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}oHHK8Hu HH@HL$ +HL$ uHHK8Hu HH@HL$ +HL$ lmtHHK8HuJ HH@THL$ q+HL$ ZwHHK8Hu HH@HL$ <+HL$ RvHHK8HuHH@HL$ +HL$ \wHHK8HuHH@HL$ *HL$ wHHK8HuvHH@HL$ *HL$ S^wHHK8HuAHH@KHL$ h*HL$ ޠwHHK8Hu HH@HL$ 3*HL$ uHHK8HuHH@HL$ )HL$ DsHHK8HuHH@HL$ )HL$ ?vHHK8HumHH@wHL$ )HL$ zwHHK8Hu8HH@BHL$ _)HL$ %xoHHK8HuHH@ HL$ *)HL$  uHHK8HuHH@HL$ (HL$ |oHHK8HuHH@HL$ (HL$ frHHK8HudHH@nHL$ (HL$ qHHK8Hu/HH@9HL$ V(HL$ 謺sHHK8HuHH@HL$ !(HL$ 1vHHK8HuHH@ HL$ 'HK8HtHH;H@  HC8H$HĀ]HL$SHHHL$ UuHHK8HuDHH@N HL$ k'HL$ xvHHK8HuHH@ HL$ 6'HL$ HL$ qHHK8HuHH@HL$ HL$ rHHK8HuHH@HL$ HL$ qHHK8HuxHH@HL$ HL$ EHnHHK8HuCHH@MHL$ jHL$ qHHK8HuHH@HL$ 5HL$ kqHHK8HuHH@HL$ HL$ qHHK8HuHH@HL$ HL$ sHHK8HuoHH@yHL$ HL$ \tHHK8Hu:HH@DHL$ aHL$ tHHK8HuHH@HL$ ,HL$ tHHK8HuHH@HL$ HL$ ]tHHK8HuHH@HL$ HL$ 訕tHHK8HufHH@pHL$ HL$ tHHK8Hu1HH@;HL$ XHL$ tHHK8HuHH@HL$ #HL$ tHHK8HuHH@HL$ HL$ tHHK8HuHH@HL$ HL$ tHHK8Hu]HH@gHL$ HL$ jtHHK8Hu(HH@2HL$ OHL$ 赏tHHK8HuHH@HL$ HL$ >nHHK8HuHH@HL$ HL$ 苗qHHK8HuHH@HL$ HL$ tHHK8HuTHH@^HL$ {HL$ AtHHK8HuHH@)HL$ FHL$ tHHK8HuHH@HL$ HL$ wysHHK8HuHH@HL$ HL$ "qHHK8HuHH@HL$ HL$ tHHK8HuKHH@UHL$ rHL$ tHHK8HuHH@ HL$ =HL$ 3'pHHK8HuHH@HL$ HL$ ntHHK8HuHH@HL$ HL$ 5pHHK8HuwHH@HL$ HL$ [pHHK8HuBHH@LHL$ iHL$ vHHK8Hu HH@HL$ 4HL$ vHHK8HuHH@HL$ HL$ %vHHK8HuHH@HL$ HL$ vHHK8HunHH@xHL$ HL$ 軜vHHK8Hu9HH@CHL$ `HL$ 膟vHHK8HuHH@HL$ +HL$ vHHK8HuHH@HL$ HL$ ܮvHHK8HuHH@HL$ HL$ *rHHK8HueHH@oHL$ HL$ 20uHHK8Hu0HH@:HL$ WHL$ -\sHHK8HuHH@HL$ "HL$ 8wHHK8HuHH@HL$ HK8HtHH;H@ HC8H$HĀ]H\$HL$UH$HHHL$ 蹖tHHK8Hu7HH@AHL$ ^ HL$ tHHK8HuHH@ HL$ ) HL$ +uHHK8HuHH@HL$ HL$ 蚬oHHK8HuHH@HL$ HL$ pHHK8HucHH@mHL$ HL$ ptHHK8Hu.HH@8HL$ U HL$ 軅tHHK8HuHH@HL$ HL$ 6#rHHK8HuHH@HL$ HL$ uHHK8HuHH@HL$ HL$ sHHK8HuZHH@dHL$ HL$ FqHHK8Hu%HH@/HL$ L HL$ boHHK8HuHH@HL$  HL$ !uHHK8HuHH@HL$ HL$ #uHHK8HuHH@HL$ HL$ @wHHK8HuQHH@[HL$ x HL$ BwHHK8HuHH@&HL$ C HL$ IuHHK8HuHH@HL$  HL$ uHHK8HuHH@HL$ HK8HtHH;H@ HC8H$HĀ]H\$HL$UH$HHHL$ @qHHK8Hu'HH@1HL$ N HL$ 贝sHHK8HuHH@HL$  HL$ sHHK8HuHH@HL$ HL$ DwHHK8HuHH@HL$ HL$ tHHK8HuSHH@]HL$ zHL$ 0tHHK8HuHH@(HL$ EHL$ {tHHK8HuHH@HL$ HL$ tHHK8HuHH@HL$ HL$ 1oHHK8HuHH@HL$ HL$ 8oHHK8HuJHH@THL$ qHL$ ?oHHK8HuHH@HL$ <HL$ nHHK8HuHH@HL$ HL$ nHHK8HuHH@HL$ HL$ nHHK8HuvHH@HL$ HL$ CnHHK8HuAHH@KHL$ hHL$ NnHHK8Hu HH@HL$ 3HL$ YnHHK8HuHH@HL$ HL$ dnHHK8HuHH@HL$ HL$ _uHHK8HumHH@wHL$ HL$ joHHK8Hu8HH@BHL$ _HL$ u oHHK8HuHH@ HL$ *HL$ oHHK8HuHH@HL$ HL$ [uHHK8HuHH@HL$ HL$ &oHHK8HudHH@nHL$ HL$ oHHK8Hu/HH@9HL$ VHL$ !oHHK8HuHH@HL$ !HL$ $oHHK8HuHH@HL$ HL$ %oHHK8HuHH@HL$ HL$ m(oHHK8Hu[HH@eHL$ HL$ AoHHK8Hu&HH@0HL$ MHL$ HoHHK8HuHH@HL$ HL$ ^oHHK8HuHH@HL$ HL$ iloHHK8HuHH@HL$ HL$ noHHK8HuRHH@\HL$ yHL$ oHHK8HuHH@'HL$ DHL$ 蚎oHHK8Hu߃HH@HL$ HL$ %hpHHK8Hu߃HH@HL$ HL$ @sHHK8Hu~߃HH@HL$ HL$ tpHHK8HuI߃HH@SHL$ pHL$ ~pHHK8Hu߃HH@HL$ ;HL$ 聴tHHK8HuރHH@HL$ HL$ tHHK8HuރHH@HL$ HL$ qHHK8HuuރHH@HL$ HL$ "tHHK8Hu@ރHH@JHL$ gHL$ 荸tHHK8Hu ރHH@HL$ 2HL$ tHHK8Hu݃HH@HL$ HL$ ctHHK8Hu݃HH@HL$ HL$ μtHHK8Hul݃HH@vHL$ HL$ 9tHHK8Hu7݃HH@AHL$ ^HL$ 褿tHHK8Hu݃HH@ HL$ )HL$ )qHHK8Hu܃HH@HL$ HL$ *qHHK8Hu܃HH@HL$ HL$ UqHHK8Huc܃HH@mHL$ HL$ 0lqHHK8Hu.܃HH@8HL$ UHL$ xqHHK8HuۃHH@HL$ HL$ rHHK8HuۃHH@HL$ HL$ mHHK8HuۃHH@HL$ HL$ 謎qHHK8HuZۃHH@dHL$ HL$ mHHK8Hu%ۃHH@/HL$ LHL$ qHHK8HuڃHH@HL$ HL$ 轷qHHK8HuڃHH@HL$ HL$ qHHK8HuڃHH@HL$ HL$ mHHK8HuQڃHH@[HL$ xHL$ mHHK8HuڃHH@&HL$ CHL$ qHHK8HuكHH@HL$ HL$ qHHK8HuكHH@HL$ HL$ mHHK8Hu}كHH@HL$ HL$ nHHK8HuHكHH@RHL$ oHL$ enHHK8HuكHH@HL$ :HL$ 0qHHK8Hu؃HH@HL$ HL$ rHHK8Hu؃HH@HL$ HL$ F rHHK8Hut؃HH@~HL$ HL$ q@nHHK8Hu?؃HH@IHL$ fHL$ LNnHHK8Hu ؃HH@HL$ 1HL$ כrHHK8Hu׃HH@߃HL$ HL$ ysHHK8Hu׃HH@߃HL$ HL$ |sHHK8Huk׃HH@u߃HL$ HL$ h]sHHK8Hu6׃HH@@߃HL$ ]HL$ OsHHK8Hu׃HH@ ߃HL$ (HK8HtHH;H@ ރHC8H$HĀ]H\$HL$UH$HHHL$ ooHHK8HuwփHH@ރHL$ HL$ DnHHK8HuBփHH@LރHL$ iHL$ ߣrHHK8Hu փHH@ރHL$ 4HL$ J wHHK8HuՃHH@݃HL$ HL$ uwHHK8HuՃHH@݃HL$ HL$ `wHHK8HunՃHH@x݃HL$ HL$ TnHHK8Hu9ՃHH@C݃HL$ `HL$ 6,tHHK8HuՃHH@݃HL$ +HL$ a oHHK8HuԃHH@܃HL$ HL$ ,nHHK8HuԃHH@܃HL$ HL$ nHHK8HueԃHH@o܃HL$ HL$ nHHK8Hu0ԃHH@:܃HL$ WHL$ nHHK8HuӃHH@܃HL$ "HL$ HrHHK8HuӃHH@ۃHL$ HK8HtHH;H@ ۃHC8H$HĀ]HL$SHHHL$ vLpHHK8HuDӃHH@NۃHL$ kHL$ apHHK8HuӃHH@ۃHL$ 6HL$ nHHK8Hu҃HH@ڃHL$ HL$ 跽tHHK8Hu҃HH@ڃHL$ HL$ vHHK8Hup҃HH@zڃHL$ HL$ vHHK8Hu;҃HH@EڃHL$ bHL$ 8SqHHK8Hu҃HH@ڃHL$ -HL$ årHHK8HuуHH@كHL$ HK8HtHH;H@ كHC8HĀ[H\$HL$UH$HHHL$ vHHK8HuGуHH@QكHL$ nHL$ 贪qHHK8HuуHH@كHL$ 9HL$ 蟳qHHK8HuЃHH@؃HL$ HL$ rHHK8HuЃHH@؃HL$ HL$ urHHK8HusЃHH@}؃HL$ HL$ ЙrHHK8Hu>ЃHH@H؃HL$ eHL$ 2nHHK8Hu ЃHH@؃HL$ 0HL$ XqHHK8HuσHH@׃HL$ HL$ rHHK8HuσHH@׃HL$ HL$ lsHHK8HujσHH@t׃HL$ HL$ sHHK8Hu5σHH@?׃HL$ \HL$ nHHK8HuσHH@ ׃HL$ 'HL$ nHHK8Hu΃HH@փHL$ HK8HtHH;H@ փHC8H$HĀ]HL$SHHHL$ 6pHHK8HuD΃HH@NփHL$ kHL$ vHHK8Hu΃HH@փHL$ 6HL$ HvHHK8Hu̓HH@ՃHL$ HL$ 'oHHK8Hu̓HH@ՃHL$ HL$ 袮oHHK8Hup̓HH@zՃHL$ HL$ oHHK8Hu;̓HH@EՃHL$ bHL$ 蘳oHHK8Hu̓HH@ՃHL$ -HL$ coHHK8HũHH@ԃHL$ HK8HtHH;H@ ԃHC8HĀ[H\$HL$UH$HHHL$ 蹓rHHK8HuG̃HH@QԃHL$ nHL$ DNnHHK8HũHH@ԃHL$ 9HL$ _qHHK8Hu˃HH@ӃHL$ HL$ nHHK8Hu˃HH@ӃHL$ HL$ tHHK8Hus˃HH@}ӃHL$ HL$ 0mHHK8Hu>˃HH@HӃHL$ eHL$ tHHK8Hu ˃HH@ӃHL$ 0HL$ 6xpHHK8HuʃHH@҃HL$ HL$ !mHHK8HuʃHH@҃HL$ HL$ LmHHK8HujʃHH@t҃HL$ HL$ WqHHK8Hu5ʃHH@?҃HL$ \HL$  qHHK8HuʃHH@ ҃HL$ 'HL$ qHHK8HuɃHH@уHL$ HL$  qHHK8HuɃHH@уHL$ HL$ qHHK8HuaɃHH@kуHL$ HL$ ntHHK8Hu,ɃHH@6уHL$ SHL$ ٮtHHK8HuȃHH@уHL$ HL$ qHHK8HuȃHH@ЃHL$ HL$ qHHK8HuȃHH@ЃHL$ HL$ JsHHK8HuXȃHH@bЃHL$ HL$ %sHHK8Hu#ȃHH@-ЃHL$ JHL$ sHHK8HuǃHH@σHL$ HL$ sHHK8HuǃHH@σHL$ HL$ @tHHK8HuǃHH@σHL$ HL$ pHHK8HuOǃHH@YσHL$ vHK8HtHH;H@ 1σHC8H$HĀ]H\$HL$UH$HHHL$ 詂sHHK8HuƃHH@΃HL$ HL$ $oHHK8HuƃHH@΃HL$ HL$ :qHHK8Hu]ƃHH@g΃HL$ HL$ BqHHK8Hu(ƃHH@2΃HL$ OHL$ qHHK8HuŃHH@̓HL$ HL$ 4pHHK8HuŃHH@̓HL$ HL$ dsHHK8HuŃHH@̓HL$ HL$ fqHHK8HuTŃHH@^̓HL$ {HL$ 1rHHK8HuŃHH@)̓HL$ FHL$ HH@HHL$ eHL$ ;_HHK8Hu HH@HL$ 0HL$ 6_HHK8HuԵHH@޽HL$ HL$ `HHK8HuHH@HL$ HL$ _HHK8HujHH@tHL$ HL$ _HHK8Hu5HH@?HL$ \HL$ _HHK8HuHH@ HL$ 'HL$ ]"`HHK8Hu˴HH@ռHL$ HL$ _HHK8HuHH@HL$ HL$ `HHK8HuaHH@kHL$ HL$ N`HHK8Hu,HH@6HL$ SHL$ `HHK8HuHH@HL$ HL$ d_HHK8Hu³HH@̻HL$ HL$ _HHK8HuHH@HL$ HL$ `HHK8HuXHH@bHL$ HL$ `HHK8Hu#HH@-HL$ JHL$ 0_HHK8HuHH@HL$ HL$ _HHK8HuHH@úHL$ HL$ _HHK8HuHH@HL$ HL$ A:`HHK8HuOHH@YHL$ vHL$ H`HHK8HuHH@$HL$ AHL$ D`HHK8Hu屃HH@﹃HL$ HL$ 6`HHK8HuHH@HL$ HL$ M3`HHK8Hu{HH@HL$ HL$ =`HHK8HuFHH@PHL$ mHL$ J`HHK8HuHH@HL$ 8HL$ S_HHK8HuܰHH@渃HL$ HL$ 4^HHK8HuHH@HL$ HL$ :^HHK8HurHH@|HL$ HL$ O^HHK8Hu=HH@GHL$ dHL$ ^HHK8HuHH@HL$ /HL$ ^HHK8HuӯHH@ݷHL$ HL$ `_HHK8HuHH@HL$ HL$ 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Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: D:\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 MMMMCan't happenD:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899falsetrue\\x%02xinfnanty10invalid string position...\\?\\/UNKNOWNINVALID_ARGUMENT:CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): , 'map/set too longunordered_map/set too longinvalid hash bucket count /\ was false.Stacktrace: Cpuai.onnx.mlai.onnx.preview.trainingai.onnx.trainingai.onnxVIWEFnarrowing_errorAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityD:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::IDataTransfer::CopyTensorAsync is not implementedD:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=D:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: OrtValue::GetIsTensor()OrtValue::GetMutableD:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hIsTensorSequence()Trying to get a TensorSeq, but got: IsSparseTensor()Trying to get a SparseTensor, but got: Integer overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowORT_LOAD_CONFIG_FROM_MODELTensor sequence must contain only primitive typesonnxruntime::TensorSeq::SetTypeelem_type_ != nullptri < tensors_.size()D:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.hTensorSeq: tensor to be added has a different data type.onnxruntime::TensorSeq::GetAtonnxruntime::TensorSeq::AddIsSameDataType(tensor.Get())Unknown Exception, got tried creating tensor with negative value in shapeNot able to find appropriate IDataTransfer to copy sparse datanot enough space: expected D:\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::UseBlockSparseIndicesRegisterCustomOpsLibrary: Failed to load libraryUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: 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 enabledstring buffer allocation failedlengths allocation failedinput array doesn't equal tensor sizeOutput buffer allocation failedOrtValue should contain a Tensor or a Sparse Tensorelement index is out of boundsThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse dataindex is out of boundsshape is invalidoutput buffer is too small. Use GetStringTensorDataLength.offsets buffer is not equal to tensor sizeout of indexbuffer size is too small for string elementindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.input array is too shortin[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.OrtCreateValueImplSeqHelperAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrSpecified provider is not supported.Invalid build with no providers availablelocation 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_chunkInvalid key found: max_power_of_two_extend_bytesThe requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s 1.19.2ORT Build Info: git-branch=HEAD, git-commit-id=ffceed9d44, build type=RelWithDebInfo, cmake cxx flags: /guard:cf /DWIN32 /D_WINDOWS /D_DISABLE_CONSTEXPR_MUTEX_CONSTRUCTOR /DWINAPI_FAMILY=100 /DWINVER=0x0A00 /D_WIN32_WINNT=0x0A00 /DNTDDI_VERSION=0x0A000000 /Qspectre /DONNXRUNTIME_ENABLE_INTEL_METEOR_LAKE_MOBILE_PLATFORM_PERF_PATCH /O2 /Ob1 /DNDEBUG /Zc:__cplusplus /EHsc /wd26812 -DEIGEN_HAS_C99_MATH -DCPUINFO_SUPPORTEDinvalid vector subscriptT utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. onnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataTensor must always contain primitive types. Found: Invalid index requested for map type.OrtCreateValueImplMapHelpervalue_type != nullptrValue type is not supported yet: Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::check++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkSequence is missing type entry for its elementonnxruntime::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 >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::check != len >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::DataTypeImpl::GetType() == type_OrtCreateMapMLValueonnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggerD:\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: DML execution provider is not supported in this build. OrtApis::SessionOptionsAppendExecutionProviderProvider options key/value cannot be emptyMaximum string length for a provider options key/value is 1024.OpenVINOSNPED:\a\_work\1\s\onnxruntime\core\session\provider_registration.ccQNNAZUREXNNPACKWEBNNUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'D:\a\_work\1\b\RelWithDebInfo\installed\include\google/protobuf/repeated_ptr_field.h in Output [TypeInferenceError] is null expected to have tensor or sparse tensor type: expected to have tensor or sparse type in expected to have tensor type in "). domain: "Trying to add a domain to LastReleaseVersion map, but the domain is already exist with last version: Trying to add a domain to DomainToVersion map, but the domain is already exist with version range (, domain: "CPUExecutionProvideronnxruntime::OpKernel::ComputeAsyncshape inference not yet supported for non-tensor typesD:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInvalid shape infovalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypeOrtShapeInferContext::OrtShapeInferContextNo requested allocator availableFailed to fetch a stream hosting the requested resourceOrtShapeInferContext::SetOutputTypeShapesymbolic and integer dims mismatch!Attribute does not exist.Invalid attribute.Invalid contextFailed to fetch type shape info for the index.Size of data not large enough to hold the array of floats.Size of data not large enough to hold an int64.Size of data not large enough to hold a float.Attribute has no float value.Size of data not large enough to hold the string.Size of data not large enough to hold the array of strings.Attribute has no int64 value.Size of data not large enough to hold the array of int64.::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input nameUnexpected attribute type. Result buffer is not large enough::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a type::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo cannot get a valid logger from its execution provider' in custom op 'Output buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution providerInputOutputUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKerneloutputOnly the last to a custom op may be marked variadic.inputinputsoutputsi == count - 1onnxruntime::CreateSchema::::operator ()Another version of operator 'static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o)). onnxruntime allows the overloading of an operator if all versions have the same number of declared 'has a different number of No kernels to registers.ops.size() > 0defined list of typesall typesThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.1 == undefinedonnxruntime::CreateSchemacustom op registered at runtime input to be of optional typestinput count does not matchonnxruntime::IsCompatiblecustom op schemas mismatch, expecting input to be of variadic typendth input to be of single typeoutput count does not match input to keep same homogeneity input to keep same arity output to keep same homogeneity output to keep same arity output to be of optional type output to be of variadic type[CustomOP type inferencing error]: kernel Input: onnxruntime::InferOutputTypes output to be of single type is absent, but not optional. Op : [CustomOP type inferencing error]: no kernel def matches node inputs for Op: [CustomOP type inferencing error]: no type constraint found for output: Op: [CustomOP type inferencing error]: no type constraint found for input: onnxruntime::CreateCustomRegistry`InlinedVector::at(size_type) const` failed bounds checkinvalid unordered_map key[ShapeInferenceError] Unsupported Source/Target type= Declared=Mismatch between inferred and declared type. Inferred=onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawTensor type mismatch.type == dtype_shape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape) != new size (Tensor size ( Max:Validating no unexpected access using an invalid node_index. Got:D:\a\_work\1\s\include\onnxruntime\core/graph/graph.hDEFAULTnode_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImpl is not present.Required output at index D:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.output_ptronnxruntime::OpKernelContext::RequiredOutputp_ml_valueonnxruntime::OpKernelContext::OutputIsSameDataType(tensor)ep.context_enablesession.disable_cpu_ep_fallbackep.context_file_pathep.context_embed_modeonnxruntime::LoadDynamicLibraryFromProviderD:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.ccuint8int32float32int8uint64int1uint32int64uint16int16bfloat16float16complex64complex128doublestringfloat8e5m2float8e5m2funzfloat8e4m3fnfloat8e4m3fnuzshapedata_typeuint4int4data_type_bodyCustom op is missing `data_type` attr.shape_Provider_SetHostonnxruntime::ProviderSharedLibrary::Unloadonnxruntime_providers_shared.dllonnxruntime::ProviderSharedLibrary::Ensureonnxruntime::ProviderLibrary::Unloadonnxruntime_providers_cuda.dllonnxruntime::ProviderLibrary::GetGetProvideronnxruntime_providers_rocm.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_cuda_ut.dllonnxruntime_providers_cann.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_migraphx.dllonnxruntime_providers_vitisai.dllonnxruntime_providers_openvino.dllnum_of_threadscache_dirdevice_typeenable_npu_fast_compiledisable_dynamic_shapesnum_streamscontextenable_opencl_throttlingenable_qdq_optimizerdisable_cpu_fallbackexport_ep_ctx_blobmodel_priorityso_epctx_embed_modeonnxruntime::TryGetProviderInfo_TensorRTso_export_ep_ctx_blobso_epctx_pathTensorRT Provider not available, can't get interface for itonnxruntime::GetProviderInfo_TensorRT is already in session option.The custom op domain name AddTensorRTCustomOpDomainToSessionOptionSessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.SessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryCANN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryCUDA execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryep_context_enableep_context_embed_modeoneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.ep_context_file_pathSessionOptionsAppendExecutionProvider_VitisAI: Failed to load shared libraryRequired input at index input_ptrMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::OpKernelContext::RequiredInputRelease_State_Create_State_Compute_' op_type:kernel already mapped to existing nodematching node is missingD:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'invalid arg_num.invalid node output countinvalid node input count: , expect: standalone_Attribute type not supported yet.onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsarg_num < static_cast(input_count_)onnxruntime::standalone::InvokeOpKernel definition was not found for node Domain:'onnxruntime::standalone::CreateOpEnv 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.Provided allocator is nullOrtMemoryInfo is nullenv_ptr == p_instance_.get()OrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cc it.second= index=Size mismatch:onnxruntime::IOBinding::BindInputSize mismatchmapped_output_names_.size() == output_names_.size()onnxruntime::IOBinding::BindOutputImplp_providermapped_feed_names_.size() == feed_names_.size()onnxruntime::SyncProvidersonnxruntime::IOBinding::SynchronizeInputsonnxruntime::IOBinding::SynchronizeOutputsCould not find OrtValue with name 'onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hCached EP instance for graph replay is not set yet before calling ReplayGraph()Provided type is not an optional sequence tensorIsOptionalSeqTensor(type)onnxruntime::utils::GetElementTypeFromOptionalSeqTensorProvided type is not an optional tensorIsOptionalTensor(type)onnxruntime::utils::GetElementTypeFromOptionalTensorD:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.hMemcpyTransformer/ORTM] [json.exception.session.disable_quant_qdqsession.use_env_allocatorssession.load_model_formatsession.save_model_formatsession.set_denormal_as_zerosession.intra_op.allow_spinningsession.use_ort_model_bytes_directlysession.use_ort_model_bytes_for_initializerssession.disable_aot_function_inliningoptimization.disable_specified_optimizerssession.inter_op.allow_spinningsession.force_spinning_stopsession.strict_shape_type_inferencesession.allow_released_opsets_onlyoptimization.minimal_build_optimizationssession.dynamic_block_basememory.enable_memory_arena_shrinkagesession.intra_op_thread_affinitiessession.debug_layout_transformationsession.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytesgpu_graph_id%Y-%m-%d_%H-%M-%SMemcpyFromHostdisable_synchronize_execution_providersapplyInvalid value for The environment variable contained the value: are '0' and '1'. MemcpyToHostShapesave value of 'save' is only valid when saving an ORT format model.ModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)The only supported values for the environment variable Reading the provided model for the ORT configonnxruntime::FinalizeSessionOptionsD:\a\_work\1\s\onnxruntime\core\session\inference_session.ccRemoving ETW Sink from loggerDone Removing ETW Sink from loggeronoffonnxruntime::GetSeverityAdding ETW Sink to logger with severity level: onnxruntime::InferenceSession::ConstructorCommon::::operator ()Done Adding ETW Sink to logger with severity level: onnxruntime::InferenceSession::ConstructorCommon;Creating and using per session threadpools since use_per_session_threads_ is true-Flush-to-zero and denormal-as-zero are onnxruntime::InferenceSession::ConstructorCommon::::operator ()Could not finalize session options while constructing the inference session. Error Message: status.IsOK()!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fn-inter-op-intra-opsession-Dynamic block base set to Affinity string must not be emptysession_env.EnvCreatedWithGlobalThreadPools()onnxruntime::InferenceSession::TraceSessionOptionsGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessioncustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.onnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec providerCould not parse model successfully while constructing the inference sessionresultError during EndProfiling(): Having memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. Can't register custom op domains with ORT for Execution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.So disabling it for this session since it uses the DML Execution Provider.onnxruntime::InferenceSession::SaveToOrtFormatFailed to save ORT format model to file: Exception during loading: onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registry failed:Load model from ORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().This session already contains a loaded model.onnxruntime::InferenceSession::LoadWithLoaderEncountered unknown exception in LoadWithLoader()model_loading_uriModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoonnxruntime::InferenceSession::TransformGraph::::operator ().onnxFailed to load model because protobuf parsing failed.model_loading_arrayonnxruntime::LoadOrtModelBytes bytes were able to be read. failed. Only onnxruntime::InferenceSession::LoadOrtModel::::operator ()post_layout_transform_step_onnxruntime::InferenceSession::TransformGraph::::operator ()onnxruntime::InferenceSession::TransformGraphCastFloat16TransformerSerialized version info is null. Invalid ORT format model.) has saved runtime optimizations. They will be ignored.The old ORT format model (version ] is not supported in this build onnxruntime::InferenceSession::LoadOrtModelWithLoaderThis session has already been initialized.ORT model verification failed.InferenceSession is null. Invalid ORT format model.onnxruntime::`anonymous-namespace'::PartitionOrtFormatModelonnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsException during initialization: onnxruntime::InferenceSession::Initialize::::operator ()The ORT format model version [Missing Model. Invalid ORT format model.The provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceModel was not loaded.Session has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.onnxruntime::InferenceSession::Initialize::::operator ()Initializing session.onnxruntime::InferenceSession::InitializeModel was not loadedThis session cannot use the graph capture feature as requested by the user as all compute graph nodes have not been partitioned to the it is safe to use the graph capture feature.of the same shape as the representative input (common case), will match the shapes produced in the model for other inputs as the model has control flow nodes which can't be supported by This session cannot use the graph capture feature as requested by the user This session cannot use the graph capture feature as requested by the user as the model has control flow nodes which can't be supported by as all compute graph nodes have not been partitioned to the as all the graph nodes have not been assigned to This session cannot use the CUDA Graph feature as requested by the user This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA/HIP Graph feature as requested by the user.using the representative input used to capture the graph, As long as the intermediate shapes produced in the model Use the graph capture feature with caution. This model has shape massaging nodes that will execute on CPU. 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.1024Session successfully initialized.Encountered unknown exception in Initialize()Conflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerInvalid rank for for the following indices Got invalid dimensions for index: session_initialization Please fix either the inputs/outputs or the model. Expected: Got: Unexpected Valid values kInput, kOutputarg_type == ArgType::kInput || arg_type == ArgType::kOutputonnxruntime::InferenceSession::ValidateInputsOutputs))()) , expected: ( data type. Actual: ( elements, but names has name: Invalid feedfetch elements. has sparse_tensor but received a sparse tensor but received a tensor sequenceseq but received a tensor' expected to be of type: with name: 'tensor CUDA Graph for this model with tag: Replaying the captured onnxruntime::InferenceSession::RunSession was not initializedAt least one output should be requested.Invalid output name:Failed to parse the cuda graph annotation id: with graph annotation id: Start another run for necessary memory allocation or graph capture.input name cannot be emptyNULL input supplied for input output name cannot be emptySession not initialized.Running with tag: Encountered unknown exception in Run()model_runonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingfetch_unique_ptrs[i] != nullptrunknown exceptionintra op thread pool must have at least one thread for RunAsynconnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::SetTuningResults does not support TunableOp.). Invalid TuningResults (index=Profiler is disabled.onnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.Cannot find execution provider Unsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: ). Reason: Failed to load TuningResults (index=, enable TunableOp for usingCorrectly set TuningResults for error message: Unable to shrink arena: onnxruntime::InferenceSession::ShrinkMemoryArenasInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: combination in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination is not an arena based allocator: The registered allocator for device-id run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformersbad conversion961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5invalid string_view position.json_onnxruntime::ParseStringWithClassicLocaleD:\a\_work\1\s\include\onnxruntime\core/common/parse_string.hlist too longother_errorlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringFailed to parse value: "unknown errorbad castsystemgeneric*invalid stoi argumentstoi argument out of rangeDevice:[ OrtAllocatorType: OrtMemType: id:] DeviceId: MemoryType:DeviceType:name:OrtMemoryInfo:[TensorrtExecutionProviderCUDAExecutionProviderROCMExecutionProviderJsExecutionProviderDmlExecutionProviderMEMORY_EFFICIENT } config_options: { use_deterministic_compute:PRIORITY_BASED graph_optimization_level: max_num_graph_transformation_steps: session_log_verbosity_level: session_log_severity_level: thread_pool_allow_spinning: use_per_session_threads: inter_op_param: intra_op_param: enable_mem_pattern: optimized_model_filepath: enable_profiling: execution_order: session_logid: profile_file_prefix: enable_cpu_mem_arena: enable_mem_reuse:Provider onnxruntime::ExecutionProviders::AddD:\a\_work\1\s\onnxruntime\core/framework/execution_providers.hdelimiter must not be empty execution_mode:Session Options { has already been registered.!delimiter.empty()onnxruntime::utils::SplitStringD:\a\_work\1\s\onnxruntime\core/common/string_utils.hcom.ms.internal.nhwccom.microsoft.nchwccom.microsoftcom.microsoft.experimentalorg.pytorch.atentensor(uint32)unknowntensor(double)tensor(float)tensor(float8e4m3fn)tensor(bfloat16)tensor(int32)tensor(uint64)tensor(float16)tensor(int64)tensor(int8)tensor(uint16)tensor(int16)tensor(float8e5m2)tensor(float8e4m3fnuz)tensor(uint8)tensor(float8e5m2fnuz)seq(tensor(int8))seq(tensor(uint64))seq(tensor(int32))seq(tensor(int16))seq(tensor(uint8))seq(tensor(uint32))seq(tensor(uint16))tensor(bool)tensor(string)tensor(complex128)tensor(complex64)seq(tensor(float16))seq(tensor(int64))seq(tensor(double))seq(tensor(float))seq(tensor(bfloat16))seq(tensor(bool))seq(tensor(string))seq(tensor(complex128))seq(tensor(complex64)), but it is already registered from file line version: ) from file Schema error: forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).] (usually, this means you bumped the operator version but Trying to register schema with name (domain: , but its domain is not known by the checker., but its version is not in the inclusive range [Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.An allocator for this device has already been registered for sharing.D:\a\_work\1\s\onnxruntime\core\session\environment.ccinter-opXTNo allocator for this device has been registered for sharing.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.intra-opException caught: Constrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.Y is not implemented in CreateAndRegisterAllocatorV2()valuesession_optionsort_configUnsupported value for intra_op_num_threads: tuning_results at line , column Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: D:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for inter_op_num_threads: Setting intra_op_num_threads to Setting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDParallel modeSequential modeSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to resultsepJson stored in the `ort_config` key cannot be parsed. Error message: validatorsUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting enable_profiling to onnxruntime::SetEnableProfilingThe Model Proto hasn't been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelexecution_mode option in the model file must be an integergraph_optimization_level option in the model file must be an integerintra_op_num_threads option in the model file must be an integerinter_op_num_threads option in the model file must be an integerTuning results stored in the model file cannot be parsed. Error message: . Ignoring...onnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: key '' not foundcannot use at() with cannot use key() for non-object iteratorsbinarybooleannumberdiscardednullinvalid map keyarrayobjectout_of_rangecannot get valueparse errorsyntax error parse_errortype_errorinvalid_iterator; expected invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if given- while parsing unexpected ; last read: 'number literalstring literal'{''['true literalnull literalfalse literalend of inputunknown token'[', '{', or a literal'}'']'','':'invalid comment; expecting '/' or '*' after '/'invalid comment; missing closing '*/'invalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid number; expected digit after '.'invalid number; expected digit after '-'invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017vector too longinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Fiterator does not fit current valueobject keycannot compare iterators of different containersnumber overflow parsing 'object separatortype must be object, but is type must be string, but is type must be array, but is type must be number, but is cannot use erase() with iterator out of rangeAzureExecutionProviderFlattenExpandMaxPoolTransposeReshapeGatherArgMinArgMaxGlobalAveragePoolAveragePoolSqueezeResizeTileUnsqueezeReduceProdReduceMaxReluReduceSumLeakyReluGlobalMaxPoolReduceMinReduceMeanSliceSigmoidSoftmaxLogSoftmaxEluGeluHardSwishHardSigmoidSignCosExpTanhAtanSqrtSinAsinAbsRoundDepthToSpaceNegLRNLogFloorCeilMulDivSubPowClipSpaceToDepthAddLpNormalizationSplitMinConvTransposeConvGridSamplePReluMaxConcatEqualBatchNormalizationGreaterOrEqualGreaterGemmMatMulLayerNormalizationInstanceNormalizationMultiple entries for operator is not supported. OpType=TopKonnxruntime::QDQ::SelectorManager::InitializeSelectorsMapinsertedLessOrEqualLessPadWhereOp version is not supported forD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.cconnxruntime::QDQ::SelectorManager::GetQDQSelectionsInsertedPrecisionFreeCast_Castcast node to cast from float16 to float32 on cpuD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.honnxruntime::GraphTransformer::Recurseonnxruntime::RemoveDuplicateCastTransformer::ApplyImplRemoveDuplicateCastTransformerdtypeonnxruntime::InsertCastTransformer::ApplyImplidx < input_defs.size()toD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::IsIsolatedFp16NodeOnCpudtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.ACLExecutionProviderArmNNExecutionProvidersession.qdqisint8allowedsession.x64quantprecisionoptimization.enable_gelu_approximationsession.disable_double_qdq_removersession.enable_quant_qdq_cleanupQDQFinalCleanupTransformerBiasDropoutFusionBiasGeluFusionsession.qdq_matmulnbits_accuracy_levelAttentionFusionCommonSubexpressionEliminationBiasSoftmaxFusionCastEliminationDivMulFusionDoubleQDQPairsRemoverConvBNFusionConvMulFusionConstantSharingConvAddFusionGatherSliceToSplitFusionGatherToSliceFusionExpandEliminationFastGeluFusionDynamicQuantizeMatMulFusionEmbedLayerNormFusionEliminateDropoutDropoutGemmTransposeFusionGemmActivationFusionGemmSumFusionGeluApproximationGeluFusionLayerNormFusionSimplifiedLayerNormFusionLabelEncoderFusionLabelEncoderEliminateIdentityIdentityNoopEliminationNotWhereFusionMatMulScaleFusionMatmulTransposeFusionMatMul_BatchNormalization_FusionMatMulIntegerToFloatFusionMatMulActivationFusionMatMulAddFusionReluQuantRewriteQuickGeluFusionQDQPropagationTransformerQDQS8ToU8TransformerAvx2WeightS8ToU8TransformerClipQuantRewritePad_FusionPreShapeNodeEliminationTransposeOptimizer_UnsqueezeEliminationEliminateSliceTransposeOptimizerRocmBlasAltImplSkipLayerNormFusionFuseReluClipReshapeFusion4onnxruntime::optimizer_utils::GenerateTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformerUnsupported optimization level: onnxruntime::optimizer_utils::GenerateRewriteRules_RuleBasedTransformerLevelonnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildh~D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.cconnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplAdded by onnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeEnsureUniqueDQForNodeUnit/duplicatedDequantizeLinear MIGraphXExecutionProviderVitisAIExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy onnxruntime::MemcpyTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cc for Memcpy nodes are added to the graph Set session_options.log_severity_level=1 to see the detail logs before this message.. It might have negative impact on performance (including unable to run CUDA graph). Copy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()Add onnxruntime::TransformerMemcpyImpl::AddCopyNode after before Execution type 'Memcpyonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializers with status: onnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cc modified: GraphTransformer Bad optional access failed: Layout/Transpose optimization for QLinearGlobalAveragePoolchannels_lastFusedConvQLinearAveragePoolThis transformer is already registered D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersbetablock_sizeaxisQuantizeLinearD:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.cconnxruntime::GatherSliceToSplitFusion::ApplyImplRangeonnxruntime::GatherToSliceFusion::ApplyImpl/GatherSliceToSplitFusion/Split for Fused Gather nodessplitsSqueeze for Fused Gather nodessqueeze_axessplit_outputSlice for Fused Gather nodesSliceAxesInitializerUnsqueezeAxesInitializeraxesUnsqueeze_Unsqueeze for Fused Gather nodesunsqueeze_output_range_input_defs.size() == 3D:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFoldIfNodeIf node condition initializer: `' is expected to have a single boolean elementendstartConstantFoldingfetches.size() == node->OutputDefs().size()Could not find a CPU kernel and hence can't constant fold Ifonnxruntime::ConstantFolding::ApplyImplUnable to constant fold. InlineIfSubgraph failed node '': Unsupported output type of . Can't constant fold Unsupported data type: div_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodemul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesFusedMatMulConstant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializerD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccalphaFused MatMul and Scale/MatMulScaleFusion/onnxruntime::MatMulScaleFusion::ApplyImplonnxruntime::`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::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsSlice ends is less than INT_MAXExpected value:Slice parameter is not expected. Input index:CheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedStart MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedGemm weight shape is not expectedPass ValidateGemmInitializerunidir mask is not constantonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpermpast_k_transpose perm attribute not matchedFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose has not perm attributek_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32Cast mask from int64 to int32Pass CheckNodesInPathVreshape initializer value is not expectedStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseStart FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLqkv_biasMask shape is unknown or not 2D, or data type unknownonnxruntime::ConvertMaskToInt32D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplshape of layer norm bias tensor not expectedTotal fused Attention node count: MatchPastSubgraph returns falseFused Attention subgraphs Attentionnum_headsunidirectionalmask_filter_valueFused an attention node for GPT.qkv_weightsonnxruntime::FuseSubGraphQKFused an attention node.onnxruntime::FuseSubGraphQKDistilBertFaild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphonnxruntime::FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32epsilonbn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_qdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantonnxruntime::QDQS8ToU8Transformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.ccdropSplitQDQdropdrop_no_int16_supportdrop_no_int16_support_and_positive_scaledropDQ1DQ2DQ*DQDQMatMulToMatMulNBitsQDQSelectorActionTransformerauto_padpadsNOTSETconstantmodeindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)NhwcFusedConvNhwcConvUnsupported operator: and domain: D:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.honnxruntime::NodesToOptimize::GetNodeExpected Conv then Add.activation_paramsonnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"ConvAddReluConvActConvActivationFusionD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::OpTypeactivation != nullptrExpected activation node.activationonnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Failed to get Clip min/max constants.onnxruntime::MatMulActivationFusion::ApplyImpl with activation D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cc_FusedActivationFusedMatMulActivationactivation_UnsqueezeElimination_onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node UnsqueezeElimination cannot remove node due to invalid axesD:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::QuickGeluFusion::ApplyImplQuickGeluD:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.cc/QuickGeluFusion/Unexpected data type for Clip 'min' input of minD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyFuseReluClip__min_zero_constantGelu approximationFastGeluonnxruntime::GeluApproximation::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: BiasGelu@ݑܑݑConcatTrainingCannot replace concat node with initializer:startsendsonnxruntime::ReshapeFusion::Fuse_Subgraphonnxruntime::ReshapeFusion::ApplyImplFused reshape node: Total fused reshape node count: D:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccallowzerotransAtransBD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.ccnew_gemm_output_defs.size() == 1new_gemm_input_defs.size() == 3onnxruntime::GemmSumFusion::Applyonnxruntime::GemmSumFusion::SatisfyConditionSumother_sum_input != nullptrgemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0Fused Gemm with Sum/GemmSumFusion/sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())!sum_input_movedgraph.RemoveNode(gemm_node.Index()) and node != nullptronnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndInvalid node index node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodeD:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndonnxruntime::`anonymous-namespace'::ValidateQDQInsertionEdgesD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.ccExpected at least one edge into which to insert QDQ pair.QDQ insertion edge [0] for NodeArg but expected NodeArg QDQ insertion edge does not have a valid graph NodeArg for must have a source or a destination nodenode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputonnxruntime::`anonymous-namespace'::InsertQDQPairs_pre_qsrc node ("Inserted Q/DQ pair between _qFailed to set op schema for added Q node._q_to_dqInserted by QDQPropagationTransformer] for NodeArg ", index: ] has NodeArg QDQ insertion edge [". at NodeArg "node ("dst node ("onnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::QDQPropagationTransformer::ApplyImplFailed to set op schema for added DQ node.src_node != nullptr_post_dq_dqonnxruntime::`anonymous-namespace'::GetNextPropagationEdgesonnxruntime::`anonymous-namespace'::PropagateDQForwardonnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgedst_node != nullptrdeque too longOutput 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 1 of position shape.onnxruntime::MatchInputToConcatSubgraphGather 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.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Cast Input from int64 to int32_Cast_Int32D:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Input shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputThe parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Second input of Gather should be a constant with value 1. Failed to match Shape node. position_embeddingsmask_indexOptional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.NonZeroConstantOfShapeThe parent of two shape nodes are expected to be input_ids.two paths share the same shapeThe first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Output edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherPosition embedding shape is not expected.Input id is not valid. Failed to match position embedding subgraph.Failed to get position embedding weights.Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). Segment id is not valid. Input_ids and segment id should have the same shape. Word embedding shape not expected.onnxruntime::FuseSubGraphfused EmbedLayerNorm subgraphs EmbedLayerNormalizationPosition embedding shape not matched.Position embedding data type shall be float or float16.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplYieldOpDynamicQuantizeMatMulDynamicQuantizeLinearMatMulIntegerToFloatD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplInvalid free dimension override.FreeDimensionOverrideTransformerwhich does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::ApplyImplThe model has input '' with a fixed dimension size /MatMulAddFusion/fused Matmul and Add D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplfused FusedGemmfused Gemm with activation ScaledTanhSoftsignSoftplusSeluD:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplThresholdedReluParametricSoftplusonnxruntime::GetQConstantLowerUpperD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.ccUnexpected data type for QuantizeLinear input y_zero_point of ConvAddActivationFusiononnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesConvAddActweight_zp_s8_2_u8Unable transforming DynamicQuantizeLSTM operator,onnxruntime::TryConvertDynamicQuantizeLSTMD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.cc int8 overflow might impact precision ! cannot locate recurrence tensor of const int8 type,QLinearMatMulQLinearConvQAttentionQGemmMatMulIntegeronnxruntime::Avx2WeightS8ToU8Transformer::ApplyImplDynamicQuantizeLSTM unable to locate recurrence tensor or its zero point value,Fused Gemm with Transpose/GemmTransposeFusion/D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()onnxruntime::UpdateConsumerCountcast != nullptrtranspose_node.InputDefs().size() == 1onnxruntime::GetTransposePermsfused MatMul and Transpose _transformedonnxruntime::MatmulTransposeFusion::ApplyImpltransBatchAtransBatchBonnxruntime::ReorderCastAndTranspose/MatmulTransposeFusion/Created a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_onnxruntime::ConvMulFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.cconnxruntime::RocmBlasAltImpl::ApplyImpl__backwardpass/SimplifiedLayerNormFusion/SimplifiedLayerNormalization/LayerNormFusion/fused LayerNorm subgraphs onnxruntime::SimplifiedLayerNormFusion::ApplyImplstash_typeD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImpl@ݑܑ ܑݑ@ݑܑݑSkipLayerNormalizationfused SkipLayerNorm subgraphs Cast Input to float_FloatD:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cconnxruntime::SkipLayerNormFusion::ApplyImplreorderstridesReorderInputspatial_bn_nchwcD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformerImpl::TransformConvreshape_nchwcgroupVALIDoutput_channels <= nchwc_output_channelsBuffer overflowkernel_shapedilationsSAME_LOWERSAME_UPPERonnxruntime::NchwcTransformer::ApplyImplfloornearest_modescalesUpsampleasymmetriccoordinate_transformation_modehalf_pixelalign_cornerschannelsReorderOutputlinearnearestonnxruntime::HandleNegativeAxisIsAxisInRange(axis, tensor_rank)D:\a\_work\1\s\onnxruntime\core/providers/common.haxis is not in valid range [-D:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.ccBiasSoftmax into softmax(input + bias)onnxruntime::BiasSoftmaxFusion::ApplyImplis_inner_broadcastؑpؑoutput_dtypeDoubleQDQRemoved_Not eliminating output [ of node representative.output_index != kInvalidOutputIndex] because it's the graph's output.onnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::QDQFinalCleanupTransformer::ApplyImplonnxruntime::`anonymous-namespace'::CleanUpNodeSequenceD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cc with name "Found back-to-back nodes: NotD:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer.ccTranspose optimizer failed: onnxruntime::TransposeOptimizer::ApplyImplGenerated from Matmul BatchNormalization fusionMatMulBnFusion_GemmMatMulBnFusion_GemmB_MatMulBnFusion_GemmBiasvar_tensormatmul_b_tensorbias_tensormean_tensoronnxruntime::MatmulBNFusion::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_bn_fusion.ccscale_tensorD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccT1onnxruntime::NhwcTransformer::ApplyImplNhwcTransformerfused Add-Dropout-(Add) for BitmaskBiasDropoutBiasDropoutBitmaskDropoutD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.cconnxruntime::BiasDropoutFusion::ApplyImplMatMulNBitsFusionMatMulNBitsFuseBiasD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_nbits_fusion.cconnxruntime::`anonymous-namespace'::actions::BiasFusion::ValueMovestarget.GetOutputEdgesCount() == 1Nonnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::RuleBasedGraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::GeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs skeys_values_default_onnxruntime::OpNodeProtoHelper::GetAttrD:\a\_work\1\s\include\onnxruntime\core/framework/op_node_proto_helper.honnxruntime::BiasGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccܑ@ݑܑݑTotal shared scalar initializer count: onnxruntime::ConstantSharing::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.cc  onnxruntime::ConvAddFusion::Applyadd_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccConvAddFusion_Add_B_ConvAddFusion_B_q_or_dq_input_defs.size() >= 2D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.cconnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointonnxruntime::QDQ::IsQOrDQScalePositiveConstantScalarNhwcMaxPoolcom.microsoft.QuantizeLinearstorage_ordercom.microsoft.QLinearAddcom.microsoft.DequantizeLinearcom.microsoft.QLinearConcatcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearReduceMeancom.microsoft.QLinearMulcom.microsoft.QLinearSigmoid does not match rank perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length D:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimization\ort_optimizer_api_impl.cconnxruntime::ApiValueInfo::PermuteDimsPermutation entry out of bounds for shape Added in transpose optimizer_outopset_import_iter != domain_to_version_map.end() domain not found in opset imports.An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.onnxruntime::GetSinceVersionForNewOpsince_version.has_value()Transpose Optimizer is adding an unexpected node: Existing destination type is not compatible with source type.const_transpose_optimizeronnxruntime::ApiGraph::CopyValueInfodst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Failed to get size of TensorProto0 <= p && p_int < shape_proto->dim_size()onnxruntime::ApiTensor::NumElementssize >= 0No NodeArg found for name onnxruntime::ApiTensor::Dataonnxruntime::ApiGraph::GetValueInfonode_arg_ != nullptrsuccessFailed to find initializer for name: Failed to find initializer to reshape with name onnxruntime::ApiGraph::TransposeInitializer to have different number of elementsonnxruntime::ApiGraph::ReshapeInitializernew_num_elts == old_num_eltsCannot reshape initializer M  M  M M MM M MM MMMMM M MMMM M MM M M M M M M pܑ pܑ v pܑ _ pܑ Reciprocalnoop_with_empty_axesIsNaNIsInfCeluShrinkSinhTanAcosCoshAcoshAsinhAndAtanhXorOrkeepdimssaturateBitShiftModCastLikeMeanReduceLogSumExpReduceLogSumReduceL1ReduceSumSquareHardmaxReduceL2LpPoolUnsupported ONNX opset: GlobalLpPoolMaxUnpoolRandomUniformRandomNormalLikeMultinomialRandomNormalRandomUniformLikeD:\a\_work\1\s\onnxruntime\core\optimizer\utils.ccmaxUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax::::operator () `pIndex out of range input arg counts but there are only D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cc, actual input arg count: Expected at least onnxruntime::`anonymous-namespace'::MoveInputOutputImpl::::operator ()Node index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexExpected input arg count of zero for input onnxruntime::`anonymous-namespace'::MoveInputOutputImpltarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.onnxruntime::MoveInputOutputcalled_ == 1size() == other.size()onnxruntime::Initializer::subonnxruntime::Initializer::addExpecting the same sizeAxis must be non-negativeaxis >= 0onnxruntime::Initializer::mulonnxruntime::Initializer::divscalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)onnxruntime::Initializer::scale_by_axisInvalid other(scalers) sizeonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceonnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccInitializer must have a datatypeutils::HasDataType(tensor_proto)Expecting the same data typedata_type() == other.data_type()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.empty()onnxruntime::Tensor::MutableDataAsSpanFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccTried to allocate without valid type information, ort_value index=onnxruntime::OptimizerExecutionFrame::Info::Infoonnxruntime::TryCreateKernelNot enough produced nodes in the runtime optimization record.onnxruntime::SetOpSinceVersionForProducedNodesonnxruntime::MatchAndProcessonnxruntime::SelectorActionTransformer::ApplySelectorsAndActionsNew node op (Too many produced nodes in the runtime optimization record.).) does not match produced node op in runtime optimization record (Applying runtime optimization action Missing action onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizations for transformer Nodes to optimize are not valid, skipping action.Target node is not assigned to a compatible execution provider, skipping action.Existing registration with name D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.ccMatched inserted_in_name_to_entryonnxruntime::SelectorActionRegistry::RegisterSelectorAndActiongenerated at runtimeQLinearMatMulNBits accuracy level must be between 0 and 4opsetinit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14ainit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8KD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\qdq_actions.cconnxruntime::QDQ::DQMatMulToMatMulNBitsAction::DQMatMulToMatMulNBitsActionaccuracy_level_ >= 0 && accuracy_level_ <= 4onnxruntime::QDQ::DQMatMulToMatMulNBitsAction::ProcessNewNodeBuffered tensors map cannot be nullbitsaccuracy_levelFailed to add buffered tensor fused_DQ_MatMul_zero_point_T_TFailed to set node op schema.onnxruntime::ReplaceWithNew::RunForSaveonnxruntime::ReplaceWithNew::Runonnxruntime::CreateReplacementNodeFailed to remove node.D:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::MergeIntoTarget::RunOpenVINOExecutionProviderDnnlExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderQNNExecutionProviderNnapiExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderTvmExecutionProviderVSINPUExecutionProviderCANNExecutionProviderMake the EP: name shorteronnxruntime::GetAvailableExecutionProviderNames::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.ccprovider.name.size() <= kMaxExecutionProviderNameLenPؑpؑؑp  ؑؕP8ؑ𕒀8Pxh~cubiconnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistry is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max()perm size: does not match input rank: is repeated.Attribute perm of Transpose has an invalid value. Value perm: does not align with rank of input data: [ Method IncrementIndexAndComputeOffset assumes this value is strictly positive.(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=onnxruntime::Transpose::Computeinput_tensor_ptr != nullptrMismatched data types between input and output Tensors. onnxruntime::UnpackInt4TensorFailed to unpack Int4x2 Tensor to an int8_t Tensor(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)Expected to transpose int4 tensoronnxruntime::TypedDoTransposeEltWiseFailed to pack 8-bit Tensor into 4-bit Tensoronnxruntime::DoTransposeInt4boxes 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.boxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensoroutput != nullptrThe most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsonnxruntime::NonMaxSuppression::Compute0 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppressioncenter_point_boxcenter_point_box only support 0 or 1Missing/Invalid 'axis' attribute valueonnxruntime::Gather::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.ccGather Tind type not supported in this build.info.GetAttr("axis", &axis_).IsOK()onnxruntime::GatherBase::GatherBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hTind must be within the inclusive range [indices element out of data bounds, idx=info.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueX != nullptronnxruntime::UnsqueezeBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAxes input is null'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::Computeaxes_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 axisFor per axis quantization, x_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)T2x_zero_point and x_scale must have the same shape for blocked quantizationzero_point_ptr->Shape()[i] == scale.Shape()[i]For per axis quantization, scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimonnxruntime::PrepareForQDQD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.ccblock_size must be 0 for per-tensor quantization.quant_block_size == 0x_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_scale must be ceil(Di/block_size) on the quantize axis i for blocked quantizationscale.Shape()[i] == (input_shape[i] + quant_block_size - 1) / quant_block_sizex_scale and x must have the same shape despite the quantize axis for blocked quantizationscale.Shape()[i] == input_shape[i]x_scale and x must have the same rank for blocked quantizationscale.Shape().NumDimensions() == input_shape.NumDimensions()x_zero_point must be null or have the same rank as x for blocked quantizationzero_point_ptr == nullptr || zero_point_ptr->Shape().NumDimensions() == input_shape.NumDimensions()onnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeDequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeUnsupported input type.onnxruntime::QuantizeLinear::Computeonnxruntime::DequantizeLinear >::Computeonnxruntime::DequantizeLinear >::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeDequantizeLinear into BFLOAT16 is not implemented yet.onnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::QuantizeLinear >::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear >::DequantizeLinearonnxruntime::QuantizeLinear >::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear >::Computeblock_size_ >= 0onnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::QuantizeLinearonnxruntime::QuantizeLinear::Compute'block_size' must be non-negative.onnxruntime::DequantizeLinear::DequantizeLinearonnxruntime::DequantizeLinear::DequantizeLinearD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valueedgewrapInvalid 'mode' attribute valueonnxruntime::PadBase::PadBasereflectPads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)onnxruntime::Pad::ComputeValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorPads tensor size should be equal to twice the input dimension count pads_data.size() == 2 * data_rankUnsupported input data type of onnxruntime::PadValueFromFloatvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::SliceIteratorBase::InitCannot use 'edge' mode to pad dimension with a value of 0. Input shape:dims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()onnxruntime::PadBase::ComputePadsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor size should be equal to twice the number of explicitly provided axes.pads_data.size() == narrow(2 * num_axes)Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Axes tensor should be a 1D tensor num_axes_dims == 1Input tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesdata_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroBIonnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size() but has D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.ccLoopVGraph in 'body' attribute of Loop should have inputs. Found:All scan outputs MUST be tensorsLoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: Loop had zero iterations and the shape of subgraph output onnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutput was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::ExecuteInconsistent shape in loop output for output. Expected:onnxruntime::Loop::Initinfo.GetAttr("body", &proto).IsOK()'Loop' node has outputs so the subgraph requires Got:num_subgraph_outputs - 1 == num_outputsonnxruntime::Loop::Computesession_statefeeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.info_ == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.Subgraph SessionState was not found for 'body' attribute.onnxruntime::Loop::SetupSubgraphExecutionInfo outputs.then_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_' attribute.If nodes condition input must have exactly one elementinfo.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptronnxruntime::IfImpl::ExecuteFailed to create output tensor for If output onnxruntime::IfImpl::InitializeSubgraph SessionState was not found for 'Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for . shape=Dimension of input must be 1 instead of axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a vector tensor.onnxruntime::SqueezeBase::ComputeOutputShapeinput_shape[i] == 1onnxruntime::Squeeze::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.hD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenThe rank of input tensor must be >= axisinput count mismatchonnxruntime::Flatten::Computegsl::narrow_cast(X_shape.NumDimensions()) >= axisSizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hfinal_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputAttempt to retrieve final output before it was set.D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsonnxruntime::Scan<9>::Init of Invalid value in scan_input_axes for input Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was ' dimension but input 'Invalid scan input: has length of Output OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axes but expected Number of entries in 'scan_input_axes' was scan_output_directionsscan_input_axesinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directions dimensions or more but input had shape of Expected onnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::SetupSubgraphExecutionInfofeeds_fetches_manager_ && info_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsonnxruntime::ScanImpl::TransposeOutputScanInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::Execute. Output tensor rank was At most one dimension can be -1.unknown_dim == -1A dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size != -1size != 0 && (input_shape_size % size) == 0input_shape_size == size, requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()Attribute shape is not set.onnxruntime::Reshape_1::Reshape_1onnxruntime::Reshape::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1Unable to get an allocatoronnxruntime::IdentityOp<1>::Computeonnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.ht_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseD:\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 >::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: Must have a single dimension of 1t_proto_p->dims()[0] == 1onnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeTensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())utils::HasDataType(t_proto)Unsupported value attribute datatype: invalid stod argumentstod argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stoll argumentstoll argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastAttribute saturate is only used for cast to float 8 types.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1onnxruntime::`anonymous-namespace'::CastToStringFailed to write value with snprintf().snprintf() failed with return value: snprintf_result > 0INF%.8gNaN-INFStringSplitT3delimitermaxsplitInvalid regex pattern: patternRegexFullMatchD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\regex_full_match.cconnxruntime::RegexFullMatch::RegexFullMatchre_.ok()StringConcatdetect_positivedetect_negativeNull input ptrtanhnoneUnsupported approximation_algorithm: approximatebad allocationAffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5.AffineGridmode_ != CubicOnly support GirdSample in 4-D or 5-D cases.onnxruntime::GridSample::Compute" not supported, expect linear, nearest or cubicmode "onnxruntime::GridSample::GridSampleLast dimension of grid: grid_dims[grid_dims.NumDimensions() - 1] == data_dimsOnly 4-D or 5-D tensor is supportedinput_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5 does not match input batch size Grid batch size grid_dims[0] == NOnly support GridSample Cubic mode in 4-D cases.for input dimension of grid dimensions must be static_cast(grid_dims.NumDimensions()) == data_dims + 2onnxruntime::GridSample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.cc, expect " not supported, expect zeros, border or reflectionpadding_mode "onnxruntime::GridSample::GridSampleD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.hbilinearbicubic" not supported, expect bilinear, nearest or bicubiczerospadding_modereflectionborder@ `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_?dft_length must be a scalar value.onnxruntime::discrete_fourier_transformD:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.ccdft_length must be greater than zero.Unsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::DFT::Computeonnxruntime::STFT::ComputeUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.DFTSTFTonesidedinverseDivide by zeroSafeIntExceptionHandler::SafeIntOnDivZeroratio input should have a single value.tensor->Shape().Size() == 1onnxruntime::signal::get_scalar_value_from_tensorD:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.hsignal shape must end in 1 (real) or 2 (real, imaginary).signal_components == 1 || signal_components == 2onnxruntime::short_time_fourier_transformIf both frame_length and window are set, then the size of the window must be equal to the frame_length.onnxruntime::dft_bluestein_z_chirpUnsupported bit size.onnxruntime::bit_reverseframe_length == window_lengthEnsure that the dft size is smaller than the signal.window_size <= signal_sizeOnly works on matrices with three dimensions.fast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKfast_shape.size() == 3onnxruntime::ValidateFastReduceRKRfast_shape[0] * fast_shape[2] == output.Shape().Size()count == 1Reduction on all axes, output size should be 1.onnxruntime::ValidateMustBeOverloadedmust be overloaded.fast_shape.size() == 2Only works on matrices with two dimensions.Output size mismatch.onnxruntime::ValidateFastReduceKRonnxruntime::ValidateFastReduceRKfast_shape[0] == output.Shape().Size()last_loop_red_size > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptyprojected_index.size() > 0last_loop_size > 0select_last_indexinfo.GetAttr("keepdims", &keepdims).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_kernel_base.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseonnxruntime::CommonFastReduceCopyonnxruntime::ValidateNoTransposeReduceonnxruntime::ValidateKeepDimsCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseAxes input and attribute should not both be present for reduction.onnxruntime::check_and_reduce_empty_set_inputaxes.empty() or 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 . It can only be (default) or onnxruntime::UpsampleBase::StringToUpsampleModemode attribute is not_smallernot_larger model uses the deprecated attributeextrapolation_value`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset cubic_coeff_around_prefer_floor and anti-aliasing is set to exclude_outsideonnxruntime::UpsampleBase::UpsampleBaseinfo.GetAttr("mode", &mode).IsOK()antialiasD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.h(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.keep_aspect_ratio_policystretchUpsample operatorResize operator'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 'Linear' mode only supports: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the onnxruntime::UpsampleBase::ParseScalesDatascales size should be greater than 0.all values in axes should be less than rank of the dataround_prefer_ceilonnxruntime::UpsampleBase::StringToCoordinateTransformationModenearest_mode:[ceilScale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than 0.onnxruntime::UpsampleBase::ScalesValidationkeep_aspect_ratio of [] is not supported!pytorch_half_pixelonnxruntime::UpsampleBase::StringToKeepAspectRatioPolicytf_crop_and_resizetf_half_pixel_for_nncoordinate_transform_mode:[half_pixel_symmetric by axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. onnxruntime::TensorAllocator::TensorAllocatorstart_offset % span_size == 0 && real_end % span_size == 0onnxruntime::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:D:\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::BroadcasterBitwiseOrBitwiseNotUnsupported X type: BitwiseXorinputCount >= 1Must have 1 or more inputsD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Min_6::ComputeBitwiseAndonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::mod_internal::CallModImpl::operator ()input_size < std::numeric_limits::max()D:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeshape_data_tensor.Shape().GetDims().size() == 1Tensor with shape information must be 1 dimensional.fmodonnxruntime::UntypedExpand(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1fmod attribute must be true for floating point typesonnxruntime::Mod::Moddata_n.Shape() == shapeAll inputs must have the same shapeonnxruntime::Mean_6::Computeonnxruntime::Max_6::ComputeExpandBroadcastLooper should only have a shape for the second input.data_1.Shape() == shapeonnxruntime::ExpandBroadcastLooper!helper.HaveTwoTensorInputs()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computehelper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.onnxruntime::BroadcastLooperonnxruntime::BitShift::Compute::::operator ()cur1 == end1onnxruntime::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.input_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computeonnxruntime::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::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()'. Valid values are 'LEFT' or 'RIGHT'.RIGHTonnxruntime::BitShift::BitShiftInvalid direction value of 'onnxruntime::BitShift::BitShiftonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKerneldirectionLEFTonnxruntime::BitShift::BitShift((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOpDilations dimensions should match kernel shapedilations.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0ceil_modecount_include_padD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()Unknown AutoPadType Stringonnxruntime::StringToAutoPadTypeinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesNo kernel shape is set.kernel_shape num_dims is not compatible with X num_dims.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.`InlinedVector::at(size_type)` failed bounds checkonnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.Input dimension cannot be less than 3.onnxruntime::PoolBase::Computeonnxruntime::PoolProcessContext::initD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::MaxPoolV8::ComputeImplD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.honnxruntime::LpPoolV18::LpPoolV18onnxruntime::AveragePoolV19::ComputeUnsupported kernel dimension : onnxruntime::LpPoolV18::Computeonnxruntime::Pool::ComputeUnsupported pooling size : typeonnxruntime::Optional::OptionalD:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.hOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()D:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableOptionalGetElementonnxruntime::PropagateInputOrtValueToFirstOutputThe TypeProto attribute in the Optional op Trying to use OptionalGetElement on an optional type OrtValue which contains no dataonnxruntime::Optional::Computecan only be of type(tensor) or (seq(tensor))OptionalOptionalHasElementOonnxruntime::OptionalGetElement::ComputesplitInvalid value in 'split' attribute. All values must be > 0 Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Axis=Input cannot be split evenly on selected axis. Input shape=The split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1 Input shape=Cannot split using values in 'split' attribute. Axis=Invalid num_outputs value of NumOutputs=. Size of dimension being split is D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hnum_outputsstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBaseIf 'num_outputs' is specified, the 'split' input should not be provided.dst.DataType() == src.DataType()onnxruntime::DispatchStridedCopysrc and dst types must matchonnxruntime::SplitImpl::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()counter.current_offset == lastcopy shape must have non-negative sizetotal_num_elements_to_copy >= 0dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()onnxruntime::StridedCopyD:\a\_work\1\s\onnxruntime\core/framework/copy.hlast >= firstCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.ScatterScatterElementsCPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'. vs Indices vs updates dimensions differs at position=data type is different from updates typeIndices and updates must have the same rankCPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.muladdScatterElements op: input tensor must have at least one dimensiononnxruntime::ScatterDatareductiononnxruntime::Scatter,class std::allocator > > >::ScatterD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cc is greater than input dim= at pos=. Input rank=Indices must have the same rank as Input. Indices rank=Indices dim=Indices type is not supported., indices shape: ScatterNDCPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: 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 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 '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 'min'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'., data shape: last dimension of indices must not be larger than rank of input tensorinput tensor and indices tensor must has rank larger than 0. input 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'.updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()invalid indice found, indice = onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()Col2Imstrides_.empty()strides_.size() == image_dim_numberpads_.empty()dilations_.empty()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.honnxruntime::Col2Im::Col2Imonnxruntime::Col2Im::Computedilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.size of 'strides' attribute, if provided, should equal to the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.ccUHannWindowMelWeightMatrixBlackmanWindowHammingWindowperiodicoutput_datatypeonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()highest_index >= 0 && highest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.D:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cconnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()lowest_index >= 0 && lowest_index < num_spectrogram_binslower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'No attribute with name:'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Compute'is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelgammaonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.hunknown kernel typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.cconnxruntime::functors::ElementWiseRangedTransform::Createonnxruntime::functors::ScaledTanh::Initwill 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_scaleonnxruntime::RoiAlignBase::RoiAlignBaseD:\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 Number 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 matchRoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrInvalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid input var: 0th dimension != Invalid input var: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != is_spatial_onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormtraining_modemomentumTraining mode only supports spatial BNRNNonnxruntime::RNN::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccforwardreversebidirectionalcliplayoutinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN::RNNAffinelayout_ == 0activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.activationsinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betap != nullptr || size == 0onnxruntime::IAllocator::ValidateAllocationD:\a\_work\1\s\include\onnxruntime\core/framework/allocator.hMemory allocation failed. Size='. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::Computeactivation_func_names.size() == static_cast(num_directions_) * 3LSTMonnxruntime::DeepCpuLstmOp::PrePackD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.finput_forgetsigmoidD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()onnxruntime::LSTMBase::LSTMBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.hallocator != nullptronnxruntime::IAllocator::ValidateAllocator * Invalid size requested for allocation: onnxruntime::IAllocator::ValidatedCalcMemSizeForArrayonnxruntime::DeepCpuGruOp::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccactivation_func_names.size() == static_cast(num_directions_) * 2GRUGRU operator does not support double yetInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hlinear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()Can not get unpacked span from prepacked weights!is_prepacked_onnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::SafeRawPointerC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())lda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpaninfo.GetAttr("upper", &temp).IsOK()upperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::TriluInput tensor should have a rank of at least 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cck should be a 1-D or 0-D tensor.Triluonnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)Axis tensor should be 0D or 1DAxis tensor must be provided to the CumSum opCumSumAxis tensor should be of type `int32_t` or `int64_t`exclusiveonnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::WritableSliceIterator::Initdims.size() == steps.size()onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initextents.size()=onnxruntime::WritableSliceIterator<__int64>::Initsteps.size()=dims.size() == extents_.size()starts.size()=dims.size() == starts.size()dims.size()=onnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.ccHardmax inputs N, D and N * D must be < . N=onnxruntime::Hardmax::Compute, D=D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.ccD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computeindices tensor data type not supportedindicesGatherNDinput_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchbatch_dimsinvalid index found, index = indices tensor must has rank larger than 0X input is required!onnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::Computeonnxruntime::NonZero::Computefailed to get first output!onnxruntime::DepthToSpace::DepthToSpaceDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hCRDinfo.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseblocksizeAttribute blocksize is not set.onnxruntime::DepthToSpace::ComputeUnsupported input type in DepthToSpace op: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccUnsupported input type in SpaceToDepth op: onnxruntime::SpaceToDepth::ComputeDepthToSpace requires input depth to be a multiple of (block_size * block_size)SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeSpaceDepth ops require a 4-D input. Provided rank: GatherElements op: Data type of input 'data' should match the data type of the outputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElementsGatherElements op: Cannot operate on scalar inputonnxruntime::GatherElements::GatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GetIndexGatherElements op: Out of range value in index tensoronnxruntime::core_implD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.ccGatherElements op: Unsupported tensor type, size:onnxruntime::Tile::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cc'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: the tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensionalInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is empty'axes' has duplicates'step' value cannot be 0'axes' has an axis outside of the tensor dimension countInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()onnxruntime::SliceBase::SliceBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.hMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Unexpected element size of Cannot slice scalarsonnxruntime::SliceBase::ComputeStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got Starts and ends shape mismatchStarts and axes shape mismatchonnxruntime::SliceBase::FillVectorsFromInputEnds must be a 1-D arrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccStarts must be a 1-D arrayonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerSteponnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()Must have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseData type mismatchonnxruntime::ConcatBase::ComputeImpl has mismatched dimensions of Non concat axis dimensions must match: Axis Ranks of input data are different, cannot concatenate them. expected rank: input_rank == reference_rankCannot concatenate scalars got: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccinput != nullptronnxruntime::ConcatBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.hnew_axisonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeD:\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 shape0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1seedMeanVarianceNormalizationonnxruntime::MeanVarianceNormalization::ComputeNo valid axes are specified. This is not handled now.normalize_varianceD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\mean_variance_normalization.cconnxruntime::`anonymous-namespace'::ComputeMeanVarianceNormalization2DX and Y must both have M * N elements.across_channelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4biasbeta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()size_ % 2 == 1size_ > 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()sizeGEMM: Dimension mismatch, W: K: N:right[i] >= 0left[i] >= 0right.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.honnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1info.GetAttr("transB", &temp).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.honnxruntime::GemmBase::GemmBaseinfo.GetAttr("transA", &temp).IsOK()onnxruntime::GemmBroadcastBiasc_shape != nullptrc_shape is required if c_data is providedM_ >= 0 && K_ > 0 && N_ >= 0Gemm: Invalid bias shape for broadcastD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::Computeleft_num_dims and right_num_dims must be >= 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computenum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad != num_output_dimsright operand cannot broadcast on dim left operand cannot broadcast on dim left_num_dims > 2 && left_num_dims == right_num_dimsTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueMatMul dimension mismatchonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::Computemin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.hmin should be a scalar.min->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()invalid expand shapeEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetEinsum op: An implementation for the input type ->equationMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()onnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hEinsumOneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computekop_kernel_info.GetAttr("k", &k_temp).IsOK()onnxruntime::TopkOpset9ConstructorCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()onnxruntime::TopkOpset10ConstructorCommononnxruntime::TopkOpset11ConstructorCommonlargestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()] should not be greater than specified axis dim value [k argument [input count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeoutput count mismatch, expected 2 outputs to be present for TopK operatorData type is not supported in this build.Unsupported tensor type of Uniquedelta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:SequenceInsertonnxruntime::SequenceEmpty::Compute), input tensor data type (onnxruntime::CloneTensorSequenceEraseData type of the input tensor MUST be same as that of the input sequence. Sequence data type (num_inputs >= 1SequenceConstructViolation of the requirement that all input tensors must have the same data type.onnxruntime::SequenceConstruct::Compute) != split_dim_size (SplitToSequenceInvalid data type for split tensor split_size_sum (onnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputsplit_scalar > 0Split should be > 0Invalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })SequenceLengthSonnxruntime::GetSeqIdxSequenceAt) specified for sequence of size (D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceAt::ComputeInvalid sequence index (Unsupported 'dtype' value: SequenceEmptyConcatFromSequenceonnxruntime::ConcatFromSequence::ComputeGot nullptr for sequence input.D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccInvalid input shape: output_paddinggroup count is <= 0output_shape filter_number: num_input_channels: Input channels is not divisible by group.filter number not equal to input channel number.D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeonnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesonnxruntime::ConvAttributes::ConvAttributesauto_pad == AutoPadType::NOTSET W: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.hkernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape. channels_last: X num_dims does not match W num_dims. X: group: onnxruntime::ConvTranspose::DoConvTransposeD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. kernel channels: onnxruntime::ComputePadAndOutputShapeInput channels C is not equal to kernel channels * group. C: Output channels M is not divisible by group. M: Not enough elements in kernel shape. Expected: Not enough elements in strides. Expected: Not enough elements in pads. Expected: Not enough elements in dilations. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeonnxruntime::Conv::Computeoutput and sum shape must matchD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0onnxruntime::MaxUnpool::MaxUnpoolpads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.onnxruntime::MaxUnpool::Computeoutput_shape is smaller than minimum required. output_shape:Shape must be 1 dimensional as it's tensor data of a shape inferred output shape:Detonnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccMatrix dimensions are not equal. Square matrix is expectedonnxruntime::Compress::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccCompressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()size overflowonnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.ccx_ptr != nullptrD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()batch_axisInvalid batch_axis of . Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()time_axistime_axis and batch_axis must have different values but both are time_axis < 2Invalid time_axis of batch_axis < 2batch_axis != time_axis. Value must be in range [0,Invalid sequence length: Unknown tensor type of sequence_lens shape must be {batch_size}. Got:. batch_size=ReverseSequenceD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::ComputeT4prepacked_buffers[0].get() == nullptrD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.cconnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeOutputScaleIsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsValidQuantParam(W_scale, M)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeOffsetIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.IsValidQuantParam(W_zero_point, M)QLinearConv : filter zero point shape invalidIsScalarOr1ElementVector(Y_zero_point)onnxruntime::QLinearConv::ComputeOffsetonnxruntime::QLinearConv::ComputeOutputScaleW_zero_point_data[i] == W_zero_point_valueConvIntegerD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.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::MatMulComputeHelper::Compute::::operator ()IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validD:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.cconnxruntime::MatMulInteger::ComputeIsBQuantParamSupported(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(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(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 1StringNormalizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\text\string_normalizer.cconnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesMultiByteToWideChar failed errcode = onnxruntime::string_normalizer::Utf8ConverterWindows::ComputeRequiredSizeToUtf8WideCharToMultiByte failed errcode = - attribute is_case_sensitive is not setis_case_sensitiveen-USonnxruntime::string_normalizer::Utf8ConverterWindows::from_bytesLOWERattribute case_change_action is not setcase_change_actiononnxruntime::StringNormalizer::StringNormalizerlocaleattribute case_change_action has invalid valueNONEUPPERSingle dimension value must be greater than 0onnxruntime::StringNormalizer::Compute::::operator ()onnxruntime::StringNormalizer::Compute::::operator ()stopwordsonnxruntime::StringNormalizer::ComputeInput dimensions are either[C > 0] or [1][C > 0] allowedResize: input tensor's rank does not match the output tensor's rank.onnxruntime::UpsampleBase::ParseSizesDataaxes should be less than output_dims.size() Output dim value: onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatch Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. Upsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseCompute: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeUpsample: unexpected modeonnxruntime::UpsampleNearestInvalid roi input index.onnxruntime::Upsample::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeResize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputemin_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_countsNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= std::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })weights must be of equal size but ngram_indexes size: ngram_indexesNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowed!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()Got weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specified(items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0.-gramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole onnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDFTfIdfVectorizermode is requiredmin_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0TFIDF is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNonemax_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0max_gram_length >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_onnxruntime::ngram_details::PopulateGrams id: Duplicate ngram detected, size: p.first->second->id_ == 0op_kernel_info.GetAttr("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()EyeLikeEyeLike : Input tensor dimension is not 2Scan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initialize has batch size of but onnxruntime::Scan<8>::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoInvalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesScan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::ValidateInput did not match batch size of sequence_lens length of onnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hpooled_shapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPoolspatial_scale_ > 0pooled_height_ > 0pooled_shape.size() == 2info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_width_ > 0R->Shape()[1] == 5MaxRoiPoolD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::Computeroi_batch_id < batch_sizeroi_batch_id >= 0p_ == 1 || p_ == 2op_kernel_info.GetAttr("p", &p_).IsOK()onnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormInvalid input data: number of dimensions is less than 3: onnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.hMismatch between input data and B: size of B != input channel count vs. Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::Computeonnxruntime::RandomNormal::RandomNormalinfo.GetAttr("mean", &mean_).IsOK()scaleD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hmeanbatch_size is < 1Empty dimensions for input tensornum_samples is < 1num_classes is < 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::RandomNormalLike::Computeonnxruntime::RandomUniformLike::ComputeCould not infer data type from input tensor with data type Output type not supported in this build: Invalid data type of onnxruntime::RandomNormalLike::RandomNormalLikeinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("high", &high_).IsOK()highinfo.GetAttr("dtype", &dtype).IsOK()info.GetAttr("scale", &scale_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttr("sample_size", &num_samples_).IsOK()sample_sizeONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::Multinomial::Multinomiallowonnxruntime::RandomUniform::RandomUniformonnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttr("low", &low_).IsOK()onnxruntime::MultinomialComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::ml::GetDefaultresult.IsOK()LabelEncoder could not unpack default tensor default_tensoronnxruntime::ml::LabelEncoder::LabelEncoderclasses_stringsInput of tensor(string) must have output of tensor(int64)info.GetAttr("default_int64", &default_int_).IsOK()keys_floatsInput of tensor(int64) must have output of tensor(string)default_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_int64info.GetAttr("default_string", &default_string_).IsOK()keys_int64sdefault_floatvalues_int64s_Unusedvalues_stringsvalues_floatskeys_stringsonnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::GetAttributeLabelEncoder is missing attribute LabelEncoder could not unpack tensor attribute onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,double>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,double>::LabelEncoder_4values_tensorkeys_tensorkeys.size() == values.size()Keys and values must have the same length.onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,short>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_4 and the number of values is ) must have the same length. However, the number of key is onnxruntime::ml::LabelEncoder_4<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_4<__int64,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2 attributes in LabelEncoder (name: num_keys == num_valuesThe TreeEnsembleRegressorBRANCH_LTBRANCH_GTEBRANCH_LEQLEAFSUMMINSOFTMAX_ZEROAVERAGELOGISTICSOFTMAXBRANCH_GTBRANCH_EQpost_transformn_targetstarget_weights_as_tensortarget_weightsonnxruntime::ml::detail::TreeEnsembleCommon::InitD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.h-base_values_as_tensornodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()onnxruntime::ml::detail::TreeEnsembleCommon::computen_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initaggregate_functionUnknown aggregation function in TreeEnsemble.base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()Node (coor.node_id >= 0 && coor.node_id < n_nodes_) is already there. in tree base_values.empty()nodes_modes.size() < std::numeric_limits::max()nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()base_valuesnodes_falsenodeidsInput shape needs to be at least a single dimension.nodes_truenodeidsnodes_valuesnodes_nodeidsnodes_treeidsnodes_missing_value_tracks_truenodes_modesnodes_featureidsnodes_hitratesclass_weights_as_tensornodes_hitrates_as_tensornodes_values_as_tensortarget_nodeidstarget_weightsbase_values_as_tensortarget_idstarget_treeidsonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccFalse node must always be the next node, but it isn't at index onnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon::AddNodes with flags but got Tree id mismatch. Expected at position (weights).TreeEnsemble only works on 1D, 2D tensors.) is different from n_nodes (Number of nodes in nodes_ (A node cannot point to itself: (falsenode). (truenode).Unable to find node onnxruntime::ml::detail::TreeAggregatorMax::MergePredictionD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggpredictions.size() == predictions2.size() but input tensor has One path in the graph requests feature onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg features.predictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator::FinalizeScoresit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictionX dims is empty.class_idsclasslabels_int64sbase_values_as_tensorclass_weightsclasslabels_stringsclass_treeidsclass_nodeidsonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.cconnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initclasslabels_int64sTreeEnsembleClassifieronnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionpredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpZipMapMust provide classlabels_strings or classlabels_int64s but not both.Input features_per_batch[Zipmap only supports 1D or 2D input tensors] != number of classlabels[D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countPOLYRBFLINEARkernel_paramsonnxruntime::ml::SVMCommon::SVMCommonkernel_typeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hSVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccUnexpected mode:Invalid argumentonnxruntime::ml::SVMRegressor::Computerhocoefficientsn_supportsonnxruntime::ml::SVMRegressor::SVMRegressorsupport_vectors!coefficients_.empty()one_classonnxruntime::IAllocator::AllocArrayD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.hSVMClassifierclasslabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()prob_bonnxruntime::ml::SVMClassifier::SVMClassifiervectors_per_classprob_aonnxruntime::ml::SVMClassifier::ComputeUnsupported data type of classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Unexpected value for 'add_second_class' of scores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceScalerEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.!scale_.empty()Empty scale in attributesoffsetonnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()Scale size: () != (D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOpUnknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64sNormalizerRank of input to Normalized must be less than 2. Got MAXD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1Unexpected NORMALIZE value of D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressortargetsinterceptsLinearRegressorInput shape had more than 2 dimension. Dims=Scores output is incorrect size. Expected: Found:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifiermulti_classLinearClassifierUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplscores_output_data.size() >= scores_output_sizeonnxruntime::ml::CastInputToFloatshape_size == out.size()Empty input dimensions.Empty value of imputed values.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:string_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOpDictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpInput of int64 must have output of string onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hnum_entries == int_categories.size()CategoryMapperInput of string must have output of int64invalid stof argumentstof argument out of range%fDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCast Expected std::map or std::mapInvalid input type of value: 0.fUnexpected CAST_TO value of D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMapNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccBinarizer is NaNInput data with index: thresholdArrayFeatureExtractorInvalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[Invalid argument: X input has empty dimensions.Unsupported typeiteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextonnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #validInvalid values in 'Subgraph must have the shape set for all outputs but did not.onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::AllocateOutputonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_onnxruntime::scan::detail::Info::Infonum_variadic_inputs == num_subgraph_inputs' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.ccdirections.size() == num_entriesNumber of entries in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsThe subgraph in 'body' requires inputs but Scan was only given op_kernel_info.GetAttr("axis", &axis_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cconnxruntime::LayerNormImpl::LayerNormImpl. Size of scale and bias (if provided) must match this. Got scale size of and bias size of onnxruntime::`anonymous-namespace'::ComputeImplSize of X.shape()[axis:] == Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {affineInput initial_h must have shape {leakyrelureluscaledtanhthresholdedreluInput X must have 3 dimensions only. Actual:Input W must have shape {}. Actual:Input B must have shape {Input R must have shape {Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fweights.quant_para_Invalid activation function of alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameeluhardsigmoidsoftplussoftsignonnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got A + (M * K) <= A_endD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.cconnxruntime::LSTMBase::ComputeImplInput P must have shape {Input initial_c must have shape {SoftmaxCPU inputs N, D and N * D must be < Right shape: Ranks of pair-wise operands must be equal. Right shape: right.Shape().Size() == right_shape_override.Size()left_dim == right_dimEinsum op: Input shapes do not alignleft_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsLeft shape: The override dims are not compatible with given tensor's shape. Left shape override: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputcandidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumComputePreprocessor::RunD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccEinsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleEinsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript provideddim_iter == rank... in the same dimensionAnother operand has a dim value of Found '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input . The shape is: is incompatible in the dimension Output subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the inputFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersCannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Einsum op: Unsupported data type for Diagonal for input shape input_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyEinsum op: The candidate output does not match the actual output's shapeinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Transpose failed: Length of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::Transposeonnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulData types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: InverseSkipSimplifiedLayerNormalizationgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchinput is expected to have 3 or 2 dimensions, got last two dimensions of skip needs to be same as inputbias is expected to have 1 dimension, got Last dimension of bias and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormepsilon_ >= 0skip is expected to have 3 or 2 dimensions, got skip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccScaleinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerDynamicSlice) + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder (Attribute border needs to be specified with four border elements, got input dimensions insteadCroponnxruntime::contrib::Crop::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) needs to be greater than or equal to the leftBorder (Input's width () + scale_[0] () + scale_[1] (pred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)BifurcationDetectormax_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_src_tokens_len >= prev_suffix_match_idx_dataonnxruntime::contrib::BifurcationDetector::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.hmin_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()min_ngram_sizeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()onnxruntime::contrib::BifurcationDetector::BifurcationDetectorscores_dims.size() == 2scores_dims[0] == batch_sizeTidNGramRepeatBlocktoken_id < vocab_sizeonnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()input_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.hngram_size_ > 0ngram_sizeonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::ComputeCDistmetricinfo.GetAttr("metric", &metric).IsOK()The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDistonnxruntime::contrib::CDist::CDistD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1Null crop_size_ptrCropAndResizeConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MatMulFpQ4::Computeblob_shape.NumDimensions() == 1Right hand side of MatMulFpQ4 must be a 2D matrix!D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_fpq4.ccSecond input of MatMulFpQ4 must be a 1D blob!blk_quant_typeMatMulFpQ4(size_t)blob_len == buf_sizeOperator MatMulFpQ4 not yet supported on this hardware platform.b_shape.NumDimensions() == 2Quantized and packed blob size differ from expected!buf_size > 0Status::OK() == info.GetAttr("N", &N_)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_bnb4.ccquant_typeStatus::OK() == info.GetAttr("block_size", &block_size_)onnxruntime::contrib::MatMulBnb4::MatMulBnb4Status::OK() == info.GetAttr("K", &K_)MatMulBnb4Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.Status::OK() == info.GetAttr("quant_type", &quant_type_)onnxruntime::contrib::MatMulBnb4::Computequant_type_ == FP4 || quant_type_ == NF4D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dequantize_blockwise_bnb4.honnxruntime::contrib::DequantizeBlockwiseBnb4only block size 16, 32, 64, 128, 256 are supported.quant_type == FP4 || quant_type == NF4;*??>?*>>**920k2ʾM?5=qx=$>|>:>>?9??column_wise_quant_onnxruntime::contrib::MatMulNBits::ComputeRow-wise quantization is not supported for nowonnxruntime::contrib::MatMulNBits::MatMulNBitsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_nbits.ccOnly 4b quantization is supported for MatMulNBits op, additional bits support is planned.nbits_ == 4TransposeMatMulinput_num_bytes % 4 == 0Invalid assumption of output element sizesizeof(uint32_t) == output_element_bytesonnxruntime::contrib::MurmurHash3::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccMurmurHash3keyspositiveonnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO 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: Expecting 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::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.ccSparseToDenseMatMulCurrently supporting only 2-D matricesCurrently support only COO and CSR(x64) formatsWordConvEmbeddingonnxruntime::contrib::WordConvEmbedding::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cc Conv kernal size 2 : Char embedding size does not match conv kernal size 2. char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute . Char embedding size: conv kernal size 1: conv_window_size attribute: embedding_size attribute: Conv filter size does not match embedding_size attribute. conv filter size: conv_window_sizechar_embedding_sizeembedding_sizeseparatorstokenexpattribute mincharnum must have a positive valuemincharnum > 0mincharnumattribute mincharnum is not setpad_valueattribute pad_value is not setonnxruntime::contrib::Tokenizer::TokenizerD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccmarkattribute mark is not setTokenizertensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedMatch contains invalid utf8 chars: onnxruntime::contrib::Tokenizer::TokenExpressionInput string contains invalid utf8 chars:onnxruntime::contrib::Tokenizer::SeparatorExpressionTokenizerCan not digest tokenexp: Input string contains invalid utf8 chars: !char_tokenezation_ || mincharnum_ < 2Can not digest separators: !separators.empty()mincharnum is too big for char level tokenezation!tokenexp.empty()separators must not be emptyEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexp}, actural: D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTMstatic_cast(activation_func_names.size()) == num_directions_ * 3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention v weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{}, Got: Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, ], while found!Attention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplinit_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_init_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::SetupSubgraphExecutionInfogpt_subgraph_ == nullptrinfo.GetAttr("decoder", &proto).IsOK()decoderinit_decoderinfo.GetAttr("encoder", &proto).IsOK()encoderD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.cconnxruntime::contrib::transformers::Sampling::Initparameters_.model_type == IGenerationParameters::kModelTypeGptSampling is requiredNode input should be a scalar. Got shape of 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_sizeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsmin_lengthmax_lengthD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.honnxruntime::contrib::transformers::GreedySearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.honnxruntime::contrib::transformers::GreedySearchGpt::ExecuteInput '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 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got onnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchGpt::ExecuteRotaryEmbeddingMInput 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got Input 'cos_cache' dimension 0 should be same as max_sequence_length, got Input 'position_ids' dimension 1 should be of size sequence_length, got Input 'position_ids' dimension 0 should be of size batch_size, got rotary_embedding_dim must be less than or equal to head_sizenum_heads must be provided if rotary_embedding_dim is specifiedInputs 'cos_cache' and 'sin_cache' are expected to have Updating cos_cache and sin_cache in RotaryEmbedding is not currently supportedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\rotary_embedding.cconnxruntime::contrib::RotaryEmbedding::Computethe same shapeInput 'sin_cache' is expected to have 2 dimensions, got Input 'cos_cache' is expected to have 2 dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got Input 'x' is expected to have 3 or 4 dimensions, got onnxruntime::contrib::RotaryEmbedding::RotaryEmbeddingnum_heads > 0num_heads must be provided if rotary_embedding_dim is specifiedis_packed_batchinginterleavedrotary_embedding_dimhead_size must be a multiple of 8. Got head_size = Input 'query' is expected to have 3 dimensions, got info.GetAttr("sparse_block_size", &sparse_block_size).IsOK()sparse_block_sizerotary_interleaveddo_rotaryinfo.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0kv_num_headsD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_base.honnxruntime::contrib::SparseAttentionBase::SparseAttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0SparseAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\sparse\sparse_attention.cconnxruntime::contrib::SparseAttention::ComputeInput 'past_key' and 'past_value' shall have same shapeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/sparse/sparse_attention_helper.honnxruntime::contrib::sparse_attention_helper::CheckInputspast_key != nullptr && past_value != nullptrmax_sequence_length should be no less than total_sequence_length:, max_sequence_length deduced from block_row_indices:block_col_indices must have shape (num_layout, max_nnz), where max_nnz <= max_blocks * max_blocks.block_row_indices must have shape (num_layout, max_blocks + 1) where num_heads is divisible by num_layout.total_sequence_length tensor must be of one element.packed qkv hidden size= does not match num_heads and kv_num_headsInput 'query' and 'value' shall have same shapeInput 'query' and 'key' shall have same dim 1 (sequence length)Input 'query' and 'key' shall have same dim 0 (batch size)Input 'key' is expected to have 3 dimensions, got Input 'key' and 'value' shall be both present, or both absent in the case of packed qkv.cos_cache and sin_cache dimension 1 must be the same.sin_cache dimension 1 must be <= head_size / 2 and a multiple of 8.cos_cache dimension 1 must be <= head_size / 2 and a multiple of 8.max_rotary_sequence_length should be no less than total_sequence_length:, max_rotary_sequence_length:cos_cache and sin_cache dimension 0 should be same size.head_size shall be a multiple of 16. Got head_size = cos_cache and sin_cache must be passed to SparseAttention when do_rotary = 1key_total_sequence_lengths must have shape (batch_size).Input 'past_key' dimension 3 should be same as head_size, got max_cache_sequence_length should be no less than total_sequence_length:, max_cache_sequence_length:Input 'past_key' shall have kv_num_headsInput 'past_key' dimension 0 should be batch_size Input 'past_key' is expected to have 4 dimensions, got past_key->DataRaw() == present_key->DataRaw() && past_value->DataRaw() == present_value->DataRaw()GroupQueryAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\group_query_attention.cconnxruntime::contrib::GroupQueryAttention::ComputeInput 'query' and 'value' shall have same dim 0 (batch size)Input 'value' is expected to have 3 dimensions, got head_size must be a multiple of 8. Got head_size % 8 == num_heads must be a multiple of kv_num_heads. Got num_heads % kv_num_heads == local_window_sizesmooth_softmaxD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\gqa_attention_base.honnxruntime::contrib::GQAAttentionBase::GQAAttentionBaseInput 'cos_cache' and 'sin_cache' shall be both present or both absent.sin_cache dimension 0 should be not be less than total_sequence_length.cos_cache dimension 0 should be not be less than total_sequence_length.head_size shall be a multiple of 16. Got head_size % 16 == seqlens_k must be shape (batch_size).Input 'past_key' and 'past_value' shall be both present or both absent.Input 'past_value' dimension 3 should be same as head_size, got Input 'past_value' shall have kv_num_headsBNSH Input 'past_key' and 'past_value' should have same dimension 2 (max sequencelength or past sequence length), 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 'value' is expected to have same hidden size as key.Input 'query' and 'value' shall have same dim 1 (sequence length)past_present_share_bufferqkv_hidden_sizesD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/bert/attention_base.honnxruntime::contrib::AttentionBase::AttentionBaseMultiHeadAttentionTrueFalseORT_DISABLE_FLASH_ATTENTIONInput 'key' and 'value' shall have same shape (batch_size, num_heads, kv_sequence_length, head_size)Input 'key' and 'value' shall have same dim 1 (kv_sequence_length)Input 'query' and 'key' shall have same dim 2 (hidden_size)Input 'query', 'key' and 'value' shall have same dim 0 (batch_size)Expect rank of key and value be same, and either 3 or 4Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect rank of key be 5 for packed kvExpect rank of query be 3 for packed kvD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionCPUBase::ApplyAttentionInput 'key_padding_mask' shape is not expected.Input 'bias' length is expected to be 2 * hidden_size + hidden_size_v, got Input 'bias' is expected to have 1 dimension, got Input 'bias' shall be empty when packed kv is usedPacked qkv of 3D BS3NH format is not support by MultiHeadAttentionInput 'past_key' and 'past_value' shall be both present or both absentonnxruntime::ParseEnvironmentVariableTryParseStringWithClassicLocale(value_str, parsed_value)Failed to parse environment variable - name: "", value: "4D mask in attention cpu kernel is not supportedInput '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 Input 'relative_position_bias' dimension 0 should be batch_size or 1, got Input 'relative_position_bias' is expected to have 4 dimensions, got past_sequence_length tensor must be of one element when past_present_share_buffer is setInput '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 'past_key' dimension 1 should be same as number of heads, got Expect 'query' shape (batch_size, sequence_length, num_heads, 3, head_size) for packed qkvInput 'value' shall absent when 'key' is absentD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention_helper.honnxruntime::contrib::multihead_attention_helper::CheckInputsInput 'query' is expected to have 3 or 5 dimensions, got onnxruntime::contrib::MultiHeadAttention::MultiHeadAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.cconnxruntime::contrib::MultiHeadAttention::ComputePacked KV not implemented for CPUPacked QKV of shape (B, L, N, 3, H) not implemented for CPUD:\a\_work\1\s\onnxruntime\core/platform/env_var_utils.hGreedySearchonnxruntime::contrib::transformers::GreedySearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.cconnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is ].Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_decoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::ComputeSubgraph SessionState was not found for 'encoder' attribute.encoder_session_stateencoder_feeds_fetches_manager_Model type is not supported.decoder_output_cross_qkBeamSearchWhisperBeamSearchparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperonnxruntime::contrib::transformers::BeamSearch::InitD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfot5_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_ == nullptrwhisper_encoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearchWhisper::Executeonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::CheckInputsparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchT5::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Execute, vocab_size is no_speech_token_id is out of range, it is parameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizeonnxruntime::contrib::transformers::BeamSearchWhisper::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_whisper.hdecoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiondecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with past_present_share_bufferdecoder_subgraph_.past_present_share_buffer_Must specify input cross_qk_layer_headinput_tensor_cross_qk_layer_head != nullptronnxruntime::contrib::transformers::BeamSearchGpt::ExecuteD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchBase::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.hnum_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::InitializeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::Attention::ComputeRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding.SampleOpTBTATYZTCTYD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.hD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.cconnxruntime::contrib::QGemm::Computeonnxruntime::contrib::QGemm::CheckInputsQGemm : scale of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point of input a must be a scalar or 1D tensor of size 1QGemm : zero point and scale of input b should have same shape sizeQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of y must be null or a scalar or 1D tensor of size 1QGemm : zero point of y must be null or a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1onnxruntime::contrib::QEmbedLayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.ccBeta zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationonnxruntime::contrib::NhwcMaxPool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validonnxruntime::contrib::MatMulIntegerToFloat::ComputeMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)Input W_zero_pointR_zero_point} for per-tensor/layer quantization or shape { must have shape {Weight zero point must be zeroDynamicQuantizeLSTM : W_scaleR_scaleWeight point must be constantRecurrent} for per-channel quantization. Actual:, 4*onnxruntime::contrib::DynamicQuantizeLSTM::PrePackD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cconnxruntime::contrib::QAttention::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1.MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)QLinearMulQLinearAddMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)onnxruntime::contrib::`anonymous-namespace'::QLinearImplD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.ccMatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)opset must be existed in attributes of QlinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxonnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.cconnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13QLinearSoftmaxQLinearSigmoidQLinearLeakyReluInput x_scale must be a scalar or 1D tensor of size 1input x_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.cconnxruntime::contrib::QLinearAveragePool::ComputeImplIsScalarOr1ElementVector(tensor_x_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)input y_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinear Pooling unsupported pooling size!tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearWhereinput_def_count == kExpected_input_countThere must be inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Wrong input type encountered for zero point input def of x, y, zD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.cconnxruntime::contrib::QLinearWhere::QLinearWhereInput scale is not float for quantized input y @ 5onnxruntime::contrib::QLinearWhere::Computetensor_x_scale->IsDataType()Input scale is not float for quantized input x @ 2Wrong input type encountered for zero point of quantized input @tensor_z_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_y_scale->IsDataType() QLinearConcatinput_def_count >= 5 && (input_def_count - 2) % 3 == 0Each input must be (tensor, scale, zero_point) tuple!Wrong input type encountered for zero point input def @Input scale is not float for input def @D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcatInput scale is not float for quantized input @onnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 3 && input_count_x3 % 3 == 0tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()onnxruntime::contrib::QLinearGlobalAveragePool::ComputeIsScalarOr1ElementVector(tensor_y_zero_point)IsScalarOr1ElementVector(tensor_x_zero_point)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.cconnxruntime::contrib::MatMulInteger16::ComputeA != nullptr && B != nullptrMatMulInteger16onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Compute(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countchannels_ > 0' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::ml::GetVectorAttrsOrDefaultUnexpected type (proto.data_type() == proto_type' has one dimension but is empty.n_elements > 0' is specified but is empty.onnxruntime::ml::GetNumberOfElementsAttrsOrDefaultD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cc'.(for attribute 'Attribute:'' must be a vector.Attribute 'n_dims == 1Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 row[.. rows[ [seqno=onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps not support normalize yet.!normalize_mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemoryprefix.size() == narrow(prefix_length)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.cconnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processseed >= 0Seed must be >= 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.cconnxruntime::contrib::transformers::SamplingParameters::ParseFromInputseos_token_idmodel_typedecoder_start_token_idpad_token_idpresence_penaltymin_tokens_to_keepvocab_sizecustomtemperatureno_repeat_ngram_sizefilter_valuetop_prepetition_penalty > 0.0fcontext != nullptrD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.cconnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs) shall be no more than max_length > sequence_lengthrepetition_penalty shall be greater than 0, got max_length <= kMaxSequenceLengthdims.size() == 2input_ids shall have 2 dimensions. Got max_length () shall be greater than input sequence length (@beginning_timestamp_token_idno_timestamps_token_idtranscribe_token_idtranslate_token_idno_speech_token_idstart_of_lm_token_idnum_beams shall be a positive integer no more than extra_decoder_tensor_dims[0] == batch_sizenum_return_sequences shall be a positive integer, got num_beams >= 1 && num_beams <= kMaxNumBeamsextra_decoder_tensor_dims.size() == 2extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor first dim not same as batch_size. Got , expecting logits_processor >= 0logits_processor shall be a non-negative integer, got onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributesmodel_type == IGenerationParameters::kModelTypeWhisper) shall be be no more than num_beams (num_return_sequences >= 1num_beams >= num_return_sequencesnum_return_sequences (D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccdims.size() == 3input_features shall have 3 dimensions. Got decoder_dims.size() == 2decoder_input_ids shall have 2 dimensions. Got onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsearly_stoppingsubgraph output 0 (logits) shall be float or float16 data typesubgraph input 2 (attention_mask) shall have int32 typesubgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph past state dimension 0 shall have length of 2subgraph past state is expected to have 5 dimension, got subgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph past state dimension 2 shall have a positive value for number of headspast_0subgraph input 2 shall be named as attention_mask, got: subgraph past state cannot be nullptrsubgraph input 3 shall be named as past_0, got: subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph logits output is expected to have 3 dimension, got subgraph input 1 (position_ids) shall have int32 typesubgraph input 0 (input_ids) shall have int32 typesubgraph output 0 shall be named as logits, got: logitssubgraph input 1 shall be named as present_0, got: present_0onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedssession_state_ != nullptrinput_ids_shape.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.ccSetup must be called before CreateInitialFeedsposition_idssubgraph input 0 shall be named as input_ids, got: attention_masksubgraph input 1 shall be named as position_ids, got: onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).input_idsInvalid 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)CPU beam search current not support output cross QK.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsBeamSearch op: An implementation for the input type onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsstart_token_id >= 0original_decoder_input_ids_shape.NumDimensions() == 2input_features_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferis_kv_cacheonnxruntime::contrib::SamplingCpuHelper::SampleD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.hlast_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedsonnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputslogits_shape.NumDimensions() == 3element_type == DataTypeImpl::GetType()onnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AddBiasReshapeonnxruntime::contrib::MaybeTransposeToBNSHonnxruntime::contrib::MaybeTransposeToBNSHAndAddBiasD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_utils.ccD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.ccExpect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentInputs '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 Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Input 'past' expect k_hidden_size == v_hidden_size k_hidden_size= q_hidden_size=Inputs '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 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 '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 'past' dimension 2 shall have length of Input 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Attention cannot have both past and relative_position_biasbias_dims[0]= v_hidden_size=qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameqkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads: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'DecoderMaskedSelfAttentionsubgraph output present_key_self_0 is expected to have 4 dimension, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.cconnxruntime::contrib::transformers::Subgraph::GetParametersbeam_widthonnxruntime::contrib::transformers::Subgraph::SetupDecoderMaskedMultiHeadAttentionpast_sequence_lengthunimplemented activation: activation_params count mismatchbeta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got Input 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeonnxruntime::contrib::transformers::BeamHypotheses::Outputtop_k <= beams_used_output_scores->IsDataType()onnxruntime::contrib::transformers::BeamSearchScorer::OutputScorestarget.size() == final_scores.size()output_sequence_scores->IsDataType()onnxruntime::contrib::transformers::BeamSearchScorer::Finalizeoutput_sequences != nullptrstatic_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()beam_idx == num_beams_beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)Batch can only be done if all beams have been generatednext_scores.size() == next_indices.size()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()decoder_masked_attention shall use with past_present_share_bufferD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.cconnxruntime::contrib::transformers::T5DecoderSubgraph::Validateencoder_hidden_statesdecoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder_attention_maskdecoder subgraph input 0 shall be named as input_ids, got: number of outputs expected to be 1 + 2 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers, got:Invalid hidden_states expension: has_hidden_state_ == falseonnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesdecoder 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 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph output 0 shall be named as logits, got: onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeedsdecoder subgraph past inputs shall have same data type as that of encoder_hidden_states.decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typedecoder subgraph input 1 shall be named as encoder_hidden_states, got: When outputing cross qk, number of outputs expected to be first_present_output_index_ + 3 * layers, got:number of outputs expected to be first_present_output_index_ + 2 * layers, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_decoder.cconnxruntime::contrib::transformers::WhisperDecoderSubgraph::Validatecpu_allocator shouldn't be nullptronnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeedsencoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0number of outputs expected to be 2 + 4 * layers, got:expect >=6 outputs, got:D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.cconnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validateexpect 2 inputs, got:encoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0encoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder_input_idsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.cconnxruntime::contrib::transformers::T5EncoderSubgraph::Validateexpect 3 inputs, got:onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 shall be named as encoder_attention_mask, got: QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableOrtTypeInfo::FromOrtValuetensor_data_type != nullptrOrtValue is TensorSequence type but has no element Tensor DataType.The type is not tensor, sparse tensor, sequence, map or optional typeOrtTypeInfo::FromTypeProtoThis TypeProto does not have ValueCase setThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeTensor types should have been handled alreadyD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccCudaCudaPinnedHipHipPinnedOpenVINO_GPUWebGPU_Buffer alignment= size= nmemb=onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccSpecified device is not supported.Tensor type is undefinedthe ort_value must contain a constructed tensor or sparse tensorArgument is not a tensorOrtApis::GetTensorTypeAndShapeD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccUnsupported indices_format passed`anonymous-namespace'::GetIndicesTensortype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeOrtSequenceTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.cctype_proto is not of optional typevalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypeOrtOptionalTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypeOrtMapTypeInfo::FromTypeProtoD:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.ccTensor shape.Size() must be >= 0onnxruntime::Tensor::CalculateTensorStorageSizeD:\a\_work\1\s\onnxruntime\core\framework\tensor.ccTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrCalculation for Tensor storage size overflowedelt_type != nullptronnxruntime::Tensor::Tensortensor size overflowonnxruntime::Tensor::SizeInBytesshape.Size() must >=0onnxruntime::Tensor::Initonnxruntime::IDataTransfer::CopySparseTensorssrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.ccD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.helem_proto != nullptronnxruntime::data_types_internal::SequenceTypeHelper::Setonnxruntime::data_types_internal::OptionalTypeHelper::Setonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::OptionalTypeBase::GetElementTypeexpected a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::MapTypeHelper::Setonnxruntime::data_types_internal::IsCompatibleonnxruntime::SequenceTensorTypeBase::GetElementTypesparse tensor type is not currently registered or supportedMLDataType for: Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerFloat8E5M2Float8E5M2FNUZInt4x2UInt4x2(unknown type) is not supportedtensor type Not implementedonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainer(null)floatboolFloat8E4M3FNFloat8E4M3FNUZonnxruntime::OptionalTypeBase::IsCompatibleutils::HasElemType(thisProto->optional_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kOptionalTypeD:\a\_work\1\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: p.secondthisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleMust contain Csr format. Contains: allocator_ != nullptronnxruntime::SparseTensor::AsCsrFormat() == SparseFormat::kCsrcformat_data_.size() == 2UExpecting two indices. Got: onnxruntime::SparseTensor::ValidateCsrIndicesdense shape must 2-D. Got: Not expecting an allocator setonnxruntime::SparseTensor::UseCooIndicesonnxruntime::SparseTensor::MakeCooDataUse MakeCooStringsonnxruntime::SparseTensor::MakeCooStringsExpecting data type to be set as stringThis method should follow a call to constructor that supplies the allocatorFormat() == SparseFormat::kUndefinedExpecting to contain one index, got: onnxruntime::SparseTensor::AsCoo must be equal to or twice the values size: format_data_.size() == 1Uvalues_count == index_sizeIndex size: Sparse format must not be set. Already contains format: onnxruntime::SparseTensor::GetCooIndexDimsonnxruntime::SparseTensor::GetSparseTensorFromOrtValuethe ort_value must contain a constructed sparse tensor must be less than total buffer size: this tensor already has populated sparse_indicesonnxruntime::SparseTensor::AllocateBufferValues size Format() == SparseFormat::kCooMust contain Coo format. Got: Must have the same size. Got src_size: dst_size: D:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cconnxruntime::`anonymous-namespace'::CopyDataSrc and Dst must be of the same typeX-device copy of strings not supportedMust have the same shapeonnxruntime::SparseTensor::MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataUnable to find a data transfer for copying from device type: to device type: This instance should not be emptyonnxruntime::SparseTensor::CopyDestination must have a CPU allocator setDestination should be emptyIndices shape must have dim[0] == 2Expecting indices to have 2-D shape . Got: Expecting index blocks: to be equal to values blocks: Expecting fully sparse tensors to have indices shape {0}Expecting fully sparse tensors to have value shape {0}Use MakeBlockSparseStringsonnxruntime::SparseTensor::UseBlockSparseIndicesonnxruntime::SparseTensor::MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataFormat() == SparseFormat::kBlockSparseMust contain BlockSparse format. Got: Expecting one index. Got: onnxruntime::SparseTensor::AsBlockSparseonnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got: the same as values size: Inner and Outer indices must either be both zero or non-zero rows: Expecting inner index size: This method does not expect allocator to be setOuter index count must be rows + 1 or zero. Got: Use MakeCsrStringsonnxruntime::SparseTensor::UseCsrIndicesonnxruntime::TensorShape::SizeFromDimension for SizeFromDimension. Tensor has dimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.{onnxruntime::TensorShape::Slice} for SizeToDimension. Tensor has dimensions.dimension <= num_dimsInvalid dimension of D:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimension] already exists with value []. It will be overwrittenonnxruntime::ConfigOptions::AddConfigEntryConfig with key [ D:\a\_work\1\s\onnxruntime\core\framework\config_options.ccConfig value is longer than maximum length: Config key is empty or longer than maximum length 128XIIIIAn entry for this name has already been added: onnxruntime::SessionOptions::AddExternalInitializersFromFilesInMemoryinit_num == values.size()Expecting same size spans.//onnxruntime::SessionOptions::AddExternalInitializers.\\./num_files == files_buffers.size().\Received nullptr for nameReceived OrtValue is not a tensor. Only tensors are supported.Received nullptr for OrtValueonnxruntime::SessionOptions::AddInitializerBuffer containing the initializer must be owned by the user.An OrtValue for this name has already been added: D:\a\_work\1\s\onnxruntime\core\framework\session_options.ccNo attribute with this name is defined.Attribute name and type don't match is expected to have type: Requested attribute: No attribute with name: is defined. expected to be of type: but is of type: Attribute: onnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_D:\a\_work\1\s\onnxruntime\core/framework/node_index_info.hTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsonnxruntime::OpKernelContext::GetOrCreateOutputMLValueCPU allocator not foundD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextkernel != nullptrOpKernel was null. Num args is onnxruntime::OpKernelContext::OutputMLValuearg_num < arg_counts.size()Invalid arg_num of frame != nullptrExecution frame was null", ": "file_size type: */_ORT_MEM_ADDR_/*TensorProto type onnxruntime::utils::TensorProtoToTensorTensorProtoToTensor() tensor shape mismatch! can not be written into Tensor type Initialized tensor with unexpected type: Missing raw data for tensor proto. Invalid tensor.string tensor can not have raw datatensor can't contain negative dimsonnxruntime::utils::GetExtDataFromTensorProto are out of bounds or can not be read in full.onnxruntime::utils::GetFileContentutils::HasExternalData(tensor_proto) offset: External initializer: given file_length: size to read: ) does not match the data size(corrupted protobuf data: tensor shape size(External data type cannot be STRING.) in protoUnpackTensor: the pre-allocated size does not match the size in protoonnxruntime::utils::UnpackTensorUnpackTensor: the pre-allocate size does not match the size in protodata overflowstring tensor requires allocator to be provided., Got onnxruntime::`anonymous-namespace'::ReadExternalDataForTensoronnxruntime::utils::UnpackTensorWithExternalDataImplonnxruntime::`anonymous-namespace'::TensorProtoToOrtValueImplTensor does not have external data to read from.onnxruntime::`anonymous-namespace'::GetExternalDataInfoD:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccUnexpected number of packed int4 pairs external data size mismatch. Computed size: TensorProto: External data type cannot be UNDEFINED or STRING., external_data.length: nullptr == p_dataonnxruntime::`anonymous-namespace'::UnpackTensorWithRawDataUnpackTensor: the pre-allocated size does not match the raw data size, expected Invalid TensorProtoHasDataType(dense_proto)onnxruntime::utils::DenseTensorToSparseTensorProtoUnsupported sparse tensor data type of Must have a valid data typeUnsupported type: data_type: onnxruntime::utils::UnpackInitializerDataindices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid 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 int64 is not supported.Element_size of: Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::SparseTensorProtoToDenseTensorProtonode.output_size() == 1Sparse Indices raw data size does not match expected.Unsupported attribute value type of NodeProto for Constant should have 1 output. Got:Sparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensoronnxruntime::utils::CopySparseDataSparse indices int64 data size does not match expectedlocationlengthraw_dataonnxruntime::utils::TensorToTensorProtoonnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node ' has no data attributesConstant node: D:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0onnxruntime::KernelDefBuilder::VariadicAlias Encountered following errors: ( kernel is not supported in in the supported version rangeKernel found kernelOp with name (Kernel not found and type ( domain ( kernel_end_version: kernel start version: static_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptr However the types are incompatible. (node_version: node_version: Version mismatch.This op has been implemented only for the following types (onnxruntime::`anonymous-namespace'::MatchKernelDefTypesD:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cc), but the node in the model has the following type (kernel def can't be NULL: Conflicting with a registered kernel with op versions. However the types are incompatible.Op type (Failed to add kernel for ORT optimization- Force fallback to CPU execution for node: D:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cckernel_info != nullptrCandidate for fallback CPU execution: onnxruntime::GetCpuPreferredNodes::::operator () capable of executing this node because the CPU execution path is deemed faster than overhead involved with execution on other EPs onnxruntime::GetCpuPreferredNodesfloat8e5m2fnuzIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by onnxruntime::CreateAllocatorReceived invalid value of arena_extend_strategy D:\a\_work\1\s\onnxruntime\core\framework\allocator_utils.cconnxruntime::sparse_utils::SparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyInput must be of COO formatouter_num == (rows + 1)onnxruntime::sparse_utils::DenseTensorToSparseCooInvalid index: > dense_size: Unable to convert strings tensor to a sparse tensor that is not on CPUInput must be of CSR formatSupport 2-D matrices onlyUnsupported element size: Outer indices must be M + 1. Got: onnxruntime::sparse_utils::SparseCsrToDenseTensorinner_num == src.Values().Shape().Size()Expecting inner indices to be same as nnz. Got: D:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.cconnxruntime::sparse_utils::DenseTensorToSparseCsrUnable to convert strings tensor to a sparse tensor that not on CPUCurrently do not support dims higher than 2 dimensions: ] is not supportedonnxruntime::NodeUnit::NodeUnitQDQ node group cannot have an output from the target node that is consumed by a Q node and a graph output. target node: output idx:onnxruntime::NodeUnit::InitForSingleNodeThe QLinear op [QDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: QLinearReduceMeanQDQ node group cannot have an output from the target node being consumed by a Q node and a non-Q node. target node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: D:\a\_work\1\s\onnxruntime\core\framework\node_unit.cconnxruntime::QDQ::NodeGroup::CanCreateNodeGroup to Tensor size mismatchdata_transfer registered is nullptr.onnxruntime::DataTransferManager::CopySparseTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopyTensorsThere's no data transfer registered for copying tensors from InternalTestingExecutionProviderSNPEExecutionProvideronnxruntime::utils::UpdateWithParentStreamSubgraph has nodes running on device: while parent graph node running on device: , this is not supported yet.onnxruntime::utils::ExecuteGraphonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::FinalizeCopyInfoForFeedsfeed_locations.size() == copy_info.size()onnxruntime::utils::InitializeFeedFetchCopyInfoonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::CopyInputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::FinalizeCopyInfoForFetchesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FindDeviceForValueUnsupported OrtValue type to copy between device.D:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::BatchOrCopyMLValuenode_info_vec[i].stream_index < 0node_info.stream_index < 0onnxruntime::utils::CalculateStaticCopyInfoForFeedexec_plan_ptrFailed to find allocator for device Unsupported OrtValue type.invalid allocator.', but the existing argument with that formal parameter name has a different formal parameter type string.Failed to find args for kernel type string '' for node type ''. Op schema must be available.onnxruntime::KernelTypeStrResolver::RegisterOpSchemaonnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()Kernel type string already exists for formal parameter name 'Failed to find op_id: Invalid ORT format model.If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.D:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.cconnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrDuplicate entry for kernel type str: args entry is null.args is null.kernel_type_str is null.onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStrDuplicate entry for op id: op_kernel_type_str_args is null.onnxruntime::KernelTypeStrResolver::SaveToOrtFormatonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasonnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemakernel_type_str_args entry is null.kernel_type_str_args is null.op_id is null.op_kernel_type_str_args entry is null.Hp\Ponnxruntime::ExLibLoader::LoadExternalLibonnxruntime::ExLibLoader::~ExLibLoaderUnloading DSO D:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.cconnxruntime::ExLibLoader::{dtor}::::operator ()A dso with name has already been loaded.Caught exception while loading custom ops with message: Failed to unload DSO: Caught exception while destructing CustomOpsLoader with message: failed at retrieving the removable attributesfor node '' (''). removable attributes '), among attributes: onnxruntime::SessionState::PruneRemovableAttributesDone saving OrtValue mappings.D:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateKernelsonnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfosession.disable_prepackingsession.node_partition_config_filep_op_kernelentry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType:onnxruntime::AccumulateAllNestedSubgraphsInfosubgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulatoronnxruntime::OuterScopeNodeArgLocationAccumulator::::operator () Index: Attribute:onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStateImpl::::operator () Node(s) placed on [ All nodes placed on []. Number of nodes: Node placementsonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::SessionState::FinalizeSessionStateRerunning with verbose output on a non-minimal build will show node assignments.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.onnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::SessionState::GetNodeIndexInfoonnxruntime::VerifyEachNodeIsAssignedToAnEponnxruntime::VerifyEachNodeIsAssignedToAnEpImplCould not find an implementation for ) node with name 'Entry exists in node for attribute onnxruntime::SessionState::AddOutputNameToNodeInfoMappingnode_index_info_.has_value()SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend() is used by node ) and node (Unable to write the provided PrePackedWeights instance into the containeroutput_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input:The op type of a node cannot be emptyweights_to_be_filled_in.buffers_.size() > 0 doesn't have an implementation that can cache computed pre-packed weightsonnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()Using cached version of pre-packed weight for constant initializer: used in the node: which is of op type: !op_type.empty()onnxruntime::KernelUseSharedPrePackedBuffersduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?removed allocator_for_caching.get() != nullptr+The kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsonnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamstream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::GetStreamonnxruntime::DeviceStreamCollectionImpl::CleanUpD:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.ccidx < num_streams_bad variant access in KernelRegistryManagerfound duplicated provider '). ' ep:' (node:'The node is not placed on any Execution Provider. Failed to find kernel for onnxruntime::utils::GetMLDataTypeD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccnullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.onnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolverktsrError mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: existsonnxruntime::InlineNodes did not return correct number of compiled functions_ctx.onnxonnxruntime::InlineFunctionsAOTImplNode ' with domain:The Function Style fusion is deprecated.onnxruntime::PartitionOnnxFormatModelImpl by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP.D:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc was inserted using the NHWC format as requested by , but was not selectedonnxruntime::GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.D:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.honnxruntime::KernelLookup::KernelLookupAPI VERSION Create state function failed. Return value:!provider_type_.empty()provider_type must be specified.D:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::CreateNo provider specified.AOT inlining completed. (onnxruntime::GraphPartitioner::Partitiononnxruntime::GraphPartitioner::InlineFunctionsAOTThis model does not have any local functions defined. AOT Inlining is not performed) functions of () pruned.onnxruntime::PartitionOrtFormatModelImplonnxruntime::PartitionOnnxFormatModelonnxruntime::PartitionOrtFormatModelsingle_node_compute_func should have 1 element.' exist already.Both ep_context_path and model_path are emptyonnxruntime::CreateEpContextModelFailed to generate EP context model since the file '@ܑonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizep_int >= base_intD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::Shrink BFC Arena shrunk by !c1->in_use() && !c2->in_use() && c1->stream == c2->streamonnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum) bytes. The total allocated bytes is now onnxruntime::BFCArena::FindChunkPtr!chunk->in_use(). bin_num: (requested) num_bytes: Failed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for tried to allocate 0 bytes (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternalreserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserveh != kInvalidChunkHandleReserving memory in BFCArena for size: onnxruntime::BFCArena::ExtendExtended allocation by Allocated memory at Total allocated bytes: Available memory of is smaller than requested bytes of bytes.Failed to allocate memory for requested buffer of size onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)onnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()Creating bins of max chunk size BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)Creating BFCArena for with following configs: initial_chunk_size_bytes: D:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArena max_power_of_two_extend_bytes: memory limit: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: Could not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionForp_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForentry != regions_.end()Could not find Region for: . onnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::Coalesce!c->in_use()onnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBinBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binc2->prev == h1onnxruntime::BFCArena::Merge!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinonnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here. for output Invalid allocation kind: onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()!is_strided_tensoralloc && alloc.get() != nullptr. Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer, block in memory pattern size is: but the actual size is: Failed to get allocator for For ort_value with index: Trying to allocate memory for unused optional inputs/outputs returned nullptr, fall back to default allocation behavioronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperonnxruntime::ExecutionFrame::{ctor}::::operator ()Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end() entries which doesn't match the number of fetches the frame was initialized with of fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() failed. Error:Fetches vector passed to GetOutputs contains Get feed size: but expected feed size: onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeinvalid index shape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()OrtValue shape verification failed. Current shape: Requested shape:onnxruntime::AsStreamBasedAllocatorallocator.get() != nullptrnode_index_info and ort_value_idx_map are out of sync and cannot be usedD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.ccallocator is nullptrCould not find OrtValue with idx 'D:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueMemory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceFreeort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()Expected shape from model of does not match actual shape of onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::VerifyOutputSizesonnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\node_index_info.ccD:\a\_work\1\s\onnxruntime\core\framework\transpose_helper.cconnxruntime::TransposeSingleAxisOutwardsonnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeInvalid dim0_offset of onnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::IteratorInsufficient dimensions to slice on . Shape:. Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.!prim_type->HasSubElems()Can't slice a tensor with a subbyte element typeort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Createonnxruntime::LibraryHandles::UnloadLibrariesD:\a\_work\1\s\onnxruntime\core\framework\library_handles.cconnxruntime::utils::detail::CopyLittleEndianD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccsource and destination buffer size mismatchmodel format error! Missing 'location'model format error! failedparsing model format error! Need a key for the external data infomodel format error! Need a value for the external data infoNon-zero status code returned while running onnxruntime::ExecuteThePlan' Status Message: node. Name:'onnxruntime::ExecuteKernelD:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cc_fence_afterAsync Kernel Support is not implemented yet.thread_scheduling_stats_kernel_timeinput_type_shapeoutput_type_shapeparameter_sizeoutput_sizenode_indexactivation_size_fence_beforeSequentialExecutor::Executeop_name":[{"D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.h]}entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternal does not.All implicit inputs should have OrtValue instances by now. 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 () as input which has external outputs is reused. Be noted input buffer onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator ()onnxruntime::PlannerImpl::GetAliasMapspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()KernelDef is null for node: kernel_create_info.kernel_def != nullptrCan not find the execution provider onnxruntime::PlannerImpl::GetLocationForNodeInputp_kernel_defonnxruntime::PlannerImpl::ComputeValueLocationonnxruntime::PlannerImpl::ComputeValueLocation::::operator ()Should not have entry in kernel create info with nullptr for kernel_defThere is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_mapCan not find the node onnxruntime::PlannerImpl::ComputeReuseCountnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSize as it has external outputs cannot reuse input buffer for node Be noted Node onnxruntime::PlannerImpl::FindReusableInput which has external outputs. is reusing input buffer of node onnxruntime::PlannerImpl::ReuseBe cautious the reuse MUST be a read-only usage.onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCount against size invalid value index: D:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cconnxruntime::PlannerImpl::Indexentry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: : "bytes": [], [][ , ":{ ": {}cannot use insert() with Use DeviceBasedPartition as defaultCaught exception when reading partition config file: onnxruntime::IGraphPartitioner::CreateGraphPartitionerCaught exception during saving DeviceBasedPartitioner config: onnxruntime::DeviceBasedPartitioner::SaveConfigNumber of streams does not equal to number of device types!node_names_by_stream_.size() == device_types_.size()streamsdevicesPartitioner type is not DeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::Initializeiter != node_stream_map.end()onnxruntime::DeviceBasedPartitioner::PartitionGraph" in node-stream mapFailed to find node "onnxruntime::PlannerImpl::CreatePlanDeviceBasedPartitionerexecution_plan[plan_.node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()onnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::BuildExecutionPlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::GenerateDeallocationPlanStrided tensor is not supported in non-training build for now.!IsNonTensor(*node_output)onnxruntime::PlannerImpl::ComputeSingleStreamReusePlanonnxruntime::PlannerImpl::ComputeReusePlanOnly tensors are supported for external outputs for now. reused by onnxruntime::PlannerImpl::OptimizeReusePlanForMultiStreamcannot use operator[] with a string argument with �cannot use operator[] with a numeric argument with incomplete UTF-8 string; last byte: 0x: 0xinvalid UTF-8 byte at index \u%04x],"subtype":null}"subtype": {"bytes":[D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()onnxruntime::PrePackedWeights::GetHashDeserialize tensor Done saving initialized tensorsUsing user supplied initializer with name ( failed.[Memory] SessionStateInitializer statically allocates Skipping entry for missing optional value at idx entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for entry != initialized_tensors_to_allocate.end()Can not trace string tensor from initializer_allocation_order not found among initialized tensorsOrtValue index: OrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsCannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator () ) is different from what is supplied () because the ORT planned memory location device string tensor is not supported for copying between allocatorsFailed to copy tensor to onnxruntime::session_state_utils::DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires DeserializeTensorProto() takes either pre-allocated buffer or an allocator!With buffered tensor, tensor proto must use external location and point to buffered tensorD:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::ExtDataTensorProtoToTensoronnxruntime::session_state_utils::AllocateBufferUsingDeviceAllocatorFromShapeAndTypesession.use_device_allocator_for_initializersGraph input with name Subgraphonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorGet preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::TraceWeight buffer for initializer '' failedMem pattern for initializer ' is not foundInternal error. is not foundonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanD:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hFailed to get allocator for location: duplicated locationonnxruntime::FuncManager::GetFuncsD:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.ccCan't use func with null ptr not found. already exist.func info for node: current <= buffer_size_D:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.honnxruntime::MemPatternPlanner::TraceAllocation!using_counters_D:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.honnxruntime::StreamExecutionContext::CountDownBarrier::Setv >= 0onnxruntime::RunSincesince == endExiting due to terminate flag being set to true.onnxruntime::StreamExecutionContext::RecycleNodeInputsframe_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()D:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.cconnxruntime::StreamExecutionContext::GetDeviceStreamidx < device_stream_map_->NumStreams()TriggerDownstream - TriggerPointIndex: ActivateNotification - NotificationId: onnxruntime::ActivateNotificationStep::Execute activate notification with index LaunchKernel - NodeIndex: , Name: WaitOnEP - NotificationId: stream wait on Notification with id: D:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cconnxruntime::WaitOnEPStep::Executewait_handle_WaitOnEPStep.wait_handle is nullBarrier - BarrierId: , Count: ConstantNoOpALLOW_RELEASED_ONNX_OPSET_ONLYD:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.honnxruntime::utils::GetShapeshape != nullptrTypeProto must have shape for this to run. Please, fix your model.Duplicate sparse_tensor_initializer: 'utils::HasName(sparse_tensor)Sparse initializer must have a name. This model is invalidDuplicate constant node sparse initializer name: '' Model is invalid.onnxruntime::Graph::Graphgraph_proto != nullptrgraph_proto cannot be nullThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (output edgesinput edgesonnxruntime::Node::LoadEdgesFromOrtFormatinput index: is not the same as this node's index:onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Node::LoadEdgesFromOrtFormat, edge is missing for Node::LoadFromOrtFormat, input_arg_counts is missingSerialization error. Graph attribute was serialized without Graph instancefbs_attr cannot be nullonnxruntime::Node::LoadFromOrtFormatLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg node_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()fbs_node_arg_names cannot be nullonnxruntime::Node::SaveToOrtFormatdoes not have the graph for key Removable attributes were removed before the node is saved.Serialization of fused function body is not currently supported, Node [] op_type [onnxruntime::Node::ToProtoRemovable attributes were removed before the conversion is started.Optional Type mismatch. Expected: . Got: onnxruntime::NodeArg::UpdateTypeAndShapeType mismatch. Current= Input=Tensor element type mismatch. D:\a\_work\1\s\onnxruntime\core\graph\graph.cconnxruntime::MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge. , or sparse tensors , or optional typed entitiesSource and target must both be tensorsOutput: in node (This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.This is an invalid model. Node () input arg () does not have type information set by parent node.Node () Op () onnxruntime::Graph::UpdateShapeInferencenode.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::Graph::InferAndVerifySubgraphTypesSubgraph input missing type.Node:Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.No Graph instance was found for attribute in node Graph attribute inferencing failed: This is an invalid model. Error: the graph is not acyclic.onnxruntime::Graph::KahnsTopologicalSortSome nodes are not included in the topological sort, graph have a cycle.Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::BuildConnectionsArgument mismatch when removing edge.Invalid destination node arg slot specified when removing edge.Invalid source node arg slot specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid node indexes specified when removing edge.Argument type mismatch when adding edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.onnxruntime::Graph::AddEdgeInvalid node indexes specified when adding edge.This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: two nodes with same node name (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.This is an invalid model. Graph output () does not exist in the graph.node_argGraph ctor should have created NodeArg for initializer. Missing:onnxruntime::Graph::InitializeStateFromModelFileGraphProtograph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.This is an invalid model. Tensor does not have type information.Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializerFailed to convert dense initializer to sparseonnxruntime::Graph::RemoveNodenode->GetOutputEdgesCount() == 0Can't remove node as it still has output edges._token_onnxruntime::Graph::SaveToOrtFormatExternal file: not found from the table user provided.onnxruntime::Graph::InjectExternalInitializersFromFilesInMemoryReplaced external initializer: onnxruntime::Graph::InjectExternalInitializedTensorsexisting_entry != mutable_initializers.pointer_end()graph_proto_ is not in sync with name_to_initial_tensor_Replacement tensor's data type does not match.Replacement tensor's dimensions do not match.Trying to replace non-external initializer with external dataonnxruntime::Graph::ReplaceInitializedTensorImplFailed to find existing initializer with name !foundgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::RemoveInitializedTensorsparse_tensor_names_.count(tensor_name) == 0sparse_tensor_names_ not in sync with name_to_initial_tensor_onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::AddInitializedTensorexisting->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::Graph::Resolveonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputsError: Duplicate definition-site for () attribute () is required but not specified.Fatal error: ) is not a registered function/oponnxruntime::Graph::VerifyNodeAndOpMatchThis is an invalid model. In Node, , Error Type Error: Shape of initializer does not match. Type Error: Data in initializer '' has element type but usage of initializer in graph expects This is an invalid model. Model input () does not have type information.Type Error: Type () of output arg () of node () does not match expected type () output arg () type inference failed, inferred types: index=onnxruntime::Graph::InferAndVerifyTypeMatchType Error: Type parameter () of Optype () bound to different types (Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat) -> (""(") : (onnxruntime::Graph::SetInputsinput->Exists()Input to set must exist. in inlined subgraph: conflicts with graph initializer. Check Specializing code.onnxruntime::Graph::InlineFunctioncallnode.TryGetFunctionProto(inlined_fp)Node has no function body and cannot be inlined._inlfunc_onnxruntime::Graph::InlineFunctionProtoConstant folded (inlined) for If node: Node's: input: is not If node's input or previous node output in this subgraphInitializer name: from graph: conflicts with graph initializer. Check name generation above._if_onnxruntime::Graph::InlineIfSubgraphUnable to constant fold If node: '' Unable to fetch: insert_result.secondConstant node name: conflicts with graph initializer. Check that the node names have been made unique.onnxruntime::Graph::AddConstantProtoAsInitializerdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()onnxruntime::Graph::FinalizeFuseSubGraphSetOpSchemaFromRegistryForNode(fused_node)Schema was not found for fused node. Domain: OpType: SinceVersion:onnxruntime::Graph::CreateFusedSubGraphNodenullptr != func_meta_defonnxruntime::Graph::AllocateNodenodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.Removing NodeArg 'outer_scope_node_arg != nullptr'. It is no longer used by any node.Removing initializer ''. It is not used by any node and should be removed from the model.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgsinitializer_node_arg != nullptrCannot find NodeArgs for [onnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name ''was added but does not exist. external_stream.is_open()onnxruntime::Graph::ToGraphProtoWithExternalInitializersexternal_file_path.is_relative()onnxruntime::Graph::ToGraphProtoabsl::container_internal::raw_hash_map<>::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.honnxruntime::ConstPointerContainer > >::atindex < data_.size()NodeEdge is missing. Invalid ORT format model.Node index is out of rangeNode is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Sparse Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Domain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker., but it its domain is notthan the operator set version , but it its version is higheronnxruntime::GraphViewerToProtoD:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.ccIndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptronnxruntime::GraphViewer::GraphViewerD:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccMismatch between Graph and IndexedSubGraph. Input not found:nodeargMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: nodeMemory efficient topological order is not enabled for non-training build.onnxruntime::GraphViewer::GetNodesInTopologicalOrderInvalid ExecutionOrderDϒ誑ٕ are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.h is till opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::model_load_utils::ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph.onnxruntime::Model::ModelD:\a\_work\1\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Missing model IR version., max supported IR version: Unsupported model IR version: model may run depending upon legacy support of some older opset version operators.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 Failed to load model with error: No graph was found in the protobuf.onnxruntime::Model::LoadProtobuf parsing failed. less than 0. is less than 0.onnxruntime::Model::SaveProtobuf serialization failed. is less than 0.onnxruntime::Model::SaveWithExternalInitializersonnxruntime::Model::SaveToOrtFormatNull entry in metadata_props. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model.onnxruntime::SaveModelonnxruntime::SaveModelWithExternalInitializers failed. File doesn't existLoad model system error number Output tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.Attribute should specify a shape in Negative values are not allowed in a shape specification in valuesGivenTensorFillBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_stateshidden_prevgatesseq_lengthshiddenGRUUnitIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) + bottom_border () needs to be greater than or equal to the top_border () + right_border () needs to be greater than or equal to the left_border () + scale[0] () + scale[1] (Value of alphaValue of betaConstrain input and output types to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccBias applied to each channel, same size as C.The scale to apply.A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Threshold valuedataConstrain input and output types to all tensor types.Constrain indices to integer typesinput_as_shapeextra_shapeNegative values are not allowed in a shape specificationCexpected to be a tensor or a sparse tensor type in must have a non null shape in not specified in Value of attribute should be of integer type and specify a type in does not specify a valid type in Incompatible dimensions but has rank expected to have rank Dimension mismatch in unification between expected to have rank >xError parsing node:Error unexpected extra input in node: = Constant()apply softmax to elements for dimensions axis or highertrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1(Optional) Seed to the random generator, if not specified we will auto generate one.residualratiomaskConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.isinf_onlyisnan_onlyBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.IsAllFiniteIf true, check only for Inf, -Inf.If true, check only for NaN.Constrain the output to a boolean tensor.Three interpolation modes: bilinear (default), nearest and bicubic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.GridConstrain input types to all tensor types.Constrain output types to float tensors.dimstepsize attribute in UnfoldTensor must greater than 0.size attribute in UnfoldTensor not specified!size attribute in UnfoldTensor not positive and less than the dim size!specify the dimension to unfoldspecify the sizespecify the step.Allow inputs and outputs to be any kind of tensor.UnfoldTensorConstrain to float tensors.Constrain to integer types.DynamicTimeWarpingThe id of the end-of-sequence tokenThe id of the padding tokenThe id of the token that indicates decoding starts.no repeat ngrams sizeearly stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like T5FA'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensions in whisper graphInputs 0 shall be 2 dimensionsFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccAlpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) Alpha value.Input rank must be >= 2. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:weightpadding_idxscale_grad_by_freqConstrain input and output types to all numeric tensors.TorchEmbeddingBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.Constrain input and output types to all numeric tensors and bool tensors.presence_maskfiltered_logitsActivation function to use. Choose from relu, gelu, silu and identity. Default is reluactivation_typeNumber of top experts to select from expert poolWhether to normalize routing weightsnormalize_routing_weightsWhether to use sparse mixeruse_sparse_mixerrouter_probsfc1_experts_weightsfc1_experts_biasfc2_experts_weightsfc2_experts_biasfc3_experts_weightsfc3_experts_biasConstrain input and output types to float or float16 tensors.MoENumber of bits used in quantized weights. Default is 4 bitsexpert_weight_bitsfc1_scalesfc2_scalesfc3_scalesConstrain weights type to uint8 tensors.QMoEConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain input0 and output types to float tensorsnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)signal_ndimmust be 0, normalization currently not supportednormalizedmust be 1, only one sided FFTs supportedConstrain input and output types to float or half tensors.Rfftnumber of dimensions comprising the signalIrfftThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shaperepetition_penaltyvocab_maskprefix_vocab_masklogits_processorsequencessequences_scoresscoresConstrain input type to float or int tensors.Constrain to integer typesConstrain mask to integer typesThe id of the token that indicates decoding starts (i.e. the start of transcription token id)The id of the translate taskThe id of the transcribe taskThe id of the token that indicates LM startsThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates no timestampsThe id of the first timestampMust be 2 for whisperIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.cross_qk_layer_headextra_decoding_idscross_qknon_speech_probsConstrain cross_qk to float32 tensors.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.The value used to module the next token probabilities.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.All filtered values will be set to this float value.Minimumber of tokens we keep per batch example in the output.Presence penalty for custom samplingIf 1 custom sampling logicModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartsparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)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.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensorConstrain input and output types to any tensor type.Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the 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.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.SequenceConstrain to tensor(int32).Constrain to tensor(float).'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayidxcountsInput can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".Constrains input to only numeric types.first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. roisbatch_indicescrop_sizeComplexMulComplexMulConjWPadsConstrain input and output types to float tensorsZFirst input does not have rank 2Second input does not have rank 2Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaInput axis is invalid: Input/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredinputs are expected to have tensor type and output type should not be null.Constrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).B input for MatMul must be a 2-D matrix!4b quantization not yet supported on this hardware platform!Input q4 tensors of wrong size!Quantization typeB_shapeConstrain input matrix data types as single precision float tensorConstrain input B data types as data blobConstrain shape of B must be int64 tensor.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationactivation_axissparse_tensor(float)Input 0 shall be 3 dimensionsbias shape must be knownbias shape must be [N] where N = The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).The epsilon value to use to avoid division by zero.type used for stash mean/inv_std_varInvStdDevConstrain input X type to float tensors.Type of Mean and InvStdDev tensors.Constrain output Y, scale and bias type to float tensors.inv_std_varConstrain mean and inv_std_var to be float tensors.Constrain output Y and scale type to float tensors.EfficientNMS_TRTboxesanchorsnum_detectionsdetection_boxesdetection_scoresdetection_classesBackground class ID.background_classEncoding type for the boxes or anchors inputs.box_codingBox IOU threshold value.iou_thresholdMax detections to output.Version number of the TRT plugin.plugin_versionActivation function to apply to the scores input.score_activationScore threshold value.score_thresholdMultilevelCropAndResize_TRTfeature_map_0feature_map_1feature_map_2feature_map_3patchesImage size.image_sizePooled size.Constrain types to float tensors.Constrain types to int tensors.Whether A should be transposed. Float 8 only supprted transA=0.Whether B should be transposed. Float 8 only supprted transB=1.Scalar multiplier for the product of input bias C.Output Type. Same definition as attribute 'to' for operator Cast.Activation function, RELU or GELU or NONE (default).TSscaleAscaleBscaleYTRConstrain type to input A.Constrain type to input B.Constrain type to input C.Constrain type to result type.Constrain type for all input scales (scaleA, scaleB, scaleY).GemmFloat8EpsilonXShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()PrefixShape = Slice (XShape, Zero1D, Axis1D)NumReducedAxes = Sub (Rank, Axis1D)NumReducedAxes = Neg (Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)XU = Cast (X2D)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedV, Scale2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)max_output_boxesAttribute 'max_output_boxes' must be >= 1.pooled_sizeAttribute 'pooled_size' must be >= 1.Constrain input and output types to float/half_float tensors.Constrain quantized weight types to uint8/int32.Constrain quantized zero point types to uint8/int32/float16/float.the index tensor.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.quantization data type. 0 for FP4, 1 for NF4.Indicate if the ops run in training_mode, by default, False.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.absmaxConstrain input and output types to float/half_float/brain_float tensors.Constrain quantized weight types to uint8.PyramidROIAlign_TRTDisentangledAttention_TRTc2c_attentionc2p_attentionp2c_attentiondisentangled_attentionMaximum relative distance, k.spanScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.factorpayload of the SNPE DLC file.DLC(Optional) SNPE version used to convert the model.snpe_version(Optional) Target device like CPU, DSP, etc.target_device(Optional) Some notes for the modelnotesConstrain input and output types to uint8, uint16, float tensors.EPContextUsually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.main_contextpayload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.ep_cache_context1: indicate ep_cache_context is the context content. 0: indicate ep_cache_context is the file path to the context content.The path is relative to this Onnx file. Default is 1.embed_mode(Optional) SDK version used to convert the model.ep_sdk_version(Optional) Filename of the original ONNX model.onnx_model_filename(Optional) Hardware architecture.hardware_architecture(Optional) partitioned graph name.partition_name(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainsourceConstrain input and output types.Constrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.size of each input featuresize of each output featurenumber of bits used for weight quantization (default 4)number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.The minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.zero_pointsg_idx̼+̼+key and value cache shall be 4 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past_key input shall be 4 dimensionsInputs 0 (query) shall be 3 dimensionsNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Whether to use rotary position embedding. Default value is 0.Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeThe value to be filled in the attention mask. Default value is -10000.0fCustom scale will be used if specified. Default value is 1/sqrt(head_size)pastrelative_position_biaspresentConstrain mask index to integer typesD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccInvalid bias shapeNumber of attention heads for k and vleft_window_size for local attention (like Mistral). Default value is -1 meaning unused.Rotate using interleaved pattern. Default value is 0 (False).Use a smooth factor in softmax.seqlens_ktotal_sequence_lengthcos_cachesin_cacheConstrain mask to int tensor.Number of attention heads for queryNumber of attention heads for key and valueScaling factor applied prior to softmax. The default value is 1/sqrt(head_size)Number of tokens per sparse block. Choices: 16, 32, 64, 128Rotary use interleaved pattern or not. Default value is 0.block_row_indicesblock_col_indiceskey_total_sequence_lengthsConstrain integer type.One sided attention windows length W, or half of total window lengthwindowglobal_weightglobal_biasGglobalLongformerAttentionq_weightkv_weightqkv_hidden_sizes should have 3 elementstoken_offsetcumulative_sequence_lengthPackedAttentionInputs 0 (query) shall be 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsquerykeyConstrain input and output to float tensors.Constrain mask, offset and sequence length to integer typesPackedMultiHeadAttentioncache_indirectionCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Need output the cross attention MatMul(Q, K)output_qkpast_keypast_valuepresent_keypresent_valueqkConstrain qk output types to float32 tensors.key_padding_maskNumber of attention heads for qaboneX_bias = Add (X, bias)X_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) Max distancemax_distanceDefault value is 0.is_bidirectionalbias_tablequery_lengthkey_lengthConstrain sequence_length to int tensors.RelativePositionBiasskipinput_skip_bias_sumConstrain mean and inv_std_var to float tensors.The NGram size.scores_outConstrain scores input and output types to float tensors.The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.src_tokenscur_tokensprev_suffix_match_idxpred_tokenstokenssuffix_match_idxGemmFastGelukey_cachevalue_cachestatic_kvuse_pasthas_layer_statehas_key_padding_masknew_key_cachenew_value_cacheConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.DecoderAttentionCustom scale will be used if specified. Default value is 1.0Rotary embedding dimension. Default value is 0.Number of attention heads. Default value is 0. Must use with rotary_embedding_dimragged batch inputs or not. Default value is 0Constrain input and output types to integer tensorsembqq_rotk_rotoutput1output2Constrain input and output types to float16 tensors.Constrain input 0 type to float tensorsGemmaRotaryEmbeddingThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)mask_index_typesegment_idsword_embeddingposition_embeddingsegment_embeddingembedding_sumConstrain input and output integer tensors typesConstrain input and output float tensors types.sequence_token_countcumulated_seq_lenmax_seq_lenConstrain sequence_token_count and token_offset to integer typesRemovePaddingConstrain token_offset to integer typesRestorePaddingquery_layerquery_biasrel_poseco_aGatedRelativePositionBiasThe number of groups of channels. It should be a divisor of the number of channels CThe epsilon value to use to avoid division by zeroActivation after group normalization: 0 for None, 1 for SiLUgroups1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Constrain input X and output Y types to float tensors.GroupNormConstrain gamma and beta to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccConstrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNormbias shall be 1 dimensionBiasSplitGeluBiasAddInput data type does not match the expected data typeScale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is 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.Scale and Zero-point must be of rank 1 Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} inputs are expected to have tensor type.{additionalDocumentation}{name}A_scaleA_zero_pointB_scaleB_zero_pointC_scaleC_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.y_scaley_zero_pointConstrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccx_scalex_zero_pointConstrain 'y', 'x_scale' to float tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.The type of BFP - must match with the BFPType enumEach 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.bfp_type or UNDEFINED. Got: expected to have: Dimension=tensor(uint4)Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred=tensor(int4)Y_scaleY_zero_pointConstrain input and output types to 8 bit tensors. , ]. Its actual value is: 'axis' must be in [opset version of corresponding SoftMax.apply softmax to elements for dimensions axis,or all dims along with axis according to op-versionSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain input and output types to signed/unsigned int8 tensors.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Number of neurons in the hidden layerCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Couple the input and forget gates if 1.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Rsequence_lensinitial_hinitial_cPY_hY_cConstrain weights types to 8 bit tensors.Constrain seq_lens to integer tensor.axis must be in [-rank, rank)Required attribute axis is missingWhich axis to concat onAll inputs to Concat must have same rankT8TVConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).block_dimConstrain the input to float and bfloat.Constrain shape and strides to uint64.Constrain y to uint8.QuantizeBFPThe datatype to dequantize to.Shape of quantized tensor must be 1D.Constrain y to float and bfloat16.Constrain the input to uint8.DequantizeBFPConstrain input type to 8-bit integer tensor.reducedA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).ReduceSumIntegerKeep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.MulIntegerb_scaleb_zero_pointConstrain input B data type to 8-bit integer tensor.Constrain input A, b_scale and output Y data type as float tensor.a_scalea_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A data type to 8-bit integer tensor.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointaxis must be in [-rank, rank-1]. input rank was data_scaledata_zero_pointreduced_zero_pointreduced_scaleCoefficient of leakage.X_scaleX_zero_pointlayernorm_outConstrain input and output types to float32 tensors.mask_index_outcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.cublasLt order of output matrix.order_inputorder_outputscale_inputConstrain to float typesQQuantizeWithOrderConstrain Scale to float32 typescublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.The output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)cublasLt order of output matrixDequantizeWithOrderorder_AcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_BcublasLt order of matrix Border_YcublasLt order of matrix Y and optional matrix Cscale_Ascale_Bscale_YConstrain bias and scales to float32scale_CQOrderedMatMulcublasLt order of weight matrixHidden layer sizes of Q, K, V paths in AttentioncublasLt order of global biasorder_weightscale_K_gemmscale_Q_gemmscale_V_gemmK_weightQ_weightV_weightscale_K_weightscale_Q_weightQ_biasscale_V_weightV_biasK_biasscale_QKT_gemmconditionz_scalez_zero_pointConstrain scale types to float tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain output zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output type to float32 or 8 bit tensors.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)input_scaleweight_scaleinput_zero_pointweight_zero_pointConstrain input and output types to int8 tensors.position_embedding_quantword_embedding_quantbeta_quantgamma_quantword_embedding_scaleposition_embedding_scalesegment_embedding_scalegamma_scalebeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointbeta_zero_pointdilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.number of groups input channels and output channels are divided into.Padding for the beginning and ending along each spatial axisQLinearConvTransposescale_QKT_softmaxscale_values_gemmConstrain scales to float32 tensors.QOrderedLayerNormalizationQOrderedAttentionorder_XcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale_Xquantization scale must be float tensors.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization tensor must be int8 tensors.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.QOrderedGeluConstrain scales to float32order_global_weightscale_weightscale_biasscale_qkv_gemmscale_global_weightscale_global_gemmBe compatible with float version.scale_outputinput and zero_point pair is expected to have be same type.QOrderedLongformerAttentionweight and zero_point pair is expected to have same type.ww_scalew_zero_pointConstrain filter type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain output type to 8-bit integer tensor.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe shape of the convolution kernel. If not present, should be inferred from input W.̼+D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.The size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Works on NHWC layout or not? Default not.Whether to use ceil or floor (default) to compute the output shape.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.for node: itr != node_args.end()Attempting to get index by a name which does not exist:D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::GetIndexFromNameonnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 cannot be safely updated to in one of the subgraphs.onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputInitializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputonnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)index >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameonnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. ExplicitInputs: ImplicitInputs:Invalid input index for node . Index:Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputonnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)onnxruntime::graph_utils::FindPathFailed since multiple edges matched:AttributeProto must have a name.onnxruntime::utils::SetNodeAttributeutils::HasName(attribute)D:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.ccHpD:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::SaveRuntimeOptimizationRecordToOrtFormat is null. onnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatruntime optimization record produced op idAttempting to load runtime optimization records for a previously loaded optimizer: onnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::fbs::utils::LoadOpIdentifierOrtFormatInvalid OpIdentifier string: D:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.ccD:\a\_work\1\s\onnxruntime\core/graph/op_identifier.honnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringFailed to parse since_version from UNDEFINEDINTFLOATGRAPHSTRINGINTSFLOATSTENSORonnxruntime::fbs::utils::SaveSparseInitializerOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.ccGraph attribute value was null. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatString data type is not supported for on-device trainingSaveAttributeOrtFormat: Unsupported attribute type: Unsupported tensor data type for tensor onnxruntime::fbs::utils::GetSizeInBytesFromFbsTensoronnxruntime::fbs::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.Tensor has external data but a data reader was not provided.Missing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing dims for sparse initializer: Missing indicies for sparse initializer: onnxruntime::fbs::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.TENSORSSTRINGSSPARSE_TENSORGRAPHSSPARSE_TENSORSonnxruntime::fbs::utils::SaveInitializerOrtFormat(@ 80軖໖𻖀Number of actual parameters cannot exceed number of formal parametersonnxruntime::function_utils::Inliner::bindactuals.size() <= formals.size()all_tensor_types_ir4D:\a\_work\1\s\onnxruntime\core\graph\function_utils.ccinput_arg->Type() != nullptrTAggregatedTypes in function opset imports.onnxruntime::function_utils::CreateSchemait != opset_imports.end()No opset registered for domain domain_version != -1onnxruntime::function_utils::IOTypeConstraintHelper::::operator ()static_cast(i) < node_op_schema->inputs().size()Too many inputs for op TininToutout, domain: is not found in model local functionsonnx_func_protoThe given function name: initializer != nullptronnxruntime::FunctionImpl::FunctionImpl is not found or is not constant initializer.D:\a\_work\1\s\onnxruntime\core\graph\function.ccCan not get shape initializer data!Unsupported type:limitdeltaUnsupported non-raw-data data type!tensor rank too smallD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales dimensioninvalid scales valueCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.QWMWAWmemory_seq_lensword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsbeta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionssegment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.The past input shall be 5 dimensionsFailed to read affinities from affinity string, thread_pool_size: Found invalid processor id in affinity string: Found out-of-range processor id in affinity string: onnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setNumber of affinities does not equal to thread_pool_size minus one, affinities: actual_num_affinities == static_cast(options.thread_pool_size) - 1Received null affinity_stringSize of affinity string must be between 1 and Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1 thread_pool_size: auto_set_affinity: OrtThreadPoolParams { stack_size: affinity_str: allow_spinning: dynamic_block_base_: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)onnxruntime::concurrency::ReadThreadAffinityConfig set_denormal_as_zero: Processor id must consist of only digits: processor_from > 0 && processor_to > 0Invalid processor interval: D:\a\_work\1\s\onnxruntime\core\util\thread_utils.ccProcessor id must start from 1: processor_id > 0, skip affinity settingprocessor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)dims[d_i] < d_maxD:\a\_work\1\s\onnxruntime\core\util\math_cpu.cconnxruntime::math::NextPosition??D:\a\_work\1\s\onnxruntime\core\mlas\lib\q4_dq.cppUnsigned quant types without zero points must allocate zero points with value 0.src_zero_points || signed_quant || dst_zero_pointsBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedColumns must be multiple of 2columns % 2 == 0BlockwiseQDQQuantizer::TransposeColumnWiseQuantizedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAlignedBlockwiseQDQQuantizer::TransposeColumnWiseQuantizedPackAligned33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KA%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;rB¥B@K;?r1:n/ <*=(*>>??bad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??bad mlas activation kind), BIsSigned() is not supported on this deviceQuant GEMM format: AIsSigned(ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!    D:\a\_work\1\s\onnxruntime\core\common\helper.cconnxruntime::ToWideStringlength overflowonnxruntime::ToUTF8StringonnxruntimeOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLoggerISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultSUCCESSFAILNO_SUCHFILENO_MODELNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]file_path == nullptronnxruntime::WindowsEnv::ReadFileIntoBufferoffset < 0GetFileSizeEx Invalid fd was supplied: Received negative size from stat callonnxruntime::WindowsEnv::MapFileIntoMemoryopen file mapping , errcode = , allocation_granularity = length > buffer.size()SetFilePointerEx ReadFile fail: unexpected endonnxruntime::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::WindowsEnv::GetCanonicalPath , mapped_offset = mapped offset must be a multiple of the allocation granularity\/DeleteFile() failed - path: LoadLibrary failed with error FreeLibrary failed with error Kernel32.dllK32EnumProcessModulesUNC\"" when trying to load " "onnxruntime::WindowsEnv::InitializeCpuInfoFailed to fetch cpu info on windows consist of logical processors: core in library, error code: Failed to find symbol onnxruntime::WindowsEnv::FormatLibraryFileNameFailed to calculate byte size for saving cpu info on windows core(s) from windows system:Found total bytes Detected L2 cache size: \*ExceptionReturnNtReturnHr%hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %ws LogNtLogHrFailFast%hs(%u)\%hs!%p: kernelbase.dllMsg:[%ws] CallContext:[%hs] [%hs(%hs)] [%hs] RaiseFailFastException%ws/\Negative thread index is not allowedindex >= 0onnxruntime::WindowsThread::WindowsThread error msg: unmap view of file failed. error code: onnxruntime::UnmapFileD:\a\_work\1\s\onnxruntime\core\platform\windows\env.cckernel32.dllSetThreadDescriptiononnxruntime does not existcustom_create_thread_fn returned invalid handle. doserrno: message:WindowThread:_beginthreadex failed with errno:, first on group Cannot set cross-group affinity for thread , mask: , group_id: , processor Cannot set affinity for thread onnxruntime::WindowsThread::ThreadMain, then on , index: SetThreadAffinityMask failed for thread: fail, errcode = open file SetThreadAffinityMask done for thread: . Specify the number of threads explicitly so the affinity is not set., error msg: , error code: onnxruntime::LoopDirD:\a\_work\1\s\onnxruntime\core/platform/path_lib.honnxruntime::concurrency::ThreadPoolProfiler::Stopenabled_}}D:\a\_work\1\s\onnxruntime\core\common\threadpool.ccProfiler not started yetunnamed_thread_pool!points_.empty()LogStart must pair with LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd", }, "sub_threads": {{"main_thread": {"thread_pool_name": "Distribution], "core": RunDistributionEnqueueonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetpoints_.empty()"thread_id": "", "block_size": [": {"num_run": !current_parallel_section.has_value()Nested parallelism not supportedWaitRevokeWait"core": UnknownEventn <= num_threads_ + 1More work items than threadsD:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.honnxruntime::concurrency::ThreadPoolTempl::RunInParallel!ps_onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectiononnxruntime::concurrency::ThreadPool::ParallelForn >= 0onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectiononnxruntime::logging::EtwRegistrationManager::LazyInitializeETW registration failed. Logging will be broken: D:\a\_work\1\s\onnxruntime\core\platform\windows\logging\etw_sink.cconnxruntime::logging::EtwSink::SendImplUnexpected Severity of SessionKernelNodeonnxruntime::profiling::Profiler::Startsession_logger != nullptrD:\a\_work\1\s\onnxruntime\core\common\profiler.ccApiWriting profiler data to file onnxruntime::profiling::Profiler::EndTimeAndRecordEvent",onnxruntime::profiling::Profiler::EndProfilingonnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event."ph" : "X","ts" :"args" : {"name" :""pid" :{"cat" : ""dur" :"tid" :] " : "" : }, } 斀斀斀斀illegal input path:unexpected failureFailed to get error messageUTC, ] [: [nan(snan)nan(ind)UTCCan not switch from manual to automatic indexingInvalid type.Precision specification invalid for non-chrono::duration type, see N4950 [time.format]/1.Invalid format string.Unmatched '}' in format string.Missing '}' in format string.Unknown format specifier.Invalid type specification.Invalid modifier specification.Incompatible modifier for typeInvalid format string - missing type after %Invalid format string - chrono-specs must begin with conversion-specInvalid format string - missing type after modifier.invalid fill character '{'\x{String pointer is null.\\\u{Can not switch from automatic to manual indexing{:L%F %T}Invalid fill (too long).Missing precision specifier.Number is too bigInvalid presentation type for wchar_tInvalid presentation type for boolInvalid presentation type for floating-pointInvalid presentation type for integer0e+00Dynamic width or precision index too large.Invalid presentation type specifierFormat specifier requires numeric or pointer argument.%I:%M:%S %p{:02}{:04}Invalid presentation type for pointerInvalid presentation type for stringZero modifier requires an arithmetic or pointer presentation typeHash/sign modifier requires an arithmetic presentation type{}{:02}{}{:02}{:02}:{:02}:Number is too big.{:0{}}Precision not allowed for this argument type.Precision is not an integer.Format specifier requires numeric argument.Negative width.Width is not an integer.Argument not found.integral cannot be stored in wchar_t0B0btrueNegative precision.false\n\n\r\r0X0x\t\tffffffGzGK7A!uq m7c^6 02 p.9 ^333p= I h1*  '.6>GPZdoz -?Rey,E^x<Zy<^gffffffQ~jt*Ǻ"<8b->Nc{-1+x#c^- Knu7X%Y^_GzK`eq]p J&gչ o"4%QttS`؈$C(ӦfHRl ۩#j+vv=IJ+10Q=ѬV Kp0%)Y : ; < > A I M N Q b   < > A G K Q p u   < > @ A G K M U b   < > A F J U b  ; > ? 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HSI - , % )2oc0G9 Q F [K9 #k .t'JpFWUG AT$>- :*L D=  ,d%,  w_  8 (T . 7 :1K`Kp0%)Y: < A M Q b  < A G K Q p u  < > A M U b  < > F J U b ; > A M W b 14G579q-29=X^q]2Rr  '29VX`bes4<Bk,6 ,--*00ot %,Ĩ&G)15CL| v   8 ?  $ F8ps'-s/46>A;>@Wfp8BF^3=?"'/90;>C3;GQY08?1:<?G6@B@4G4j0kOooo0egn{B;u#&0D p-      $    @!        .72`map_typesequence_typetensor_typeonnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatWe do not support type [] for nowonnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormatD:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.ccNull entry in dimensions. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorDimensionOrtFormatonnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormat is missing type info.onnxruntime::fbs::utils::SaveValueInfoOrtFormatdim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::fbs::utils::LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Model must have opset imports. Invalid ORT format model.onnxruntime::fbs::utils::LoadValueInfoOrtFormatopset id is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatType: is not supported currentlyNull type info for . Invalid ORT format model.opset import domain is null. Invalid ORT format model.œh6X6H6optional(sparse_tensor(map(tensor(Invalid data type DataTypeUtils::FromDataTypeString - Received invalid data type string 'opaqueInvalid tensor data type Unsuported type proto value case.mapoptional) yields zero outputs, but got :: typestr: , has unsupported type: ) takes zero inputs, but got in graph not in range [min=, max= not in allowed input sizes.) has input size has unsupported type has inconsistent type Operator '' has been deprecated since version ) has more inputs () than declared ()'s input is marked single but has an empty string in the graph not in allowed output sizes.) has output size ) in op definition.has output size ', actual: 'Unrecognized attribute: Mismatched attribute type in ''. Expected: ')'s output ) has more outputs ( for operator ' appeared multiple times.' is missing. has unknown expected typeRequired attribute '' is expected to have field 'sparse_tensor'' is expected to have field 't'' is expected to have field 'type_proto'' is expected to have field 'g'(inputs_.size() - 1) == iExtra unparsed input unexpected.ONNX Schema : failed validating the check: , opset_version = Duplicate type constraint nameError parsing function body:Cannot find a function builder that satisfies the requested opset version: op_type = !(it.GetName().empty())(outputs_.size() - 1) == itarget optional type missing element type.source map type missing key type.target sequence type missing element type.source optional type missing element type.Mismatched sparse tensor element type:source sequence type missing element type.Mismatched type:Mismatched tensor element type: does not match existing output type of Input element type of Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got source map type missing value type.target map type missing value type.target map type missing key type.Mismatched map tensor key type:Key type of map input was unknownValue type of map input was unknownElement type of optional input was unknownInput was expected to have map type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Output was expected to have tensor type. Got Input was expected to have sequence type. Got One and only one attribute must be set out of D expected to be a 1D tensor but was Input was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensorElement type of sequence input expected to have optional typeElement type of input expected to have sequence type expected to have tensor or sparse tensor type. Got: unknownInput type was nullMismatch between number of inferred and declared dimensions. inferred= inferred= expected to have type but instead is null declared=Key type of map input Value type of map input Element type of optional input expected to have map type' must have Unspecified required attribute.' must have type D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.map(int64, string)map(int64, float)The input must be an integer map to either string or float.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.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.A collection of weights of the model(s).A collection of intercepts.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]The output will be a tensor of strings or integers.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).The input must be a tensor of a numeric type.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Exactly one of 'cats_*' attributes must be provided.One of 'MAX,' 'L1,' 'L2'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'The input type must be a tensor of a numeric type, either [C] or [N,C].Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Support vector coefficients.The number of support vectors.Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.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'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.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.The input must be a tensor of strings or integers, either [N,C] or [C].Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.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 integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceA string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.map(string, double)The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.map(int64, double)map(string, float)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, int64)The size of each input in the input listThe input type must be a tensor of a numeric type. are different, which is not permitted for LabelEncoders. and the key type At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set.A value that needs replacing.Value(s) to change to.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toThe default tensor type The default tensor must be a singleton 1D tensor.The number of keys and the value type must be the same in the LabelEncoder. and the number of values The input type was At least one of values_tensor, values_strings, values_int64s, values_floats must be set.A list of ints.A list of floats.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.A list of strings.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Input's shape should be 1D or 2DA default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.An integer.A float.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.A string.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 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 default value is false (0) for all nodes.The id of the tree that this node is in.Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.The id of the tree that each node is in.Child node if expression is trueChild node if expression is falseNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)nodes_splitsDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)The weight for each targetThe total number of targets.The node id of each weightThe index of the target that each weight is formembership_valuesAttribute 'membership_values' must have same type as input. Input type is nodes_trueleafsnodes_falseleafs and attribute type is Attribute 'nodes_splits' must have same type as input. Input type is Attribute 'nodes_splits' is required.Attribute 'nodes_splits' must be 1D.Attributes 'leaf_targetids' and 'leaf_weights' must both be set.Attribute 'leaf_weights' must have same type as input. Input type is and 'leaf_weights' length is Attribute 'leaf_targetids' must have same length as attribute 'leaf_weights'. 'leaf_targetids' length is leaf_targetidsleaf_weightsFor each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.Index into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.1 if true branch is leaf for each node and 0 an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entry1 if false branch is leaf for each node and 0 if an interior node. To represent a tree that is a leaf (only has one node), one can do so by having a single `nodes_*` entry with true and false branches referencing the same `leaf_*` entryIf `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.If `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.TreeEnsembleIndicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)Defines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).The weight for each leaf.tree_rootsMembers to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.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 output will be a sequence of string or integer maps to float.ZipMap input shape should be 1D or 2D.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.ccThe output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.The input type must be a tensor of integers or strings, of any shape.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.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Input type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly 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 the attributes 'target_weights', 'target_weights_as_tensor' should be specified.A list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.Only one of values_*'s can be set in label encoder.optional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(tensor(int16))optional(tensor(int32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(uint16))optional(tensor(uint32))optional(seq(tensor(complex128)))optional(tensor(uint8))optional(tensor(complex64))optional(tensor(complex128))optional(tensor(string))optional(tensor(bool))optional(tensor(float))optional(tensor(double))optional(tensor(int64))optional(tensor(float16))D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.ccConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in 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.Target shape may not have multiple -1 dimensions.target_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., saturate: int = @saturate> (input)output = Cast = 1axis must be in [-r, r-1]squeezedConstrain input and output types to all tensor types up to IRv10.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Blocksize must be positiveList 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).expanded'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.repeatsIf 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.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".Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `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) ``` . 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.Constrain input 'X' and output 'Y' to all tensor types.sizesroiIf 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.When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.gridIf align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.Constrain roi type to float or double.Input 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.concat_resultConstrain output types to any tensor type.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)..Constrain output to int64 tensor, which should be a scalar though. has rank ) and the split dimension of the input (Mismatch between the sum of 'split' () and outputs (Mismatch between number of splits (Invalid value of attribute 'axis'. Rank=Both 'split' input and 'num_outputs' attribute were given Value=Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Attribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been given) vs (Input rank for starts and ends should be the same: (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 ends'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps}, input shape = {Attribute perm for Transpose has repeated value: Invalid attribute perm {stepsupdatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.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.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Input and output types can be of any tensor type.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorThe 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.inverse_indicesCenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputsSupported modes: `constant`(default), `reflect`, `edge`, `wrap` 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], ] ``` x_shape = Shape (input_data)padded_sh = Max(x_shape, shape)x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)axes_input = Constant ()) does not match the number of axes (Number of elements of input 'shape' (x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)padded_input = Pad (input_data, pads, , axes_input)padded_input = Pad (input_data, pads)pad_amount_right = Sub(pad_amount, pad_amount_left)pads = Concat (pad_amount_left, pad_amount_right)pad_amount = Sub(padded_sh, x_shape)pad_amount_left = Div(pad_amount, k2)input_dataoutput_data = Slice (padded_input, start_dims, end_dims, axes_input)output_data = Slice (padded_input, start_dims, end_dims)start_dims = Div (sh_diff, k2)end_dims = Add (start_dims, shape)x_shape2 = Shape (padded_input)sh_diff = Sub (x_shape2, shape)If 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.CenterCropPadoutput_data. Expected range [Unexpected axis value: is referred to more than once.Axis if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.. It must be 4 for 2D or 5 for 5D.Length of input 'size' is Constrain grid types to float tensors.Constrain input `X` and output `Y` types to all tensor types.Constrain input and output types to all tensor, sequence, and optional types. { # naming one: 1, one_f: 1.0, one_1d: [1], one_f_1d: [1.0] one = Constant () two = Constant () zero = Constant () four = Constant () one_1d = Constant () zero_1d = Constant () minus_one = Constant () minus_one_f = CastLike (minus_one, theta) zero_f = CastLike (zero, theta) one_f = CastLike (one, theta) two_f = CastLike (two, theta) constant_align_corners = Constant () constant_align_corners_equal_zero = Equal (constant_align_corners, zero) size_ndim = Size (size) condition_is_2d = Equal (size_ndim, four) N, C, D, H, W = If (condition_is_2d) < then_branch = g1 () => (N_then, C_then, D_then, H_then, W_then) { N_then, C_then, H_then, W_then = Split (size) D_then = Identity (one_1d) }, else_branch = g2 () => (N_else, C_else, D_else, H_else, W_else) { N_else, C_else, D_else, H_else, W_else = Split (size) } > size_NCDHW = Concat (N, C, D, H, W) theta_3d = If (condition_is_2d) < then_branch = g3 () => (theta_then) { # theta: N by 2 by 3 => N by 3 by 4 # use of thetaN23 is a way to make shape inference happy when theta is N by 3 by 4. gather_idx_6 = Constant () shape_23 = Constant () gather_idx_23 = Reshape (gather_idx_6, shape_23) shape_N23 = Concat (N, shape_23) gather_idx_N23 = Expand (gather_idx_23, shape_N23) thetaN23 = GatherElements (theta, gather_idx_N23) # N by 2 by 3 => N by 3 by 2 r1, r2 = Split (thetaN23) # N by 1 by 3 r1_ = Squeeze (r1) # N by 3 r2_ = Squeeze (r2) r11, r12, t1 = Split (r1_) # N by 1 r21, r22, t2 = Split (r2_) r11_shape = Shape (r21) float_zero_1d_ = ConstantOfShape (r11_shape) # N by 1 float_zero_1d = CastLike (float_zero_1d_, theta) float_one_1d = Add (float_zero_1d, one_f) # N by 1 R1 = Concat (r11, r12, float_zero_1d, t1) # N by 4 R2 = Concat (r21, r22, float_zero_1d, t2) R3 = Concat (float_zero_1d, float_zero_1d, float_one_1d, float_zero_1d) R1_ = Unsqueeze (R1, one_1d) # N by 1 by 4 R2_ = Unsqueeze (R2, one_1d) R3_ = Unsqueeze (R3, one_1d) theta_then = Concat (R1_, R2_, R3_) # N by 3 by 4 # theta_then = Identity (theta) }, else_branch = g4 () => (theta_else) { theta_else = Identity (theta) } > two_1d = Constant () three_1d = Constant () five_1d = Constant () constant_D_H_W_shape = Slice (size_NCDHW, two_1d, five_1d) # [N, C, D, H, W] => [D, H, W] zeros_D_H_W_ = ConstantOfShape (constant_D_H_W_shape) zeros_D_H_W = CastLike (zeros_D_H_W_, theta) ones_D_H_W = Add (zeros_D_H_W, one_f) D_float = CastLike (D, zero_f) H_float = CastLike (H, zero_f) W_float = CastLike (W, zero_f) start_d, step_d, start_h, step_h, start_w, step_w = If (constant_align_corners_equal_zero) < then_branch = h1 () => (start_d_then, step_d_then, start_h_then, step_h_then, start_w_then, step_w_then) { # => (float, float, float, float, float, float) step_d_then = Div (two_f, D_float) step_h_then = Div (two_f, H_float) step_w_then = Div (two_f, W_float) step_d_half = Div (step_d_then, two_f) start_d_then = Add (minus_one_f, step_d_half) step_h_half = Div (step_h_then, two_f) start_h_then = Add (minus_one_f, step_h_half) step_w_half = Div (step_w_then, two_f) start_w_then = Add (minus_one_f, step_w_half) }, else_branch = h2 () => (start_d_else, step_d_else, start_h_else, step_h_else, start_w_else, step_w_else) { # => (float, float, float, float, float, float) D_float_nimus_one = Sub (D_float, one_f) H_float_nimus_one = Sub (H_float, one_f) W_float_nimus_one = Sub (W_float, one_f) # avoid divide by 0 D_equals_one = Equal (D, one) step_d_else = If (D_equals_one) < then_branch = g5 () => (step_d_else_then) { step_d_else_then = Identity (zero_f) }, else_branch = g6 () => (step_d_else_else) { step_d_else_else = Div (two_f, D_float_nimus_one) } > step_h_else = Div (two_f, H_float_nimus_one) step_w_else = Div (two_f, W_float_nimus_one) start_d_else = Identity (minus_one_f) start_h_else = Identity (minus_one_f) start_w_else = Identity (minus_one_f) } > grid_w_steps_int = Range (zero, W, one) grid_w_steps_float = CastLike (grid_w_steps_int, step_w) grid_w_steps = Mul (grid_w_steps_float, step_w) grid_w_0 = Add (start_w, grid_w_steps) grid_h_steps_int = Range (zero, H, one) grid_h_steps_float = CastLike (grid_h_steps_int, step_h) grid_h_steps = Mul (grid_h_steps_float, step_h) grid_h_0 = Add (start_h, grid_h_steps) grid_d_steps_int = Range (zero, D, one) grid_d_steps_float = CastLike (grid_d_steps_int, step_d) grid_d_steps = Mul (grid_d_steps_float, step_d) grid_d_0 = Add (start_d, grid_d_steps) zeros_H_W_D = Transpose (zeros_D_H_W) grid_d_1 = Add (zeros_H_W_D, grid_d_0) grid_d = Transpose (grid_d_1) zeros_D_W_H = Transpose (zeros_D_H_W) grid_h_1 = Add (zeros_D_W_H, grid_h_0) grid_h = Transpose (grid_h_1) grid_w = Add (grid_w_0, zeros_D_H_W) grid_w_usqzed = Unsqueeze (grid_w, minus_one) grid_h_usqzed = Unsqueeze (grid_h, minus_one) grid_d_usqzed = Unsqueeze (grid_d, minus_one) ones_D_H_W_usqzed = Unsqueeze (ones_D_H_W, minus_one) original_grid = Concat (grid_w_usqzed, grid_h_usqzed, grid_d_usqzed, ones_D_H_W_usqzed) constant_shape_DHW_4 = Constant () original_grid_DHW_4 = Reshape (original_grid, constant_shape_DHW_4) original_grid_4_DHW_ = Transpose (original_grid_DHW_4) original_grid_4_DHW = CastLike (original_grid_4_DHW_, theta_3d) grid_N_3_DHW = MatMul (theta_3d, original_grid_4_DHW) grid_N_DHW_3 = Transpose (grid_N_3_DHW) N_D_H_W_3 = Concat (N, D, H, W, three_1d) grid_3d_else_ = Reshape (grid_N_DHW_3, N_D_H_W_3) grid_3d = CastLike (grid_3d_else_, theta_3d) # grid = Identity (grid_3d) grid = If (condition_is_2d) < then_branch = g1 () => (grid_then) { # [N, D=1, H, W, 3] => [N, H, W, 2] grid_squeezed = Squeeze (grid_3d, one_1d) # [N, H, W, 3] grid_then = Slice (grid_squeezed, zero_1d, two_1d, three_1d) # [N, H, W, 2] }, else_branch = g2 () => (grid_else) { grid_else = Identity (grid_3d) } > } Constrain size's type to int64 tensors.thetaConstrain to boolean tensors.'axis' must be in [-rank(indices), rank(indices)-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Indices tensor must have rank >= 1Input 'values' must have exactly two elements.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrain to any tensor type.Constrain input to only numeric types.depth(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. 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) Specify which axis is batch axis. Must be one of 1 (default), or 0.'sequence_lens' must have rank of 1(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'input' must have rank >= 2Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Invalid value for attribute axisseq(tensor(uint4))seq(tensor(int4))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))optional(tensor(uint4))optional(tensor(int4))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(seq(tensor(bfloat16)))optional(tensor(bfloat16))Only boolD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccGraph 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 IRv10.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.condv_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.v_initialfinal_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.initial_state_and_scan_inputstensor of int64, which should be a scalar.tensor of bool, which should be a scalar.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 IRv10.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An 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.The type of the input `y_zero_point` and the output `y`.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.cc(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.The type of the input 'x'.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.'x_scale' determines the output type.The type of the inputs 'x_zero_point' and 'x'. { 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) } The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. does not match y_zero_point type output_dtype Constrain input and output types to signed numeric tensors.Constrain input and output types to high-precision numeric tensors. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } Coefficient of ELU.Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of 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) } X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.MishConstrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } { 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) } Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) Two = Constant () TwoCast = CastLike (Two, X) SqrtTwo = Sqrt (TwoCast) XSqrt = Div (X, SqrtTwo) ErfXSqrt = Erf(XSqrt) Phi = Sum (OneCast, ErfXSqrt) MultX = Mul (HalfCast, X) Y = Mul (MultX, Phi) Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation. Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) TwoOverPi = Constant () TwoOverPiCast = CastLike (TwoOverPi, X) C0 = Constant () C0Cast = CastLike (C0, X) SqrtTwoOverPi = Sqrt (TwoOverPiCast) Three = Constant () ThreeCast = CastLike (Three, X) XCubed = Pow (X, ThreeCast) XCubedC0 = Mul (C0Cast, XCubed) XC0XCubed = Sum (X, XCubedC0) TanhInput = Mul (SqrtTwoOverPi, XC0XCubed) ErfApprox = Tanh (TanhInput) PhiApprox = Sum (OneCast, ErfApprox) MultX = Mul (HalfCast, X) Y = Mul (MultX, PhiApprox) Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. Value of alpha.Value of beta. { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } slopesumdata_0Constrain input and output types to numeric tensors. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } List of tensors for { 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) } Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.ccWrong op_type name for running propagation: broadcasting: (The type of the output and its zeropoint.The type of input a and its zeropoint.The type of input b and its zeropoint.Constrain scales.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.Constrain output Y data type as 32-bit integer tensor.const_zeroconst_oneConstrain input and output types to floating-point tensors.axis tensor can be int32 or int64 onlyloss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)expanded_target = Unsqueeze (target, axes)ignore_indexinput_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_ignore_index const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)const_one_floatconst_one_casted = Cast (const_one_float)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)const_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Identity (input)input_less_than_min = Less (input, min)X_ReduceMax = ReduceMax (input) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) X_ReduceMax = ReduceMax (input, axes) { 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) } ValuesAxis has less than the requested k elements.K input must be a one-dimensional tensor of size 1.K input must be of type int64.Whether to return the elements in sorted order.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.IndicesConstrain index tensor to int64Constrain input and output types to all tensors.A {name} window with length: size. The output has the shape: [size].The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. size input must be a scalar.size input must be greater than 0.Constrain scalar length types to integers.Blackman { 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. { 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) } HannConstrain the input size to int64_t.num_mel_binsdft_length input must be scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. num_mel_bins input must be scalar.Constrain to integer tensors.Constrain to float tensorsupper_edge_hertzlower_edge_hertzsample_rateIf 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.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!First input should have at least 2 dimensions in frame_length input must be scalar.Constrain to any numerical types.Constrain signal and output to float tensors.frame_lengthframe_stepsignalConstrain scalar length types to int64_t. loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Target rank must be 1 less than the input rank.weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather_temp = Gather (weight, transform_targets)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)losstargetInput and target dimension value mismatch.Weight rank must be 1.Ellipsis represents incompatible dimensions. does not match the equation indices.NegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.InputsConstrain input and output types to all numerical tensor types.Einsum expression string.Rank of input labelsoutput = NegativeLogLikelihoodLoss (X_Log, labels)log_prob = Identity (X_Log)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)Shape3D X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) is_onesided and inverse attributes cannot be enabled at the same timeinput tensor must have rank >= 2, including the complex dimension.SoftmaxCrossEntropyLosslog_prob. Valid values are '-rank <= axis && axis != -1 && axis < rank - 1' is invalid for a tensor of rank %s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointaxis input must be a scalar.%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()axis_tensor != nullptrrank == static_cast(new_shape_proto.dim_size())operator ()dft_lengthIf `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.axis attribute value dft_length input must be a scalar.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeConstrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.Stride 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.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.If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.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). 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.running_meaninput_varinput_meanConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.running_varRatio 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 axis on which to apply normalization, -1 mean last axis.) 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) } The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Attribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = 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.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain input and output types to float and 8 bit tensors.kernel shape should have Second input tensor has wrong dimensionAttribute pads has incorrect size values in 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.The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\defs.cc'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.Attribute pooled_shape must be specifiedROI pool output shape (height, width).RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthp value of the Lp norm used to pool over the input data.inputs are expected to have tensor type in input and zero_point pair is expected to have be same type in Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.number of groups input channels and output channels are divided into. default is 1.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.weight and zero_point pair is expected to have same type in NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (XU, NewShape)N = Shape (X)InstanceShape = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)XShape = Shape (XU)C = Shape (X)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)Axes2Mean = ReduceMean (X3D, Axes2)Shape3D = Constant ()X3D = Reshape (XReshaped, Shape3D)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)ScaleShape = Constant ()ScaleT = Cast (scale)NormalizedNC = Reshape (NormalizedOriginalShape, Shape3D)NormalizedT = Cast (NormalizedNC)NormalizedU = Div (Deviation, StdDev)NormalizedOriginalShape = Reshape (NormalizedU, XShape)The floating-point precision used in stage one of the computation.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, NormalizedT)GroupNormalizationScaling parameter.The exponent.The number of channels to sum overConstrain input and output to all tensor types up to IRv10.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). Input is ether string UTF-8 or int32/int64Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative valuesConstrain input and output types to float tensors.The 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.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=11-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Attribute 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) } A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)image_shape1-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.%s:%u: %s: Assertion `%s` failed.FloatEpsilonsinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalizationConstrain input and output types to all numeric tensor types.block_shape) rank of first input is Unexpected axis value (NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)Epsilon = Cast (FloatEpsilon)Axes_1 = Constant()num_groupsXU = Cast (X)Constrain input types and output Y type to float tensors.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.The value for the sole element for the scalar, int64, output tensor.value_intThe value for the elements of the output tensor in sparse format.sparse_valueThe value for the elements of the output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.value_stringThe values for the elements for the 1D, float32, output tensor.value_floatsThe value for the sole element for the scalar, float32, output tensor.value_floatThe values for the elements for the 1D, int64, output tensor.value_ints(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.value_strings(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Input tensor must be 2-dimensionalConstrain input types.Constrain output types to be numerics or boolean.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.Lower boundary of the output values.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The 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.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Input tensor must have rank 2Constrain input types to common numeric type tensors.All inputs to 'Range' op must be of the same typeConstrain output types to integral tensors.Attribute expected to have a one-dim sparse tensor in Attribute expected to have tensor or sparse tensor type in Attribute expected to have a one-dim tensor in output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.X_random = RandomUniformLike (input)X_greater = Greater (X_random, input) { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Constrain output types to all numeric tensors and bool tensors.BernoulliInput to 'Range' op should be scalars (Tensor with only one element and shape empty)Inputs and outputs must be UTF-8 stringsD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\text\defs.ccDelimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.Maximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.Inputs must be UTF-8 stringsOutputs are bools and are True where there is a full regex match and False otherwise.Regex pattern to match on. This must be valid RE2 syntax.Input shape must have either [C] or [1,C] dimensions where C > 0The number of substrings generated.The input must be a UTF-8 string tensorTensor of substrings.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.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"undefinedsum squareproductD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.cc { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } plus infinity (if supported by the datatype) or the maximum value of the data type otherwiseminus infinity (if supported by the datatype) or undefined otherwiseminus infinity (if supported by the datatype) or the minimum value of the data type otherwise { 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) } L2 norm { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } L1 norm { 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) } log sum exponent { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } log sumWhether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.imageencoded_streamPixel format. Can be one of "RGB", "BGR", or "Grayscale".pixel_formatInput tensor must be 1-dimensionalRGBImageDecoderD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\image\defs.ccConstrain input types to 8-bit unsigned integer tensor.Constrain output types to 8-bit unsigned integer tensor.D:\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.Constrain output types to be numerics.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.output = Cast (input)D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Invalid Target shape product of 0Target shape may not have multiple -1 dimensionsInvalid position of 0'step' cannot be 0Input axes has invalid datalength of each output. Values should be >= 0.The input is not evenly splittableType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. 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 shape'axes' attribute must not contain any duplicatesEnding indices (exclusive) 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].Starting indices of corresponding axis in `axes`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthList of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.List of non-negative integers, indicate the dimensions to be insertedConstrain input and output types to all tensor and sequence types.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Attribute value for pads is requiredAttribute pads has incorrect lengthWhich axis to split on. 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], ] ``` 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".Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_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).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 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.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Constrain input and output to only numeric types.constant_valueSupported modes: `constant`(default), `reflect`, `edge`Which axis to split onlength of each outputoutputs...rank must be greater than axisWhich axis to concat on. Default value is 1.New shapelegacy optimization attribute.One float, indicates the value to be filled, default is 0paddingsThree modes: constant(default), reflect, edgeList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Constrain tiles and axis's type to int64 tensors.tilesconsumed_inputsConstrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearThe scale along height dimension. It takes value greater than or equal to 1.height_scaleThe scale along width dimension. It takes value greater than or equal to 1.width_scaleAttribute 'scales' is required.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.) is not equal to the existing rank value (Ranks inferred (All Tensor types up to IRv9.All Tensor types up to IRv4.D:\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.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9. axis value ) is not equal to number of scan inputs ( was notScan 'body' subgraph outputs should all be tensors but output outputs. Expected Graph attribute inferencing returned type information for was not a tensor.Scan input All Tensor types was Int64 tensorNumber of scan output axes specified (Number of scan input axes specified () is not equal to number of scan outputs (then_branch and else_branch produce different number of outputs. but subgraphs produce An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Loop 'body' subgraph outputs should all be tensors but output All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Mismatched tensor element type for output then=Mismatched type for output If node has else=Loop 'body' subgraph scan outputs should all be tensors but output (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.cc(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.Constrain output to boolean tensor.Constrain input to boolean tensor.Constrain input types to all (non-complex) tensors.Constrain input types to all numeric tensors.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.Constrain input/output to boolean tensors.Constrain input to integer tensors.Constrain input/output to integer tensors. { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } E_XsquaredX_squaredEX_squaredConstrain input and output to all tensor types.The storage order of the tensor. 0 is row major, and 1 is column major.Stride along each spatial axis.Processed_STDX_varianceSTDp value of the Lp norm used to pool over the input data, default is 2.0.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.Dilation value along each spatial axis of filter.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.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 the filter.varFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f. 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_varsaved_mean(float, default 0.5) the ratio of random dropoutConstrain mean and variance types to float tensors. It allows all float type for U.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated. 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. 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). Constrain output mask types to boolean tensors.The ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccXReshaped = Reshape (X, NewShape)If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Scaled = Mul (ScaleReshaped, Normalized)X3D = Reshape(XReshaped, Shape3D) Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer.The axis in which to compute the arg indices.{empty_value}D:\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).Optional is expected to have an output.Constrain output type to all optional tensor or optional sequence types.Constrain input type to all tensor and sequence types.Type of the element in the optional outputOptional is expected to have either an input or the type attribute set.Input type is null. Type information is expected for the input.Attribute 'type' should be a TypeProto and it should specify a type.Constrain output to a boolean tensor.Constrain input type to optional tensor and optional sequence types.OptionalHasElement is expected to have 1 output.OptionalHasElement is expected to have 0 or 1 input.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\defs.ccConstrain 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.OptionalGetElement must have an input element.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\old.ccweight_gatherloss_Nddloss_N1ddloss_NCddinput_gather_elementexpanded_target'shape' input must be 1D tensorinput_gather_element_transformconst_zero_castedtransform_targetsexpanded_target_int64const_zero_target_typedweight_gather_sumloss_sumloss_unweightedX_LogSMX_NDCX_NCDweight_gather_temp_1weight_gather_tempconst_one_castedsqueeze_maskIf set, defines the broadcast dimensions. See doc for details.broadcastPass 1 to enable broadcasting 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_shapeX_LogSM_NCDCoefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01.Value of beta default to 0.5Value of alpha default to 0.2Coefficient of ELU default to 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedMaximum value, above which element is replaced by maxMinimum value, under which element is replaced by minDimension on which to do the sort.Number of top elements to retrieveInvalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.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.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.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, 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.Constrain output y and its zero point data type to 8-bit integer tensor.Input type for input at index is null. Type info is expected.SequenceConstruct is expected to have at least 1 input.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.cc(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type. Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.Constrain input types to any tensor type.output_sequenceElement type of inputs are expected to be the same.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).positioninput_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence=Input type for input at index 0 is null. Type info is expected.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1Keep 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.Constrain split size to integral tensor.Invalid ``body`` argument. Expected a graphGraph attribute inferencer for "body" not available expected to be a sequence type expected to have type infoInsert and concatenate on a new axis or not, default 0 means do not insert new axis.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]`. _cond_in_itercountSequenceMap_loop_body expected but not providedExpected a sequence type for input 0Input 0 expected but not providedExpected 1 or more outputs.Expected 1 or more inputs.The graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function._seqempty_cond_seqlenSequenceMap__in_cond_outSequenceMapConstrain to any tensor or sequence type.Constrain input types to any sequence type.out_sequenceadditional_inputsNumber 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.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. D:\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).The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.selected_indicesmax_output_boxes_per_classOptionalHasElement is expected to have 1 input.Input must be an optional-type value containing an element with type information.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccThe 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.The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].First input tensor must have rank 3When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.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.D:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.ccConstrain input to integral tensors.Constrain input to float tensors.If set, defines the broadcast dimensions.Enable broadcastingD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.ccD:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\old.ccThe sequence output for the hidden is optional if 0. Default 0.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.foward 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) 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 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) is undefined so it cannot be parsed. expected size does not match the actual sizeCannot parse data from external tensors. Please load external data into raw data for tensor: ParseData type mismatch for tensor: . Expected: Actual:The type of tensor: data type is string. string content is required to be stored in repeated bytes string_data field. raw_data type cannot be string.Data size mismatch. Tensor: graphwstring_to_utf8str: string is too long for converting to UTF-8.utf8str_to_wstring: string is too long for converting to wstring.key_typeelem_type' is required but missing.Field 'name' of 'value_info' is required to be non-empty.string_dataint32_datafloat_datasetting data_type field (tensor name: ) to UNDEFINED is not allowedUnrecognized type value case (value_info name: ): value_type) is 0-element but contains data!TensorProto ( tensor name: ) is stored externally but doesn't have a location.Data of TensorProto ( tensor name: ) is stored externally and should not have data field.uint64_datadouble_dataint64_dataUnrecognized data_type (tensor name: values of data_type '' should be stored in field '' instead of 'STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field.TensorProto (tensor name: ) index value at position [] out of range [0, Sparse tensor indices () has values, but NNZ is domain ==> Context: ) should be stored in , but it doesn't exist or is not accessible.) should be file inside the , but the '' points outside the directoryLocation of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: ConstantFill) must have rank 1.sparse_tensor_proto] not in lexicographic sorted order.] out of range.) second dimension size does not match rank of tensor.) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor (type field and data field mismatch in attribute attr) has no index values.) must have rank 1 or 2.) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values (NodeProto (name: , type: ) has zero input and zero output.op_typeWarning: Model contains experimental ops:) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.' has been used as graph input names multiple times.Bad node spec for node. Name: OpType: Op registered for is deprecated in domain_version of No Op registered for with domain_version of No opset import for domain ' is not output of any previous nodes. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty name in initializer but not in graph input initializer name is not uniqueTensor initializers must have a non-empty nameinitGraph must be in single static assignment (SSA) form, however '' has been used as output names multiple times.Nodes in a graph must be topologically sorted, however input '' of node: name: stringsfloatstype_protointproducer_versionproducer_nameopset_importir_versiontype_protossparse_tensorsgraphstensorsInteger value expected, but not found.Expected character (line: column: metadata_propsdoc_stringmodel_versionExpecting primitive type as map key type.Value expected but not found.Incomplete string literal.Encountered invalid float literal!Identifier expected but not found.String value expected, but not found.Unexpected literal type.Mismatch between expected type and specified value's typeUnhandled type: %dError parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto (expected a tensor type).Unexpected type.Unexpected type in sparse-tensor element type.Singleton attribute value cannot be specified as a list.Empty list attribute value requires type annotation.Unexpected attribute type.[ParseError at position Error context: stol argument out of rangeinvalid stol argumentinput and zero_point pair is expected to have same type.unk__Inference error(s): Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Cannot infer type and shape for node name . No opset import for domain optype type case unsupported for symbolic shape inference. inferred=Graph initializer names must appear after the actual inputs: were provided.The number of graph input cannot be smaller than the number of node inputCannot use the same name as both a subgraph initializer and subgraph input: Graph has inputs but were provided(op_type:, node name: Cannot find missing input: in initializers. : (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in dimension does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of bounds.Data for input already exists.node type case unsupported. existing=key type mismatch from MapProto. existing=type case mismatch. existing=NOT_SETsparse_tensor_typeopaque_typeoptional_typeGot the last dimension of the grid tensor: The last dimension of the grid tensor must be the rank of the grid tensor - 2. ) is not equal to the existing dim value (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".Number of elements of input 'sizes' (Got grid tensor rank: Got input tensor rank: The input tensor and grid tensor must have the same rank for GridSample. Got input tensor and grid tensor ranks: The input tensor and grid tensor ranks must be >= 3. ) must be same as rank of input 'X' (Number of elements of input 'scales' (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64 values. values. Got Pads has incorrect number of values. Expected 2 * Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output Attribute 'value_float' expect a float.Attribute 'value_string' expect a string.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Constrain input and output types to numeric and Boolean tensors.axes as an input and attribute cannot be specified at the same time. Computes the {name} of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. onnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.Optionalonnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TrainingInfoProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProto      FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDBFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16FLOAT8E4M3FNFLOAT8E4M3FNUZFLOAT8E5M2FLOAT8E5M2FNUZINT16INT32INT4INT64INT8STRINGUINT16UINT32UINT4UINT64UINT8UNDEFINED{e{epattern too large - compile failedError reverse compiling 'Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.ccUnknown encoding Error parsing 'Error compiling 'SearchBitState inconsistencySearchNFA inconsistencyUnexpected re_anchor value: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencyD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccRegexp not destroyed.missing )unexpected )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]invalid named capture groupNumCapturesWalker::ShortVisit calledStack not empty.Walk NULLD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hhwxwwwwwxvvvvvvwwwshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccunknown round: RE2: unexpected op: Bad hex digit AnyLuLlLtLmD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccAddFoldedRange recurses too much.RepetitionWalker::ShortVisit calledConcat of LoNlMnMcNdPcBad call to ParseState::ParsePerlFlagsD:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.ccunhandled in AddToThreadqUnexpected opcode in short circuit: Unhandled in stepcontext does not contain textBad args: nsubmatch=DeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.Failed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Resetunhandled opcode: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.cc|D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccCannot use SearchOnePass for unanchored matches.D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.ccUnexpected opcode in IsMatch: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat of min max D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccCase not handled in ComputeSimple: CoalesceWalker::ShortVisit called[:^alnum:][:alnum:][:^alpha:][:alpha:][:^ascii:][:ascii:][:^blank:][:blank:]\D\d\S\s\W\w[:^print:][:print:][:^punct:][:punct:][:^space:][:space:][:^upper:][:upper:][:^cntrl:][:cntrl:][:^digit:][:digit:][:^graph:][:graph:][:^lower:][:lower:]09AZ__az09AZ__az ~ !~!/:@[`{~[:^word:][:word:][:^xdigit:][:xdigit:]AZAZaz 09AFaf 09AZaz0909az888H(HXHȁȁ0D DP@pP`PЀ00  Ё  hLdLplxtAdlamAnatolian_HieroglyphsAhomArmenianArabicBalineseAvestanLisuLinear_BLydianLycianMakasarMahajaniMandaicMalayalamKhudawadiKhojkiLaoLLepchaLatinLinear_ALimbuMiaoMeroitic_HieroglyphsMongolianModiMultaniMroNabataeanMyanmarMarchenManichaeanMeMasaram_GondiMeetei_MayekMedefaidrinMeroitic_CursiveMende_KikakuiOl_ChikiOghamOld_ItalicOld_HungarianOld_PermicOld_North_ArabianOld_SogdianOld_PersianNandinagariNag_MundariNewaNew_Tai_LueNoNkoNyiakeng_Puachue_HmongNushuPdPau_Cin_HauPfPePhoenicianPhags_PaPoPiOld_TurkicOld_South_ArabianOriyaOld_UyghurOsmanyaOsagePalmyrenePahawh_HmongBuhidBugineseCarianCanadian_AboriginalCcCaucasian_AlbanianChakmaCfBassa_VahBamumBengaliBatakBopomofoBhaiksukiBrailleBrahmiCypro_MinoanCypriotDeseretCyrillicDives_AkuruDevanagariDuployanDograCherokeeChamCoChorasmianCopticCommonCuneiformCsGujaratiGreekGurmukhiGunjala_GondiHangulHanHanunooHanifi_RohingyaElbasanEgyptian_HieroglyphsEthiopicElymaicGlagoliticGeorgianGranthaGothicKannadaKaithiKawiKatakanaKharoshthiKayah_LiKhmerKhitan_Small_ScriptHebrewHatranImperial_AramaicHiraganaInscriptional_PahlaviInheritedJavaneseInscriptional_Parthian22PR`!!!!00!0)080:0:@[,_,`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~ª۪ߪ@kCkoooooo0m7=KK    : : .... . . . ...!.!.  @j^j`jijnjoj55OOoo__? @ T T 34MO??Pv0J'pz|  @n_n4Mh  89;>@DFFJPl!<Up4Vn!`l@ U X _ A0000@[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++.]./0000 00070<0?0000000111111 2_22222X33MM!09..ϩϩ[[jk>?0RTfhk ;@[epp$$   ?? 88ii--  ....:.;.@.@.].].0000000012XXcc 3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq-/= ooWW  ? ? 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O Q W b c   < < > B G H K M Q Q p q u u   < < > D G H K M U W b c  < < > D F H J M U V b c  ; < > D F H J M W W b c 114:GN557799>?q+>VY^`bdgmqt]_24RSrs   +0;U^`|4Dks$7 ,,----*0/000ort} #',,Ũ&-GS)6CCLM{} /#-/@w;;-0Fgi{))]]}};;==F F ~ ~ # # # #*#*#i'i'k'k'm'm'o'o'q'q's's'u'u'''''''''''''))))))))))))))))))))))))))))#.#.%.%.'.'.).).V.V.X.X.Z.Z.\.\. 0 0 0 0 0 000000000000000>>6688::<<>>@@BBDDHHZZ\\^^ ==]]``cc  ( ) / / _ _ 00      ( * 0 2 3 5 6 8 9 < < > B G H K M Q Q Y \ ^ ^ f v (([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.U.U.W.W.Y.Y.[.[.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bbpjjjjooWW  ? ? P X     9 ?   UYGM@Ctu8=KOZ[]]AC`l<>;;DF?F AEpqCOp$t$//njojjj7k;kDkDknnoo^_ 0 9 fo6?PYPY09PYPYPYPY`jijjjPkYk@IPY !#$&*oop ' 0 9 @UW_  * . ` d f o  578<<??    ( * 9 < D F H J M U V X Z ] ] ` c f o w  9 ? ? 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H P X 46G r w x ~ X ^ p x *3i|p p t y P!_!!!`$$$$v'',,11 2)2H2O2Q2_2222205p "UUdg` ?AJnoq/M@X`jp 9 = = P P X a r     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ] ] ` a     : = = N N T V _ a z  023@E@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo1@Q`lnp BDxPmpt T3EL#MOZw5!8!0-g-------------------00<0<0A000000001/111111114MNФ *+nn妏 "@s %0F`|(@BDK`oqvzz~ªª۪ܪ  &(.װmp(*68<>>@ACDF=Pptvfoq`lnprs6@MPY\_`  0-g-o-p---  ; ; > @ I L N O   > @   > > @ @ G H K L W W   A D   > @ F H J L W W >?+,1188;<VWbdgm44#&)+0138UUWWaacdmr55;;=ACD$+45.0/0#$''èRS/034MM{{}}쫁  pr1VYxȈ􈙀🙀$舙` 8虀`HxX(ƙܙXǙȌH虀虀(  @4 әXTPtřh虀lD(8虀P Lෙd[R\|pʙ@Ќ(ә茙𣙀Ǚx虀, 荙虀Pә Ǚ(ә p@8!( p(Ǚ @ ЍȍǙ ؎8p와 莙0陀 ƙP8H8 `PXǙhpp8ә0|p𙙀|0ԙ와 0"H晀@Ǚ 癀0zݙi)z9z" z zP]@)4ʙ,陀™xH𑙀@p`H陀X`虀 𷙀h陀0(zPpЙF,H P8@와xp` ԙșzʙ𦙀䉙؉`ȣƙLp晀C@Fәzƙ%`0pƙ$@0zXЙ<Е+`(Hp™`x와ЊX@芙X8Hԋ`ƙd@ș:8pzؑh8 홀L|0 와ԙ 09O貙(`p0@ΙQ @h80Ǚ0hǙ陀 0虀в ԙ̲5 ಙСsFز ™ʙгȳ೙سP虀@d@\ptlx |P@x8hǙP@H(l`x`ʙƙ𳙀ʙ@陀>P00ș,?Bad final char: {%d}{%d,}\C{%d,%d}$^D:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\tostring.cc(?:?P<kRegexpCapture cap() == 0(){}[]*+?|.^$\>(?:)[^\x00-\x{10ffff}]\x{%x}(?-m:$)(?-m:^)\b\z(?HaveMatch:%d)\B\f[]^-\ [truncated]AZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&C&*&'%EETpsvw{}t&%@?C  3@60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}8`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,/,00,_,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~Z0ZZZZZZZZçħħŧŧZƧƧuǧʧЧѧ֧٧SS`p0h!: AZ'((O(pz'|'''  @   @n_n `nn!""C (stacktrace: *** Check failure stack trace: *** %c0000 00:00:%02d.%06d %7d RAW: Check %s failed: %sTryLock failed TryLock succeeded Wait unblocked Wait on SignalAll on Signal on D:\a\_work\1\b\RelWithDebInfo\_deps\abseil_cpp-src\absl\synchronization\mutex.cc %p @ReaderTryLock failed ReaderTryLock succeeded Lock returning Lock blocking ReaderLock returning ReaderLock blocking ReaderUnlock Unlock s->waitp != nullptr || s->suppress_fatal_errorsdetected illegal recursion in Mutex code%s%p %s %ss->waitp == nullptr || s->waitp == waitp || s->suppress_fatal_errorsdetected illegal recursion into Mutex codepw->skip == nullptrbad skip in DequeueAllWakeablethis->LockSlowWithDeadline(how, cond, KernelTimeout::Never(), flags)condition untrue on return from LockSlowCheck (v & (kMuWait | kMuWrWait)) != kMuWrWait failed: %s: Mutex corrupt: waiting writer with no waiters: %pCheck (v & (kMuWriter | kMuReader)) != (kMuWriter | kMuReader) failed: %s: Mutex corrupt: both reader and writer lock held: %pLockwaitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorsnew_h != nullptrEnqueue to empty list failed%sold_h->skip == nullptrillegal skip from headpw not w's predecessorh->skip == nullptrunexpected empty wake listpw->next == wslow releasewake_list != kPerThreadSynchNullUnlockEnqueue to list failedUnlockSlow is confusedwaitp == nullptr || waitp->thread->waitp == nullptr || waitp->thread->suppress_fatal_errorswaiters disappeared during Enqueue()!waitp != nullptrold_h == nullptr || h->maybe_unlockingMutex queue changed beneath uswaitp->thread->waitp == nullptrwaiting when shouldn't bethread should hold at least a read lock on Mutex %p %sAABBBBCB 8C (CBB0B B  hSleepConditionVariableSRW failed: %luD:\a\_work\1\b\RelWithDebInfo\_deps\abseil_cpp-src\absl\synchronization\internal\win32_waiter.cc0123456789ABCDEF%   *.*NAN0123456789ABCDEF0123456789abcdef#K-00@@@@@@@@@hHHHH@@@@@@@@@@@@@@@@@@(@000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f707172737475767778797a7b7c7d7e7f808182838485868788898a8b8c8d8e8f909192939495969798999a9b9c9d9e9fa0a1a2a3a4a5a6a7a8a9aaabacadaeafb0b1b2b3b4b5b6b7b8b9babbbcbdbebfc0c1c2c3c4c5c6c7c8c9cacbcccdcecfd0d1d2d3d4d5d6d7d8d9dadbdcdddedfe0e1e2e3e4e5e6e7e8e9eaebecedeeeff0f1f2f3f4f5f6f7f8f9fafbfcfdfefflevel >= 1block not big enough for even one levele == foundelement not in freelistleft_haven't left Arena regionD:\a\_work\1\b\RelWithDebInfo\_deps\abseil_cpp-src\absl\base\internal\low_level_alloc.ccsum >= aLowLevelAlloc arithmetic overflowi < prev->levelstoo few levels in Next()next->header.magic == Magic(kMagicUnallocated, &next->header)bad magic number in Next()f->header.arena == arenabad arena pointer in AddToFreelist()arena->allocation_count > 0nothing in arena to freenew_pages != nullptrVirtualAlloc faileds->header.arena == arenanext->header.arena == arenabad arena pointer in Next()prev < nextunordered freelistreinterpret_cast(prev) + prev->header.size < reinterpret_cast(next)malformed freelistf->header.magic == Magic(kMagicAllocated, &f->header)bad magic number in AddToFreelist()RtlCaptureStackBackTracentdll.dll%.*s[%s : %d] RAW: ... 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M t  %b }00h(њq4h0.5 $h&O p`0PщZ(ټ H:g:`A: С 5С a * T., $d7462 pщ(2 g:``2 jX0* 4:0 p`Pщdr(m ::M~"9   00(њ0.4 R pԽ(ݽ佝 FD0 4MB p`Pщ (a :!:g`2:6g`:`:  ~ !., - i p`0Pщ:( : :!:A:::n .P.~2m":"M 1& A) A% = MM\- i p`0Pщ`:(i"M 1& A) A% = MM\ t 4 R `P, 4\T p`PщԿ(ݿ :::jm"a:@ N ! b , 4\T p`PщD(ݿM@ N ! b , 4\T p`Pщ(ݿ@ N ! b   d 2p!4m"m"!m"m"' # p`0Pщ ( ) :2j$ p`P0щ\(evdT4p(@' # p`0Pщ ( 5I(' # p`0Pщ ( % : 2j-I( t- d, 4* &P\(e <<avP,a"2j RDF 8+ 4 p`0Pщ( 0bgpg2`p2 ]!., 4\T p`Pщ8Ý(ݿAÝ@ N ! b , 4\T p`Pщ|Ý(ݿÝ@ N ! b , 4\T p`PщÝ(ݿÝ@ N ! b , 4\T p`Pщĝ(ݿ ĝ@ Na ! b , 4\T p`PщLĝ(ݿUĝ@ Ny ! b , 4\T p`Pщĝ(ݿĝ@ N ! b , 4\T p`Pщĝ(ݿĝ@ N ! b , 4\T p`Pщŝ(ݿ%ŝ@ N ! b 5 $x* h+4`X p `Pщhŝ(ݿqŝ@ VQ ! b , 4\T p`Pщŝ(ݿŝ@ N ! b 5 $x* h+4`X p `PщƝ(ݿ Ɲ@ V] ! b , 4\T p`PщHƝ(ݿQƝ@ N% ! b , 4\T p`PщƝ(ݿƝ@ N ! b , 4\T p`PщƝ(ݿƝ@ N ! b p`0ǝ(ǝ4ǝ p,6Zq<' # p`0Pщ`ǝ(iǝwǝ :Iu,\V P!d p9#9#ǝ!  t49#9#ǝ!9#9#ǝ!td 4p9#9#ǝ!p9#9#ǝ' # p`0Pщ$ȝ(iǝ-ȝIm,\V  !T >#F>#@ȝ!t d 4F>#z>#Pȝ!F>#z>#Pȝ!td T 4>#F>#@ȝ!>#F>#@ȝ!T B#B#@ȝ!t d 4B#C#ȝ!B#C#ȝ!td T 4B#B#@ȝ!B#B#@ȝ!d PG#G#ǝ!  t4G#G#0ɝ!G#G#0ɝ!td 4PG#G#ǝ!PG#G#ǝ, 4\T p`Pщɝ(ݿɝ@ N ! b , 4\T p`Pщʝ(ݿ ʝ@ N ! b , 4\T p`PщDʝ(ݿMʝ@ N ! b 0 4ND p`Pщʝ(ʝʝ :g2``2 a:A| J $ D yP00 4VL p`Pщ˝R( ˝2˝ :g2``2 a:Ax J $ yP.!4 #[# ! #[# (щ(щ0 4\R p`Pщ˝(˝˝ A:gp2`: A:  Z $  T.' ) p`0Pщ4̝2(=̝K̝ :Iu,\V!d ##ǝ!  t4#E#\̝!#E#\̝!td 4##ǝ!##ǝ0 4\R p`Pщ̝(˝̝x Z $  T.' ) p`0Pщ4͝2(=̝=͝Im,\V  p! 0#n#P͝! d T 4n##\͝!n##\͝! d T 40#n#P͝!0#n#P͝! #N#P͝! d T 4N##͝!N##͝! d T 4#N#P͝!#N#P͝' ) p`0PщpΝ2(=̝yΝIu,\V P0!d#7#Ν!t7#m#Ν!7#m#Ν!td#7#Ν!#7#Ν' ) p`0Pщ$ϝ2(=̝-ϝIm,\V  0! ##@ϝ!t d T#;#Pϝ!#;#Pϝ! t d T##@ϝ!##@ϝ' ) p`0Pщϝ2(=̝ϝIm,\V! ##@ϝ!t d T#+#Н!#+#Н! t d T##@ϝ!##@ϝ' # p`0PщН(iǝНIu,\V!dp##Ν!t##Н!##Н!tdp##Ν!p##Ν' # p`0Pщ4ѝ(iǝ=ѝIm,\V 0!T#H#Pѝ!  td H#~#`ѝ!H#~#`ѝ!td T#H#Pѝ!#H#Pѝ' # p`0Pщѝ(iǝ=͝!T##Pѝ!  td #>#ѝ!#>#ѝ!td T##Pѝ!##Pѝ0"xh' p`0Pщҝ(iǝҝm],m|V  p`P!4 #%#ҝ!@@1(x h %#a#ҝ!%#a#ҝ!x h 4 #%#ҝ!#%#ҝ' # p`0Pщlӝ(iǝuӝI],q\VP!4@$$ӝ!td$$ӝ!$$ӝ!td4@$$ӝ!@$$ӝ0 4\R p`Pщ0ԝ(˝9ԝ Z $  T.!4 $e $ҝ!@@1(x h e $ $Tԝ!e $ $Tԝ!x h 4 $e $ҝ! $e $ҝ' # p`0Pщ՝(iǝ՝I],\V!4$$ӝ!td$$$՝!$$$՝!td4$$ӝ!$$ӝ0 4ND p`Pщ՝(ʝ՝| J $ , n4P0. 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