorProto ( tensor name: ' points outside the directoryraw_datadouble_datauint64_data) is stored externally and should not have data field.float_dataint32_datastring_dataint64_dataUnrecognized type value case (value_info name: data_type) to UNDEFINED is not allowedsetting data_type field (tensor name: ConstantFill' of node: Nodes in a graph must be topologically sorted, however input '' has been used as output names multiple times.Sparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers is not output of any previous nodes.name: initTensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph input OpType: Bad node spec for node. Name: ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however ' with domain_version of No Op registered for is deprecated in domain_version of Op registered for ) has zero input and zero output., type: NodeProto (name: No opset import for domain 'Attribute (name: ) should refer to attribute in parent node.Warning: Model contains experimental ops:op_typeinvalid stol argumentstol argument out of rangeInteger value expected, but not found.Unexpected literal type.String value expected, but not found.Identifier expected but not found. column: (line: Expected character producer_versionmodel_versiondoc_stringmetadata_propstype_protosir_versionopset_importproducer_namestringstensorsgraphssparse_tensorsinttype_protofloatsError context: [ParseError at position Unhandled type: %dUnexpected attribute type.Unexpected type.Error parsing TensorProto (expected a tensor type).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Incomplete string literal.Value expected but not found.Expecting primitive type as map key type.Unexpected type in sparse-tensor element type. already exists.Data for input is out of bounds.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph., node name: (op_type: were provided.Graph initializer names must appear after the actual inputs: in initializers. Cannot find missing input: inputs but Graph has Cannot use the same name as both a subgraph initializer and subgraph input: The number of graph input cannot be smaller than the number of node inputShape inference error(s): were provided optype . No opset import for domainCannot infer type and shape for node name Container for generated shape data cannot be nullptr when enable_data_propagation option is set.type case mismatch. existing=key type mismatch from MapProto. existing=type case unsupported. existing=type case unsupported for symbolic shape inference. inferred=opaque_typesparse_tensor_typeNOT_SET inferred=optional_type: (Inferred shape and existing shape differ in dimension unk__Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Pads has incorrect number of values. Expected 2 * Input 'scales' must have float element type.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64 values. values. Got Number of elements of input 'sizes' () must be same as rank of input 'X' (Number of elements of input 'scales' (Number of elements of input 'scales' must be same as rank of input 'X'Dimension value inferred (Either `sizes` or `scales` must be provided, but not both of themUnknown value for `keep_aspect_ratio_policy`: Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".) is not equal to the existing dim value (Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output 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.onnx.AttributeProtoonnx.FunctionProtoonnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.Optionalonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.ModelProto   FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDonnx.SequenceProtoonnx.MapProtoonnx.OptionalProtoSearchNFA inconsistencySearchBitState inconsistencyDFA out of memory: pattern length SearchOnePass inconsistencySearchDFA inconsistencybytemap range Unexpected re_anchor value: program size list count endpos: text size: RE2: invalid startpos, endpos pair. [startpos: Error reverse compiling 'pattern too large - compile failedInvalid RE2: ': C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.ccError compiling 'Error parsing 'Unknown encoding Walk NULLC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hStack not empty.invalid UTF-8invalid perl operatorNumCapturesWalker::ShortVisit calledinvalid named capture groupno argument for repetition operatortrailing \bad repetition operatorinvalid repetition sizemissing ]invalid character class rangeunexpected )missing )unexpected errorno errorinvalid character classinvalid escape sequenceC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccRegexp not destroyed.Unexpected op in Regexp::Equal: Bad reference count 9p99909 9`9P98898X8H88No ranges in char classCompiler::Copy called!Missing case in Compiler: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccshould never happenBad call to ParseState::ParsePerlFlagsPcMnNlNdMcLtLlLoLmBad hex digit LuAnyunknown round: RE2: unexpected op: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccAddFoldedRange recurses too much.Concat of RepetitionWalker::ShortVisit calledUnexpected opcode in short circuit: Bad args: nsubmatch=context does not contain textUnhandled in step in AddToThreadqC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.ccunhandled Unexpected special state in RunStateOnByteNULL state in RunStateOnByteFailed to analyze start state.StateSaver failed to restore state.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.ccunhandled opcode: DeadState in RunStateOnByte|RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheUnexpected opcode: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccjob_.size() = GrowStack() failed: njob_ = Cannot use SearchOnePass for unanchored matches.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.ccUnexpected opcode in IsMatch: Malformed repeat DoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is Simplify case not handled: SimplifyWalker::ShortVisit calledCase not handled in ComputeSimple: CoalesceWalker::ShortVisit calledC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccIIIII8JI8JI JI JIJIJxI HhI HIIII8IHJ(IHJXIJHIJHIHIIDJIDJHIHIH0JH0JxHIhHIHJHJ8HH(HHXH`JHH`JHPJHPJ[:^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!/:@[`{~ 09AZaz09AZ__az ~ !~ 09AFaf09AZ__azlqЦPq8qXÃdqЀ0#XqÃqh烀qу,qЃ qppHp䃀ppx烀p4܃p(pD\@z pHtp`烀pp \p 䃀Tp8ălp ǃ`p(܃8p0p@Lp[SDpÃpp烀(pÃp̃oȘooo僀oõo`o僀pohǃ`o  跃o xo МPtXHt Ѓdt!؊Xtă(tt𠃀@t8 `ƒ 8tȗs sȵt8t` sPXs0s̓ sxs@hshsPăs 0să sЃXs`ă@ssr`Ãssz|@܃ rp r"@wrڃrPr rۃrXƒ; уipw(;̃9  ;ǃ";` ; ])rrHÃP@nrr`wr`r䃀PrPxr hrЃ;`ŃoP僀B,r0ǃ r貃@rH0r̃qвqr;烀 | rqу8qq覃qXqڃCCqȳqƒ;`%0qxqȊtq;p타qăІ*qƒw ÃvÃ0w wpv8vv𲃀vvhpvpǃvxv0ăLvpDv(hv\е8Xvƒ0vHă v;@vxÃ0?011111122*2G2P2P2`222223MMƤ(+6799wy@O0484sz AZazptvwz}/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!:AZfDPY   ?AJVoqPpP=@ptv`l@ U X _ A00000J'pz|  @n_n4Mh  89;>@DFFJPl!<Up4Vn!ooWW  ? ? 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