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So.H$8dH+%(HHL[]A\A]A^A_3v.r:D$6 H|$7D$0ErrofD$4DE1sHMHHcLZo1L$p.L$LT$IMH11r.a9u.fAWAVAUATLgXUSHLHm.H t.1Hǃ0Hǃ@HǃHHHCX1f8Hz.HǃPHhLhHǃXH}H+HL/]v.HCH{@LsHƐ.HCHPH@HC HCXHc.HHHC(HCHC0HC8 {.H.LLCHHHCHu.HHCPu.H[]A\A]A^A_IILDHEH+L,Hr.LHHCXl.Lx.f.AWAVAULohATUSHLHl.H-P.L=r.1Hǃ@LeIGLuHǃPHCh1fHID$HǃXHǃ`HǃhL#L4HCI|$Ht.HE 1HCH@H|HE(Ht.HEHU0H{PLcH@HH@u.HC HPHC(HHPhH@HCH.HShHHC0HCHC8HC@HCHEy.H6.LLCXHHCHRs.HHC`t.H[]A\A]A^A_H$I3HID$L#L4HCIGLHCh k.H1w.LDHuHLi.SHHdH%(HD$H.HHHFPHxx)H;=r.u0HCHD$dH+%(u6H[fDHt$1dj.fH=ȝ.u@HHAq.AUATIUSHdH%(HD$Hb.HHHHHj.HHtWHT$1H8w.IHHHtKHHHn.HL;-q.uAIL$HD$dH+%(u@H[]A\A]ÐHq.HHEAEfDAEI]ADgp.H=l.HACf.DSHH.HH[SHH.HH[AUATIUSHdH%(HD$Hڞ.HHHHHi.HHtWHT$1Hu.IHHHtKHHHmm.HL;-p.uAIL$HD$dH+%(u@H[]A\A]ÐHYp.HHEAEfDAEI]AD'o.H=k.HCf.Dm.H@UHAUIATHHHHPHXL`Lht#)p)M)U)])e)m)u)}dH%(H81Lfh.LH Dž HHDž$0HH)HELd$H(H@IH0Lj@h.LIf.LH*CI4CfUHHSHHxn.MCHHDHD+HHH!HHJj.HtH[]HgdH%(HE1Hfo0gfo8gHfo size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_vector.hInvalid build with no providers availablestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::basic_string&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]out of indexinternal errorindex out of rangeRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundmax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkmax_power_of_two_extend_bytesInvalid key found: initial_growth_chunk_size_bytesvector::reservebasic_string::appendget() != pointer()typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IOBinding; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IOBinding&]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/unique_ptr.hbasic_string::resizebasic_string::assignbasic_string::replace was false.Stacktrace: Attempt to use DefaultLogger but none has been registered.static void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(onnxruntime::logging::Severity)/onnxruntime_src/include/onnxruntime/core/common/logging/logging.hInteger overflowstatic void SafeIntExceptionHandler::SafeIntOnOverflow()/onnxruntime_src/onnxruntime/core/common/safeint.hopaque(ml_type != nullptrnon_tensor_base != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccOpaque type is not a non_tensor type!!!OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)Not able to find appropriate IDataTransfer to copy sparse datastd::unique_ptr {anonymous}::GetDataTransfer(const OrtDevice&, const OrtDevice&)Trying to get a Tensor, but got: T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/ort_value.hthis API does not support stringslocation dimensions do not match shape sizecannot create std::vector larger than max_size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]invalid location rangeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]Trying to get a TensorSeq, but got: const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]IsTensorSequence()Trying to get a SparseTensor, but got: const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]the ort_value must contain a constructed tensorIsSparseTensor()UseCooIndicesOrtStatus* OrtApis::UseCooIndices(OrtValue*, int64_t*, size_t)UseCsrIndicesOrtStatus* OrtApis::UseCsrIndices(OrtValue*, int64_t*, size_t, int64_t*, size_t)UseBlockSparseIndicesOrtStatus* OrtApis::UseBlockSparseIndices(OrtValue*, const int64_t*, size_t, int32_t*)vector::_M_realloc_insert!this->empty()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtCustomOp*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtCustomOp*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtCustomOpDomain*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtCustomOpDomain*&]Map is missing type entry for its valuestatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = std::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Sequence is missing type entry for its elementstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const Cont&, size_t) [with T = std::map; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Can not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copytried creating tensor with negative value in shapeStrings can only reside in CPU memoryonnxruntime::SparseTensor& {anonymous}::ValidateFillInputArgs(OrtValue*, const onnxruntime::TensorShape&, const OrtMemoryInfo*)tried Filling sparse tensor with negative value in values shapeFillSparseTensorCooOrtStatus* OrtApis::FillSparseTensorCoo(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IDataTransfer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IDataTransfer&]FillSparseTensorCsrOrtStatus* OrtApis::FillSparseTensorCsr(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int64_t*, size_t)tried Filling sparse tensor with negative value in block sparse indices shapeOrtStatus* OrtApis::FillSparseTensorBlockSparse(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int32_t*)FillSparseTensorBlockSparsenot enough space: expected , got std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Output buffer allocation failed!=T Tensor type mismatch. T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]/onnxruntime_src/include/onnxruntime/core/framework/tensor.hutils::IsPrimitiveDataType(dtype_)input array is too shortgsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string]OrtValue should contain a Tensor or a Sparse TensorSparse Tensor does not contain sparse dataThis API supports Tensors or SparseTensorsshape is invalidbuffer size is too small for string elementelement index is out of boundsoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.index is out of boundsinput array doesn't equal tensor sizeconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]len >= 0 && static_cast(len) < std::numeric_limits::max()const T* onnxruntime::Tensor::Data() const [with T = long int]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]Tensor must always contain primitive types. Found: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Value type is not supported yet: value_type != nullptrconst T& OrtValue::Get() const [with T = std::vector, float> >]onnxruntime::DataTypeImpl::GetType() == type_const T& OrtValue::Get() const [with T = std::vector >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const Cont&, size_t) [with T = std::map, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Input is not of one of the supported sequence types.Input is not of type sequence or map.Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Key type is not supported yet.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Tensor sequence must contain only primitive typesvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/TensorSeq.hExpecting all elements to be tensors. Got: OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.TensorSeq: tensor to be added has a different data type.void onnxruntime::TensorSeq::Add(const OrtValue&)IsSameDataType(tensor.Get())const T& OrtValue::Get() const [with T = std::map, float>]const T& OrtValue::Get() const [with T = std::map]Input is not of one of the supported map types.elem_type_ != nullptrin[idx]->IsTensor()Unsupported input typeORT_LOAD_CONFIG_FROM_MODELonnxruntime_profile_const T& OrtValue::Get() const [with T = std::map]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long int&]Invalid index requested for map type.const T& OrtValue::Get() const [with T = std::map >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = float&]const T& OrtValue::Get() const [with T = std::map]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = double&]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = std::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, std::basic_string >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const Cont&, size_t) [with K = std::basic_string; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, long int>]const T& OrtValue::Get() const [with T = std::map, double>]const OrtValue& onnxruntime::TensorSeq::GetAt(size_t) constvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)i < tensors_.size()St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagPFvPvESt14default_deleteIN11onnxruntime18IExecutionProviderEESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEN3gsl15narrowing_errorE*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_*ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_St19_Sp_counted_deleterIPN11onnxruntime18IExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPvPFvS0_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE?onnxruntime_prof/\basic_string::substr%s: __pos (which is %zu) > this->size() (which is %zu)static void OrtEnv::Release(OrtEnv*)/onnxruntime_src/onnxruntime/core/session/ort_env.ccenv_ptr == p_instance_.get()N11onnxruntime7logging5ISinkE14LoggingWrapper_Map_base::atstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]LoadDynamicLibraryFromProvider/onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.ccFailed to load dynamic library TensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.CANN execution provider is not enabled in this build.oneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.__pos <= size()std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/basic_string.hstatic const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger()UnloadT* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]Please fetch output tensor with specified shape.T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.hp_ml_valueTensor type mismatch.type == dtype_const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) Max:node_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got:onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const/onnxruntime_src/include/onnxruntime/core/graph/graph.h is not present.Required output at index output_ptronnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&)Missing Input: Required input at index input_ptrconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::SparseTensor]basic_string::insertTensor size () != new size (void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)shape_.Size() == new_shape.Size()libonnxruntime_providers_shared.sovoid onnxruntime::ProviderSharedLibrary::Ensure()EnsureProvider_SetHostGetGetProvideronnxruntime::Provider& onnxruntime::ProviderLibrary::Get()OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared librarySessionOptionsAppendExecutionProvider_CANN: Failed to load shared librarySessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryTryGetProviderInfo_CUDATryGetProviderInfo_ROCMCUDA and/or ROCM execution provider is either not enabled or not available.void onnxruntime::TensorSeq::Add(onnxruntime::Tensor&&)void onnxruntime::TensorSeq::Add(OrtValue&&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&]IsSameDataType(tensor)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2FNUZ]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FNUZ]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FN]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2FNUZ]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FNUZ]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FN]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = bool]device_typeenable_vpu_fast_compiledevice_idnum_of_threadscache_dircontextenable_opencl_throttlingenable_dynamic_shapesnum_streamsSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared librarylibonnxruntime_providers_cuda.solibonnxruntime_providers_cuda_ut.solibonnxruntime_providers_cann.solibonnxruntime_providers_rocm.solibonnxruntime_providers_dnnl.solibonnxruntime_providers_openvino.solibonnxruntime_providers_tensorrt.solibonnxruntime_providers_migraphx.sotypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeArg; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeArg&]N11onnxruntime23NodeAttributes_IteratorEN11onnxruntime35TensorShapeProto_Dimension_IteratorEN11onnxruntime18Node__NodeIteratorEN11onnxruntime18Node__EdgeIteratorEN11onnxruntime12ProviderHostEN11onnxruntime40TensorShapeProto_Dimension_Iterator_ImplEN11onnxruntime28NodeAttributes_Iterator_ImplEN11onnxruntime23Node__NodeIterator_ImplEN11onnxruntime23Node__EdgeIterator_ImplEN11onnxruntime16ProviderHostImplESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE000102030405060708091011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798990C?C execution provider is not supported in this build. Provider options key/value cannot be emptyMaximum string length for a provider options key/value is 1024.Unknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'QNNOpenVINOSNPEXNNPACKWEBNNAZUREVitisAIinvalid node input count: , expect: invalid node output count' op_type:RegisterCustomOpNodeSchemastypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Node; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Node&]Unable to find schema for node. Domain:'/onnxruntime_src/onnxruntime/core/session/standalone_op_invoker.ccinvalid arg_num.virtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) constarg_num < static_cast(input_count_)InvokeOpAttribute type not supported yet.XG|GG|GGGLGCreateOpstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&]Kernel definition was not found for node Domain:'typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::KernelDef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::KernelDef&]N11onnxruntime10standalone23StandAloneKernelContextESt23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899matching node is/onnxruntime_src/onnxruntime/core/session/standalone_op_invoker.RegisterCustomOpkernel already mapped to existinCPUExecutionProviderSyncProvidersstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::shared_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]/onnxruntime_src/onnxruntime/core/session/IOBinding.ccSynchronizeInputsSynchronizeOutputsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::common::Status onnxruntime::IOBinding::BindInput(const string&, const OrtValue&)mapped_feed_names_.size() == feed_names_.size()BindInput it.second= index=Size mismatch:std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtDevice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::common::Status onnxruntime::IOBinding::BindOutputImpl(const string&, const OrtValue&, OrtDevice)mapped_output_names_.size() == output_names_.size()Size mismatchexecution_mode is not validgraph_optimization_level is not validOrtSessionOptions& OrtSessionOptions::operator=(const OrtSessionOptions&)/onnxruntime_src/onnxruntime/core/session/abi_session_options.ccRegisterCustomOpsLibraryFailed to unload handle for dynamic library Session config entry '' was not found.Output buffer is not large enough for session config entrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::FreeDimensionOverride; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::FreeDimensionOverride&]Input index: contains null pointerse/session/abi_session_options.ccEnv is nullOrtMemoryInfo is nullProvided allocator is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided OrtMemoryInfo is nullNo requested allocator available12OrtAllocatorN11onnxruntime10IAllocatorEN11onnxruntime16OrtAllocatorImplEN11onnxruntime34OrtAllocatorImplWrappingIAllocatorEN11onnxruntime34IAllocatorImplWrappingOrtAllocatorEFailed to fetch a stream hosting the requested resourceRequested resource does not exist::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input name::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typeOutput buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution provider::OrtKernelInfo cannot get a valid logger from its execution providerResult buffer is not large enough is not implementedComputeAsyncIsCompatibleinput count does not match input to be of optional type input to be of variadic type input to keep same arity input to be of single typeoutput count does not match output to keep same arity output to be of single type/onnxruntime_src/onnxruntime/core/session/custom_ops.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]custom op schemas mismatch, expecting input to keep same homogeneity output to be of optional type output to be of variadic type output to keep same homogeneitystd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::DataTypeImpl*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::DataTypeImpl* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Unsupported version '' in custom op 'onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&)custom op registered at runtimeOnly the last input to a custom op may be marked variadic.onnx::OpSchema onnxruntime::CreateSchema(const string&, const OrtCustomOp*)Only the last output to a custom op may be marked variadic.There 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.i == input_count - 1i == output_count - 11 == undefinedCreateCustomRegistrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::OpSchema&]N11onnxruntime8OpKernelEN3Ort9ExceptionEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_E*ZN11onnxruntime22CreateKernelCreateInfoERKSsPK11OrtCustomOpEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISB_EEE_*ZN11onnxruntime20CreateCustomRegistryEN3gsl4spanIKP17OrtCustomOpDomainLm18446744073709551615EEERSt10shared_ptrINS_14CustomRegistryEEEUlRN4onnx16InferenceContextEE_St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE22OrtDefaultCpuAllocatorCpuvector::_M_range_insertSchema error: No allocator for this device has been registered for sharing.An allocator for this device has already been registered for sharing. is not implemented in CreateAndRegisterAllocatorV2()tensor(uint16)tensor(int8)tensor(int16)tensor(bfloat16)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)tensor(float8e4m3fn)tensor(float8e4m3fnuz)tensor(float8e5m2)tensor(float8e5m2fnuz)seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16)) Target=[ShapeInferenceError] Mismatch between source and target type. Source=Unsupported Source/Target type=+II+I+III+I+IIITrying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange (domain: ) from file , but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Exception caught: FvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEE*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver1EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver2EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver3EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver4EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver5EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver6EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver7EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver8EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver9EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver10EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver11EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver12EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver13EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver14EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver15EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver16EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver17EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver18EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver19EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver1EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver2EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver3EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib20OpSet_Microsoft_ver1EEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib21OpSet_ONNX_DeprecatedEEEviEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime18internal_nhwc_onnx24OpSet_Internal_NHWC_ONNXEEEviEUlONS_8OpSchemaEE_Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for oReceived invalid value for arena extend strategy. Valid values can be either 0, seq(tensor(uint3seq(tensor(bfloacom.microsoft.excom.microsoft.ncorg.pytorch.atencom.ms.internal.e/session/enviroConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SessionState; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SessionState&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::shared_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]The provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceintra op thread pool must have at least one thread for RunAsyncsession.strict_shape_type_inferencesession.allow_released_opsets_onlyGetModelMetadataModel was not loadedModel was not loaded.GetModelInputsGetOverridableInitializersGetModelOutputsEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.offoperator()/onnxruntime_src/onnxruntime/core/session/inference_session.ccFlush-to-zero and denormal-as-zero are NewIOBindingSession was not initializedSession not initialized.Output vector pointer is NULLp_fetches->size(): Invalid Output Name:At least one output should be requested.Output vector incorrectly sized: output_names.size(): %Y-%m-%d_%H-%M-%S.jsonShrinkMemoryArenasUnable to shrink arena: OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:Device:[DeviceType: MemoryType: DeviceId: error message: Unexpected input data type. Actual: ()) , expected: (Cannot find execution provider Failed to load TuningResults (index=Correctly set TuningResults for SetTuningResultsInvalid TuningResults (index=). does not support TunableOp.). Reason: , enable TunableOp for usingInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr&)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid rank for input: Expected: for the following indices Please fix either the inputs or the model.Got invalid dimensions for input: Size mismatch: feed_names has is not expected to be of type tensor.Provided type is not an optional tensorconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalTensor(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.h is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Provided type is not an optional sequence tensorconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(onnxruntime::MLDataType)elements, but feeds has elements.Invalid Feed Input Name:Input with name: IsOptionalTensor(type)ValidateInputssparse_tensorIsOptionalSeqTensor(type)seqReceived nullptr for custom registryAddCustomOpDomainsvector::_M_default_appendLoadOrtModelBytesLoad model from failed. Only bytes were able to be read.session.use_ort_model_bytes_directlygpucpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: post_layout_transform_step__M_get() != nullptrstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::Model]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/shared_ptr_base.honnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph&, bool):: mutableFeedsFetchesInfo/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)AddPredefinedTransformers/onnxruntime_src/include/onnxruntime/core/common/parse_string.hParseStringWithClassicLocaleFailed to parse value: "T onnxruntime::ParseStringWithClassicLocale(std::string_view) [with T = long unsigned int; std::string_view = std::basic_string_view]RunReplaying the captured IsGraphCaptured()Running with tag: model_run CUDA Graph for this model with tag: onnxruntime::common::Status onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph()/onnxruntime_src/onnxruntime/core/session/inference_session.hCached EP instance for graph replay is not set yet before calling ReplayGraph()memory.enable_memory_arena_shrinkagestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtDevice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]disable_synchronize_execution_providersStart another run for necessary memory allocation or graph capture.Encountered unknown exception in Run()input name cannot be emptyoutput name cannot be emptyNULL input supplied for input onnxruntime::common::Status onnxruntime::InferenceSession::Run(const RunOptions&, gsl::span, gsl::span, gsl::span, gsl::span)fetch_unique_ptrs[i] != nullptrunknown exceptionProvider has already been registered./onnxruntime_src/onnxruntime/core/framework/execution_providers.hReceived nullptr for exec providerExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.QDQ quantization is not supported while using the DML Execution Provider. It is enabled within session options which may result in lower performance.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Can't register custom op domains with ORT for std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]RegisterExecutionProviderDmlExecutionProvidersession.disable_quant_qdqLoadWithLoaderException during loading: Unknown exceptionThis session already contains a loaded model.Encountered unknown exception in LoadWithLoader()model_loading_uri failed:ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoTransformGraphCastFloat16TransformerMemcpyTransformersession.debug_layout_transformationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>&] are '0' and '1'. FinalizeSessionOptionsis_model_proto_parsedstatus.IsOK()session.set_denormal_as_zerosession.force_spinning_stopConstructorCommonsession--intra-opsession.dynamic_block_basestoiDynamic block base set to !to.affinity_str.empty()to.custom_join_thread_fn-inter-opThe only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT configModelProto needs to be parsed to check for ORT config within itonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&)Could not finalize session options while constructing the inference session. Error Message: void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&)graph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinningsession.intra_op_thread_affinitiesAffinity string must not be emptycustom join thread function not set for intra op thread poolsession.inter_op.allow_spinningcustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()Failed to load model because protobuf parsing failed.LoadOrtModelWithLoaderORTMThis session has already been initialized.ORT model verification failed.Serialized version info is null. Invalid ORT format model.The old ORT format model (version ) has saved runtime optimizations. They will be ignored.The ORT format model version [] is not supported in this build Missing Model. Invalid ORT format model.session.use_ort_model_bytes_for_initializerstypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Model; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Model&]session.load_model_formatORTmodel_loading_arrayGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const PathString&)Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, std::istream&)onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int)Error during EndProfiling(): ~InferenceSessionUnknown error during EndProfiling()SaveToOrtFormatFailed to save ORT format model to file: InitializeInitializing session.session.use_env_allocatorssession.save_model_formatInvalid value for TensorrtExecutionProviderCUDAExecutionProviderMemcpyFromHostMemcpyToHostPartitionOrtFormatModelNchwcTransformer1024session_initializationapplySession has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.optimization.minimal_build_optimizations value of 'save' is only valid when saving an ORT format model.This session cannot use the CUDA Graph feature as requested by the user This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session cannot use the CUDA Graph feature as requested by the user as all compute graph nodes have not been partitioned to the CUDA EP.This model has shape massaging nodes that will execute on CPU. Use the CUDA Graph feature with caution. As long as the intermediate shapes produced in the model using the representative input used to capture the CUDA graph, will match the shapes produced in the model for other inputs of the same shape as the representative input (common case), it is safe to use the CUDA Graph feature.This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA Graph feature as requested by the user.session.disable_cpu_ep_fallbackConflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.ApplyOrtFormatModelRuntimeOptimizationsUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.session.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytesSession successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()St14default_deleteIN11onnxruntime5ModelEESt14default_deleteIN11onnxruntime20CPUExecutionProviderEESt18bad_variant_accessN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN8nlohmann6detail9exceptionE*ZN11onnxruntime16InferenceSession13LoadOnnxModelERKSsEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_*ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_*ZN11onnxruntime16InferenceSession10InitializeEvEUlRKN4onnx8OpSchemaEE0_*ZN11onnxruntime16InferenceSession8RunAsyncEPK13OrtRunOptionsN3gsl4spanIKPKcLm18446744073709551615EEENS5_IKPK8OrtValueLm18446744073709551615EEES9_NS5_IPSA_Lm18446744073709551615EEEPFvPvPSF_mP9OrtStatusESH_EUlvE_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE0_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlRKS1_E1_*ZN11onnxruntime12_GLOBAL__N_123PartitionOrtFormatModelERNS_5GraphERKNS_18ExecutionProvidersERNS_21KernelRegistryManagerERNS_12SessionStateEEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE_St19_Sp_counted_deleterIPN11onnxruntime20CPUExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime12CPUAllocatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEsession.strict_shape_type_inferee/session/infere@@Bvector::_M_insert_aux[json.exception.invalid_iteratortype_errorobjectbooleanbinarydiscardednumbermap::atcannot use at() with key '' not foundFMFMGMFMGMFMFMFMFMFMWJMKJMJMJMJMIMIMIM?JM3JM at line , column parse_errorparse errorD[M[M[M\M|[MD[MD[Ml[M[Mtrue literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected PbM`bMbM@bMaM`M`M`MbMaMaMaMaMaM@cM0bM bMfcMLbM\bMlbM|bM`M`M`MbMbMbMbMbMbMbM,bM::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>*&]kMlMTmMmMmMkMkMlM$lMvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >]iterator does not fit current valuestd::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator) [with _Key = std::basic_string; _Val = std::pair, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st, nlohmann::basic_json<> > >; _Compare = std::less; _Alloc = std::allocator, nlohmann::basic_json<> > >; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree, std::pair, nlohmann::basic_json<> >, std::_Select1st, nlohmann::basic_json<> > >, std::less, std::allocator, nlohmann::basic_json<> > > >::iterator]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_tree.hiterator out of range__position != end()cannot use erase() with tMsMsM{sMsMrMrMrMosMcsMtype must be number, but is ~M~M~M}M}M|M|M|M}Mu}MSequential modeParallel modetruesession_optionsintra_op_num_threadsSetIntraOpNumThreadsinter_op_num_threadsSetInterOpNumThreadsexecution_modeSetExecutionModeSetting execution_mode to graph_optimization_levelSetGraphOptimizationLevelenable_profilingSetEnableProfilingSetting enable_profiling to The Model Proto hasn't been checked for the ORT config json.ParseSessionOptionsFromModelProto/onnxruntime_src/onnxruntime/core/session/inference_session_utils.ccDid not find session options in the model file to be used while running the modelcannot compare iterators of different containersintra_op_num_threads option in the model file must be an integerUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to inter_op_num_threads option in the model file must be an integerUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integerUnsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integerSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integerUnsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = char&]invalid string: ill-formed UTF-8 byteinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001FMM֪MMMMҫMMMM~MiMKM6M!MMMMެMɬMMMMuM`MTM?M*MMM MMpMMM9MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM"MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(M(MhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMhMMMMMMMMMMMMMM'MMMMMMMMMMMMMM MMMMMMMMMMMMBMMMM0MMMkMinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator]invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literalMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMѲMMMMMMMMMMMMMMMMMMMM_MMMMM_M_M_M_M_M_M_M_M_M_MùMM]M(M]M]M]M]M]M]M]M]MM]M]M]M]M]M]M]MM]M]M]M]M]MM]M]M]M]M]M]MvM]MlMobject keyobject separatornumber overflow parsing 'MNN)NN~MN~MN~MNNNNNtuning_results. Ignoring...ParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelTuning results stored in the model file cannot be parsed. Error message: N8nlohmann6detail11parse_errorEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail10type_errorEN8nlohmann6detail12out_of_rangeEParsing RunOptions from ModelProto is not supporApply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApplyTransformers/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccThis transformer is already registered _RuleBasedTransformerExpandEliminationCastEliminationUnsupported optimization level: onnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&)/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccGenerateRuleBasedGraphTransformerstd::unique_ptr onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&, const onnxruntime::InlinedHashSet >&)QDQFinalCleanupTransformersession.enable_quant_qdq_cleanuponnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const SatApplyContextVariant&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)DoubleQDQPairsRemoverConstantSharingMatMulAddFusionReshapeFusionQDQPropagationTransformerROCMExecutionProviderRocmBlasAltImplTransposeOptimizerACLExecutionProvidersession.qdqisint8allowedArmNNExecutionProvidersession.x64quantprecisionQDQS8ToU8TransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionGatherToSplitFusionGatherToSliceFusionMatmulTransposeFusionBiasGeluFusionSkipLayerNormFusionFastGeluFusionQuickGeluFusionGeluApproximationBiasSoftmaxFusionBiasDropoutFusionMatMulScaleFusionMatMulActivationFusionAvx2WeightS8ToU8Transformersession.disable_double_qdq_removerCommonSubexpressionEliminationoptimization.enable_gelu_approximationonnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)N11onnxruntime11RewriteRuleEEliminateIdentitUnsqueezeEliminaEliminateDropoutPreShapeNodeElimGemmTransposeFusClipQuantRewriteReluQuantRewritesession.disable_session.qdqisintYieldOpstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime17EliminateIdentityEtensor(uint8)tensor(float16)idx < input_defs.size()bool onnxruntime::IsIsolatedFp16NodeOnCpu(const onnxruntime::Node&, onnxruntime::Graph&, const onnxruntime::KernelRegistry&)/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]RecurseApplyImpl/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.hInsertedCast_InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constdtype_attribute->second.has_i()N11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerERemoveDuplicateCCPUExecutionProvcast node to cast from float16 to float32 on cpuSubDivSqrtPowfused LayerNorm subgraphs epsilonstash_type/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.ccSimplifiedLayerNormalizationN11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionE    '7QLinearAveragePoolQLinearGlobalAveragePoolchannels_lastcom.ms.internal.nhwccom.microsofttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::NodeRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::NodeRef&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::GraphRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::GraphRef&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::basic_string_view; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string_view&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const std::vector*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = const std::vector*&]Layout/Transpose optimization for FN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setISsSt4hashISsESt8equal_toISsESaISsEEEPFN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setISsSt4hashISsESt8equal_toISsESaISsEEEFusedMatMul with activation _FusedActivationactivation_/onnxruntime_src/onnxruntime/core/optimizer/matmul_activation_fusion.ccN11onnxruntime22MatMulActivationFusionE FusedMatMulActivfused Matmul and Add /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccN11onnxruntime15MatMulAddFusionE /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.ccN11onnxruntime26MatMulIntegerToFloatFusionEMatMulIntegerToFstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::{anonymous}::ScaleMergeInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::ScaleMergeInfo&]Constant initializer NodeArg shape should not be null. NodeArg: std::optional onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.ccvoid onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int]Unsupported data type: GetScalarConstantInitializerdiv_inputs.size() == 2mul_inputs.size() == 2std::optional > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)_FusedMatMulAndScaleFused MatMul and Scalestd::vector onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)input_node.InputDefs().size() == 2 && scale_and_index->second < 2std::vector onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)output_node.OutputDefs().size() == 1N11onnxruntime17MatMulScaleFusionE8 bool onnxruntime::GetTransposePerms(const onnxruntime::Node&, std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_transpose_fusion.cctranspose_node.InputDefs().size() == 1cannot create std::deque larger than max_size()std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::front() [with _Tp = long unsigned int; _Alloc = std::allocator; std::deque<_Tp, _Alloc>::reference = long unsigned int&]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_deque.h!node_consumers.empty()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node*&]size_t onnxruntime::UpdateConsumerCount(onnxruntime::Graph&, onnxruntime::NodeArg*, onnxruntime::InlinedHashMap&)cast != nullptronnxruntime::Node* onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph&, onnxruntime::Node*, onnxruntime::InlinedHashMap&, std::deque&, bool&, bool&)N11onnxruntime21MatmulTransposeFusionECreated a new Cast node to interchange Cast and Created a new Transpose node to interchange Cast and Transpose nfused MatMul andMatMul_With_Trancom.microsoft.nchwcReorderOutputkernel_shapeNOTSETSAME_UPPERSAME_LOWERVALIDnearestasymmetricalign_cornersnearest_modescalescoordinate_transformation_modereorderReorderInputBuffer overflowvoid onnxruntime::NchwcTransformerImpl::TransformConv(onnxruntime::Node&)/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.ccoutput_channels <= nchwc_output_channelsreshapestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]_bn_nchwcspatialbn_scalebn_BBatchNormalizationGlobalMaxPoolN11onnxruntime16NchwcTransformerE     /onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.ccQLinearConvNhwcFusedConvN11onnxruntime15NhwcTransformerEQQQQQQQ"QQQQ&QN11onnxruntime15NoopEliminationEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long unsigned int&]N11onnxruntime14NotWhereFusionEN11onnxruntime23PreShapeNodeEliminationE DynamicQuantizeMatMulMatMulIntegerQLinearMatMul_s8_2_u8TryConvertDynamicQuantizeLSTM/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/avx2_weight_s8_to_u8.ccUnable transforming DynamicQuantizeLSTM operator, cannot locate recurrence tensor of const int8 type, int8 overflow might impact precision ! unable to locate recurrence tensor or its zero point value,N11onnxruntime27Avx2WeightS8ToU8TransformerEDequantizeLinearweight_zp_s8_2_ue/optimizer/qdq_transformer/avx2_weight_s8_to_u8TryConvertDynami ClipUnexpected data type for QuantizeLinear input y_zero_point of bool onnxruntime::GetQConstantLowerUpper(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/clip_quantizelinear.ccN11onnxruntime15ClipQuantFusionE 4/duplicatedAdded by EnsureUniqueDQForNodeUnit/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/ensure_unique_dq_for_node_unit.ccEnsureUniqueDQForEachExplicitOutputEdgeN11onnxruntime25EnsureUniqueDQForNodeUnitEEnsureUniqueDQFo with name "" and Cleaning up back-to-back nodes: /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_final_cleanup.ccN11onnxruntime26QDQFinalCleanupTransformerE*ZN11onnxruntime12_GLOBAL__N_119CleanUpNodeSequenceENS0_12NodeSequenceERNS_5GraphEmRKNS_7logging6LoggerEEUlRKSsE_transformer/qdq_final_cleanup.ccCleanUpNodeSequeQDQFinalCleanupTstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNode(const onnxruntime::Graph&, const onnxruntime::Node&, int)/onnxruntime_src/onnxruntime/core/graph/extended_graph_edge.hnode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()this->_M_is_engaged()Invalid node index node != nullptrSqueezeUnsqueezeconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge::NodeInfo; _Dp = std::_Optional_base]/opt/rh/devtoolset-11/root/usr/include/c++/11/optionalconst onnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const onnxruntime::Graph&, onnxruntime::graph_utils::ExtendedGraphEdge::End) conststatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput(const onnxruntime::Graph&, const onnxruntime::Node&, int)node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()_pre_qsrc_node || dst_nodeInserting Q/DQ pair between node ("", index: at NodeArg ""._q_to_dq_post_dqonnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEnd(onnxruntime::Graph&, onnxruntime::graph_utils::ExtendedGraphEdge::End) constonnxruntime::common::Status onnxruntime::{anonymous}::InsertQDQPair(onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&, onnxruntime::NodeArg&, onnxruntime::NodeArg*, const onnxruntime::logging::Logger&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccFailed to set op schema for added Q node.Failed to set op schema for added DQ node.At least one graph node must be specified in the propagation edge.PropagateQBackwardPropagateDQForwardconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge; _Dp = std::_Optional_base]N11onnxruntime25QDQPropagationTransformerE*N11onnxruntime12_GLOBAL__N_130GraphConstantInitializerGetterE Inserted by QDQPropagationTransfqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quant/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccN11onnxruntime20QDQS8ToU8TransformerEDequantizeLinearbool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const onnxruntime::Node&, const GetConstantInitializerFn&, bool&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_util.ccq_or_dq_input_defs.size() >= 2N11onnxruntime15ReluQuantFusionEN11onnxruntime28QDQSelectorActionTransformerEN11onnxruntime3QDQ20DropQDQNodesSelectorEN11onnxruntime3QDQ19DropDQNodesSelectorEN11onnxruntime3QDQ13UnarySelectorEN11onnxruntime3QDQ14BinarySelectorEN11onnxruntime3QDQ13WhereSelectorEN11onnxruntime3QDQ14MatMulSelectorEGlobalAveragePooQDQSelectorActiostd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Node* const&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Node* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::QDQ::NodeGroup; _Dp = std::_Optional_base]N11onnxruntime12NodeSelectorEN11onnxruntime3QDQ17NodeGroupSelectorEN11onnxruntime3QDQ24DropQDQNodeGroupSelectorEN11onnxruntime3QDQ23DropDQNodeGroupSelectorEN11onnxruntime3QDQ22UnaryNodeGroupSelectorEN11onnxruntime3QDQ23BinaryNodeGroupSelectorEN11onnxruntime3QDQ25VariadicNodeGroupSelectorEN11onnxruntime3QDQ21ConvNodeGroupSelectorEN11onnxruntime3QDQ22WhereNodeGroupSelectorEN11onnxruntime3QDQ23MatMulNodeGroupSelectorEN11onnxruntime3QDQ21GemmNodeGroupSelectorEN11onnxruntime3QDQ12BaseSelectorEN11onnxruntime3QDQ21InputVariadicSelectorEN11onnxruntime3QDQ22OutputVariadicSelectorEN11onnxruntime3QDQ12ConvSelectorEN11onnxruntime3QDQ12GemmSelectorE*ZNK11onnxruntime3QDQ24DropQDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKSsE_*ZNK11onnxruntime3QDQ23DropDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKSsE_QDQ node group cannot have DQ node that produces a graph output. DQ node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: , target node: transformer/selectors_actions/shValidateNodeGrouGetQDQSelectionsInstanceNormalizLayerNormalizatiBatchNormalizati/onnxruntime_src/onnxruntime/core/optimizer/quick_gelu_fusion.ccN11onnxruntime15QuickGeluFusionEFuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&]N11onnxruntime12FuseReluClipE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node::EdgeEnd*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node::EdgeEnd*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const int&]ConcatTrainingCannot replace concat node with initializer:Fused reshape node: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccTotal fused reshape node count: N11onnxruntime13ReshapeFusionE  e/optimizer/resh/onnxruntime_src/onnxruntime/core/optimizer/rocm_blas_alt_impl.ccN11onnxruntime15RocmBlasAltImplEApplyRulesOnNode/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::RewriteRule; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::RewriteRule&]N11onnxruntime25RuleBasedGraphTransformerEonnxruntime::Node* onnxruntime::NodesToOptimize::GetNode(size_t, bool) const/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/actions.ccCreateReplacementNodeRunForSaveFailed to set node op schema.Failed to remove node.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = gsl::not_null&]N11onnxruntime6ActionEN11onnxruntime11RemoveNodesEN11onnxruntime15MergeIntoTargetEN11onnxruntime14ReplaceWithNewEe/optimizer/selectors_actions/acNode index value is too large to save to ORT format model: onnxruntime::NodesToOptimize::ToIndices() const::/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.ccnode_idx <= NodesToOptimizeIndices::kEmptyNodeIndexA target node must be set.onnxruntime::NodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() consttarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices(gsl::span, onnxruntime::NodeIndex, gsl::span, int, int)::MoveInputOutputImplMoveInputOutputIndex out of ranNew node op (Not enough produced nodes in the runtime optimization record.) does not match produced node op in runtime optimization record (Too many produced nodes in the runtime optimization record.Existing registration with name void onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const string&, const OpVersionsMap&, std::unique_ptr, std::unique_ptr)/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.ccinserted_in_name_to_entrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = gsl::not_null&]ApplySelectorsAndActionsMatched typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Action; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Action&]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::NodesToOptimizeIndices; _Dp = std::_Optional_base]ApplySavedRuntimeOptimizationsApplying runtime optimization action Nodes to optimize are not valid, skipping action. for transformer Missing action N11onnxruntime25SelectorActionTransformerEctors_actions/selector_action_trSetOpSinceVersionForProducedNodeApplySavedRuntim_Float_CastSkipLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccN11onnxruntime19SkipLayerNormFusionECast Input to flfused SkipLayerN̼+N11onnxruntime14EliminateSliceE onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()MIGraphXExecutionProviderForEachWithIndexVitisAIExecutionProvider' doesn't support memcpy Execution type 'void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)ProcessInitializersN11onnxruntime17MemcpyTransformerE*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_Copy from/to hosai.onnxstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::TensorRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::TensorRef&]GatherpermIdentityallowzeroconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]start!(__hi < __lo)constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = long int]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_algo.hkeepdimspadsReduceSumnoop_with_empty_axesstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::basic_string_view; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::basic_string_view&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]startsLogSoftmaxHardmaxTileArgMaxArgMinReduceL2ReduceL1ReduceSumSquareReduceProdReduceMinReduceMaxReduceLogSumExpReduceLogSumCastLikeLessOrEqualGreaterOrEqualLessGreaterBitShiftPReluModAtanhAcoshAsinhAtanAcosAsinCoshSinhTanCosSinHardSwishCeluThresholdedReluSoftsignSignShrinkSeluIsNaNIsInfRoundHardSigmoidErfNegFloorCeilAbsReciprocalInstanceNormalizationMaxUnpoolGlobalLpPoolLRNGridSampleDepthToSpaceSpaceToDepthUnsupported ONNX opset: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Failed to get size of TensorProtovirtual size_t onnxruntime::ApiTensor::NumElements() const/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimization/ort_optimizer_api_impl.ccvirtual std::vector onnxruntime::ApiTensor::Data() constNo NodeArg found for name node_arg_ != nullptrvirtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) const provided for op: domain == kOnnxDomainsince_version.has_value()Transpose optimizer is expected to add only onnx domain ops. Domain: int onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map, int>&) domain not found in opset imports.opset_import_iter != domain_to_version_map.end()An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.Transpose Optimizer is adding an unexpected node: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string_view; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string_view&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&] does not match rank Permutation length out of bounds for shape Permutation entry virtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector&)perm.size() == gsl::narrow_cast(shape_proto->dim_size())0 <= p && p_int < shape_proto->dim_size()Added in transpose optimizerExisting destination type is not compatible with source type.virtual void onnxruntime::ApiGraph::CopyValueInfo(std::string_view, std::string_view)dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Failed to find initializer for name: virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector&)successTransposeInitializerFailed to find initializer to reshape with name virtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector&) to have different number of elementsCannot reshape initializer new_num_elts == old_num_eltsconst_transpose_optimizerN27onnx_transpose_optimization3api9TensorRefEN27onnx_transpose_optimization3api12ValueInfoRefEN27onnx_transpose_optimization3api7NodeRefEN27onnx_transpose_optimization3api8GraphRefEN11onnxruntime12ApiValueInfoEN11onnxruntime9ApiTensorEN11onnxruntime7ApiNodeEN11onnxruntime8ApiGraphENhwcMaxPoolstorage_ordercom.microsoft.QLinearSigmoidcom.microsoft.QLinearMulcom.microsoft.QLinearConcatcom.microsoft.QLinearAddcom.microsoft.QLinearReduceMeancom.microsoft.QLinearLeakyRelucom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearAveragePoolbad optional accessTranspose optimizer failed: /onnxruntime_src/onnxruntime/core/optimizer/transpose_optimizer.ccSt19bad_optional_accessN11onnxruntime18TransposeOptimizerEe/optimizer/transpose_optimizer.UnsqueezeElimination cannot remove node UnsqueezeElimination_N11onnxruntime20UnsqueezeEliminationEe/optimizer/unsqueeze_eliminatioUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::/onnxruntime_src/onnxruntime/core/optimizer/utils.ccRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialw+2>yE>Start ValidateGemmInitializerPass ValidateGemmInitializer/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.hGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedValidateUnidirMaskunidir mask is not constantunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.Pass CheckNodesInPathQq_transpose perm attribute not matchedStart CheckNodesInPathKk_reshape const not matchedPass CheckNodesInPathKk_transpose has not perm attributebool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector&, const std::vector&, const onnxruntime::logging::Logger&)input_indices.size() == expected_values.size() && input_indices.size() > 0Slice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:CheckSliceParametersExpected value:Start MatchGemmSubgraphFaild to match gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedPass MatchGemmSubgraphInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedStart MatchUnidirMaskSubgraphPass MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Start MatchInputMaskSubgraphFailed to find Softmax nodeFailed to find path for maskPass MatchInputMaskSubgraphOutput edge count not expected for SoftmaxOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedStart MatchInputMaskSubgraphDistilBertFailed to find reshape shape path 1Failed to find reshape shape path 2Pass MatchInputMaskSubgraphDistilBertExpandFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.gather indices not matched.CheckDistilBertReshapeShapeStart CheckNodesInPathVPass CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedreshape initializer value is not expectedStart MatchPastSubgraphFailed to match v_concatpast_v_gather indices != 1past_k_gather indices != 0Pass MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphStart FuseGptAttentionFaild to find path v to SplitFailed to find path for qFailed to find path for kFused Attention subgraphs num_headsunidirectionalmask_filter_valueFaild to find path to qkv_matmulCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT.qkv_weightsqkv_biasFuseSubGraphQKImplk root is not layer normConvertMaskToInt32/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccq root should be layer normalizationq_matmul and q_add shape not matchedk_matmul and k_add shape not matchedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Failed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32Failed to convert mask to int32FuseSubGraphFaild to find path vFuseSubGraphQKFused an attention node.FuseSubGraphQKDistilBertOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphshape of layer norm bias tensor not expectedTotal fused Attention node count: N11onnxruntime15AttentionFusionE@    Cast mask from iConvertMaskToIntBitmaskBiasDropoutBitmaskDropoutfused Add-Dropout-(Add) for /onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.ccN11onnxruntime17BiasDropoutFusionE /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccN11onnxruntime14BiasGeluFusionEfused Add and Ge is not in valid range [-axis fused into softmax(input + bias)/onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.ccint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)/onnxruntime_src/onnxruntime/core/providers/common.hIsAxisInRange(axis, tensor_rank)N11onnxruntime17BiasSoftmaxFusionEis_inner_broadcaN11onnxruntime15CastEliminationEcYeYcYdYcYcYdYOutputDefs().size()N11onnxruntime15ConstantFoldingE*ZNK11onnxruntime15ConstantFolding9ApplyImplERNS_5GraphERbiRKNS_7logging6LoggerEEUlRKSsE_e/optimizer/constypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::{anonymous}::InitializerValue; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::{anonymous}::InitializerValue&]Total shared scalar initializer count: N11onnxruntime15ConstantSharingEortshared_virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddRelu::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"Expected Conv then Add.Failed to get Clip min/max constants.virtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)N11onnxruntime20ConvActivationFusionE*N11onnxruntime12_GLOBAL__N_19selectors22ConvActivationSelectorE*N11onnxruntime12_GLOBAL__N_19selectors11ConvAddReluE*N11onnxruntime12_GLOBAL__N_17actions24FuseConvActivationActionE*N11onnxruntime12_GLOBAL__N_17actions15FuseConvAddReluE ConvActivationFuactivation_paramL>?virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_act_fusion.ccvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constN11onnxruntime23ConvAddActivationFusionE*N11onnxruntime12_GLOBAL__N_19selectors25ConvAddActivationSelectorE*N11onnxruntime12_GLOBAL__N_17actions27FuseConvAddActivationActionEConvAddActivatioNhwcFusedConvActconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.ccN11onnxruntime13ConvAddFusionEbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.ccN11onnxruntime12ConvBNFusionE mul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.ccN11onnxruntime13ConvMulFusionEpZZpZpZpZpZ=ZZpZpZZZN11onnxruntime12DivMulFusionEDoubleQDQRemoved_N11onnxruntime21DoubleQDQPairsRemoverE*ZN11onnxruntime21DoubleQDQPairsRemover15IsNodeRemovableERNS_5GraphERKmRmS5_S5_EUlRKSsE_#B ;CN11onnxruntime16EliminateDropoutE MatMulIntegerToFloatDynamicQuantizeLinear/onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.ccN11onnxruntime27DynamicQuantizeMatMulFusionEInput shape is unknown or not 2D, or data type unknown/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccInput data type is not int32 or int64CheckInputMatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeNonZeroConstantOfShapeRangeFailed to match Shape node. MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph._Int32Cast Input from int64 to int32fused EmbedLayerNorm subgraphs position_embeddingsInput id is not valid. Segment id is not valid. Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). FuseSubGraphDistilBertN11onnxruntime20EmbedLayerNormFusionEe/optimizer/embed_layer_norm_fusN11onnxruntime17ExpandEliminationEfused GPT2Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.ccN11onnxruntime14FastGeluFusionE'7=*BL?@@fast_gelu_outputonnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)/onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of Invalid free dimension override.The model has input '' with a fixed dimension size N11onnxruntime32FreeDimensionOverrideTransformerEFreeDimensionOverrideTransformerInvalid free dimension override.e/optimizer/free_dim_override_trSplit for Fused Gather nodesnum_outputsSqueezeAxesInitializer/onnxruntime_src/onnxruntime/core/optimizer/gather_fusion.ccSqueeze for Fused Gather nodesrange_input_defs.size() == 3unsqueeze_output_Unsqueeze_UnsqueezeAxesInitializerSliceAxesInitializerSlice for Fused Gather nodesvirtual onnxruntime::common::Status onnxruntime::GatherToSliceFusion::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constN11onnxruntime19GatherToSplitFusionEN11onnxruntime19GatherToSliceFusionESqueeze for Fuseunsqueeze_outputUnsqueeze for Fused Gather nodesBiasGelu/onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: N11onnxruntime17GeluApproximationEGelu approximatie/optimizer/gelu_approximation.cfused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.ccN11onnxruntime10GeluFusionE??ScaledTanhParametricSoftplusfused Gemm /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.ccN11onnxruntime20GemmActivationFusionE  other_sum_input != nullptrvirtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/gemm_sum_fusion.cc_sum_transformed!sum_input_movedvirtual onnxruntime::common::Status onnxruntime::GemmSumFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) constnew_gemm_input_defs.size() == 3new_gemm_output_defs.size() == 1gemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0graph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())N11onnxruntime13GemmSumFusionE  Fused Gemm with std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper&]N11onnxruntime19GemmTransposeFusionE Initializer must have a datatypeonnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&)/onnxruntime_src/onnxruntime/core/optimizer/initializer.ccutils::HasDataType(tensor_proto)model_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()Axis must be non-negativeaxis >= 0Invalid other(scalers) sizecalled_ == 1void onnxruntime::Initializer::scale_by_axis(const onnxruntime::Initializer&, int)void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()scalers.size() == 1 || scalers.size() == num_blocksgsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = double]Expecting the same data typeExpecting the same sizesize() == other.size()onnxruntime::Initializer& onnxruntime::Initializer::mul(const onnxruntime::Initializer&)data_type() == other.data_type()gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]onnxruntime::Initializer& onnxruntime::Initializer::sub(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::add(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::div(const onnxruntime::Initializer&)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeIndexInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeIndexInfo&]Tried to allocate without valid type information, ort_value index=allocator_ptr_Failed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const std::unordered_map, OrtValue>&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)TryCreateKernelstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::KernelRegistry; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::KernelRegistry]N11onnxruntime23OptimizerExecutionFrameE*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSK_EEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISs8OrtValueSt4hashISsESt8equal_toISsESaISt4pairIKSsSB_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSH_EEEUlRKNS_7NodeArgEmE_QLinearstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeAndMoveInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeAndMoveInfo&]N11onnxruntime19ReplaceWithNewFixedEN11onnxruntime3QDQ17QDQReplaceWithNewEN11onnxruntime3QDQ18ReplaceWithQLinearEN11onnxruntime3QDQ23UnaryReplaceWithQLinearEN11onnxruntime3QDQ24BinaryReplaceWithQLinearEN11onnxruntime3QDQ26VariadicReplaceWithQLinearEN11onnxruntime3QDQ22ConvReplaceWithQLinearEN11onnxruntime3QDQ23WhereReplaceWithQLinearEN11onnxruntime3QDQ21SplitReplaceWithQuantEN11onnxruntime3QDQ24MatMulReplaceWithQLinearEN11onnxruntime3QDQ20GemmReplaceWithQuantEinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1ro_point_uint8_b33f88f7-c464-43e3-8692-97ac832bbgenerated at run name shorterMake the EP:onnxruntime::GetAvailableExecutionProviderNames()::/onnxruntime_src/onnxruntime/core/providers/get_execution_providers.ccprovider.name.size() <= kMaxExecutionProviderNameLenOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderQNNExecutionProviderNnapiExecutionProviderJsExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderCANNExecutionProviderAzureExecutionProviderRegisterOnnxOperatorKernels/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxMLOperatorKernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constN11onnxruntime20CPUExecutionProviderE*ZN11onnxruntime20CPUExecutionProvider25CreatePreferredAllocatorsEvEUliE_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::shared_ptr&]N11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime15ProviderHostCPUEN11onnxruntime19ProviderHostCPUImplET1T2void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList]/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.hONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: utils::HasDataType(t_proto)SetValueFromTensorProto]^]]]],]]]D]T]l]]]Must have a valid input shape./onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: Must have a single dimensiont_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList]Must have a single dimension of 1N11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJlNS_9MLFloat16EfdasihtjmbEEEEEFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEE*N11onnxruntime12_GLOBAL__N_115ConstantOfShapeEPFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEEe/providers/cpu/generator/constant_of_shape_baseonnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()Invalid dtype of onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)info.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDOutput type not supported in this build: std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/random.hstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]_M_a <= _M_bCould not infer data type from input tensor with data type input count mismatch_M_stddev > _RealType(0)std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]Empty dimensions for input tensor/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccbatch_size is < 1num_classes is < 1num_samples is < 1MultinomialComputeInvalid data type of N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialEAC???0???Integer overflowʡE.@ʡE.@ʡE.ʡE.+eG?+eG???0C0C00??@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?f@f@_6.>_6.>9d?9d?t ?t ?@@start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:N11onnxruntime5RangeEdelta in Range operator can not onnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hmin_ <= max_min should be a scalar.min->Shape().IsScalar()max should be a scalar.max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = float]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long unsigned int]N11onnxruntime13clip_internal10Clip_6BaseIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_6Clip_6IfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime4ClipEN11onnxruntime6Clip_6IfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIfEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIdEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIaEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIhEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIiEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIjEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIlEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplImEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ CumSumonnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span, gsl::span)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::SliceIteratorBase::Init(gsl::span, gsl::span, gsl::span)dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()Axis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`starts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = float]dims.size() == extents_.size()Cannot apply CumSum operator on a scalar/onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cconnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = long int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = long int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = double]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = double]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEET`t``t`w`t``t`t`t`t``t`t`t`t`l`t`t`t`t`t`t`t```t`4`t`t`t``t`t`,`t`t`t`,`t`t`t`t`t`t`t`t``````d`t`ҟ```d`ԝ`d``D`ǟ```D``ݟ``t`t`t`t`t`t`t`t`t`t`t`t`t`t`t`t`t``t`t`t`t`t`T``,`t`t`t`t`t`t`t`t`t`t`t`t`t`t`t`ԝ```d`ԝ`d``d`d```d`ԝ`d`d`ԝ`4``D`t`t``ġ````D````````````9````````-`!``````N`````````````````O```e```````,`l`N``|`D`p`` ``Z````````````````````````ġ``````````````````,````,`l```````,`l``,``N`````Detonnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedX != nullptrN11onnxruntime3DetIfEEbasic_string::basic_stringEinsumDeviceCompute is not supported yettypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::EinsumEquationPreprocessor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::EinsumEquationPreprocessor&]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Tensor* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Einsum op: An implementation for the input type Missing 'equation' attributeonnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.hinfo.GetAttr("equation", &equation_).IsOK()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Tensor*&]FN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEN11onnxruntime6EinsumEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEPFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEPFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEPFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEEinsum op: There must be atleastThere was a problem acquiring temporary memory allocator in EinsEinsum op: The candidate output does not match the actual output's shapeonnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&, void*)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccoutput.SizeInBytes() == input.SizeInBytes()The rank of the input must match permutation size for Transposebool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span&)input_rank == permutation.size()The innermost dims should have the same dim value to parse the diagonal elementsstd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]Einsum op: Unsupported data type for Diagonal Length of permutation must match the rank of the input to be permutatedstd::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, onnxruntime::AllocatorPtr, void*, const Transpose&)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Tensor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Tensor&]Einsum op: Transpose failed: for input shape Cannot parse the diagonal elements along dims std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*)rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Data types of the inputs must match for MatMulstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]input_1.DataType() == input_2.DataType()Only 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr; onnxruntime::EinsumOp::DeviceHelpers::MatMul = std::function]onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccnum_broadcasted_indices < num_of_ellipsis_dims_The broadcasted dimensions of the inputs are incompatibledim_iter == rankvector::_M_fill_insertFound a '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Another operand has a dim value of Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]. The shape is: in the same dimensionstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::TensorShape&]Inputs have ellipses in them but the provided output subscript does not contain Found a '.' not part of an ellipsis in the output subscript provFound '.' not part of an ellipsis in the output subscript providThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased Output subscript contains repeat contains letters not seen in thNumber of subscripts in the input equation does not match number of input tensorEinsum subscripts string contains too many subscript labels when compared to the rank of the inpRank of the input must match number of subscript labels corresponding to the inpEinsum op: The candidate output cannot be reshaped into the op's outputvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cccandidate_output.Shape().Size() == output_shape.Size()Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rankleft_dim == right_dimThe override dims are not compatible with given tensor's shape. std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = int]left.Shape().Size() == left_shape_override.Size()right.Shape().Size() == right_shape_override.Size()Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsEinsum op: Input shapes do not alignstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::TensorShape&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = long int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = long int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = float]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = float]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = double]InputBroadcaster can only start at span boundary!void onnxruntime::InputBroadcaster::AdvanceBy(size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.hoffset % span_size_ == 0fmod(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccInvalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8 >::Compute(onnxruntime::OpKernelContext*) const::BroadcastLooper requires two tensors as input.void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper]BitwiseXorBitwiseOrBitwiseNotBitwiseAndaxis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)) for tensor of length:Invalid start/ending offset [Broadcast Output range [onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len) are not at boundary of span with size:start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::cur1 == end1cur_out == end_outonnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::LEFTRIGHTInvalid direction value of 'onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]'. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]fmod attribute must be true for floating point typesvoid onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if::value, void>::type = void]void onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = double; typename std::enable_if::value, void>::type = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext*) constUnsupported Y type: Unsupported X type: Tensor with shape information must be 1 dimensional.void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&)shape_data_tensor.Shape().GetDims().size() == 1ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&)!helper.HaveTwoTensorInputs()Must have 1 or more inputsinput_count >= 1data_1.Shape() == shapedata_n.Shape() == shapeonnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]All inputs must have the same shapeonnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]inputCount >= 1onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]/onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hinput_size < std::numeric_limits::max()onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&)onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]ElementWiseKernelonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]N11onnxruntime3PowEN11onnxruntime5Min_8EN11onnxruntime5Max_8EN11onnxruntime3NotEN11onnxruntime3AndEN11onnxruntime2OrEN11onnxruntime3XorEN11onnxruntime8functors26ElementWiseRangedTransformIfEEN11onnxruntime8functors3ExpIfEEN11onnxruntime3AddIfEEN11onnxruntime3AddIdEEN11onnxruntime3AddIiEEN11onnxruntime3AddIlEEN11onnxruntime3SubIfEEN11onnxruntime3SubIdEEN11onnxruntime3SubIiEEN11onnxruntime3SubIlEEN11onnxruntime3MulIfEEN11onnxruntime3MulIdEEN11onnxruntime3MulIiEEN11onnxruntime3MulIlEEN11onnxruntime3DivIfEEN11onnxruntime3DivIdEEN11onnxruntime3DivIiEEN11onnxruntime3DivIlEEN11onnxruntime8functors3AbsIfEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEEN11onnxruntime8functors26ElementWiseRangedTransformIdEEN11onnxruntime8functors3AbsIdEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEEN11onnxruntime8functors26ElementWiseRangedTransformIaEEN11onnxruntime8functors3AbsIaEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEEN11onnxruntime8functors26ElementWiseRangedTransformIsEEN11onnxruntime8functors3AbsIsEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEEN11onnxruntime8functors26ElementWiseRangedTransformIiEEN11onnxruntime8functors3AbsIiEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEEN11onnxruntime8functors26ElementWiseRangedTransformIlEEN11onnxruntime8functors3AbsIlEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEEN11onnxruntime8functors26ElementWiseRangedTransformIhEEN11onnxruntime8functors3AbsIhEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEEN11onnxruntime8functors26ElementWiseRangedTransformItEEN11onnxruntime8functors3AbsItEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEEN11onnxruntime8functors26ElementWiseRangedTransformIjEEN11onnxruntime8functors3AbsIjEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEEN11onnxruntime8functors26ElementWiseRangedTransformImEEN11onnxruntime8functors3AbsImEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEEN11onnxruntime8functors3NegIfEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEEN11onnxruntime8functors3NegIdEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEEN11onnxruntime8functors3NegIaEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEEN11onnxruntime8functors3NegIiEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEEN11onnxruntime8functors3NegIlEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEEN11onnxruntime8functors5FloorIfEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEEN11onnxruntime8functors5FloorIdEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIdEEEEN11onnxruntime8functors4CeilIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEEN11onnxruntime8functors4CeilIdEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIdEEEEN11onnxruntime8functors10ReciprocalIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEEN11onnxruntime8functors10ReciprocalIdEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEEN11onnxruntime8functors4SqrtIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEEN11onnxruntime8functors4SqrtIdEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEEN11onnxruntime8functors3ExpIdEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEEN11onnxruntime8functors3LogIfEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEEN11onnxruntime8functors3LogIdEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEEN11onnxruntime5Sum_6IfEEN11onnxruntime5Sum_6IdEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Sum_8IdEEN11onnxruntime5Max_6IfEEN11onnxruntime5Min_6IfEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIdEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime5EqualIdEEN11onnxruntime5EqualISsEEN11onnxruntime11LessOrEqualIfEEN11onnxruntime11LessOrEqualIdEEN11onnxruntime11LessOrEqualIiEEN11onnxruntime11LessOrEqualIlEEN11onnxruntime14GreaterOrEqualIfEEN11onnxruntime14GreaterOrEqualIdEEN11onnxruntime14GreaterOrEqualIiEEN11onnxruntime14GreaterOrEqualIlEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime10BitwiseAndIaEEN11onnxruntime10BitwiseAndIsEEN11onnxruntime10BitwiseAndIiEEN11onnxruntime10BitwiseAndIlEEN11onnxruntime10BitwiseAndIhEEN11onnxruntime10BitwiseAndItEEN11onnxruntime10BitwiseAndIjEEN11onnxruntime10BitwiseAndImEEN11onnxruntime10BitwiseNotIaEEN11onnxruntime10BitwiseNotIsEEN11onnxruntime10BitwiseNotIiEEN11onnxruntime10BitwiseNotIlEEN11onnxruntime10BitwiseNotIhEEN11onnxruntime10BitwiseNotItEEN11onnxruntime10BitwiseNotIjEEN11onnxruntime10BitwiseNotImEEN11onnxruntime9BitwiseOrIaEEN11onnxruntime9BitwiseOrIsEEN11onnxruntime9BitwiseOrIiEEN11onnxruntime9BitwiseOrIlEEN11onnxruntime9BitwiseOrIhEEN11onnxruntime9BitwiseOrItEEN11onnxruntime9BitwiseOrIjEEN11onnxruntime9BitwiseOrImEEN11onnxruntime10BitwiseXorIaEEN11onnxruntime10BitwiseXorIsEEN11onnxruntime10BitwiseXorIiEEN11onnxruntime10BitwiseXorIlEEN11onnxruntime10BitwiseXorIhEEN11onnxruntime10BitwiseXorItEEN11onnxruntime10BitwiseXorIjEEN11onnxruntime10BitwiseXorImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8ISsEEN11onnxruntime3ModE*ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO????????"?"?"?"?@K@K@K@K@@@@ɿɿɿɿ.4.4.4.4t.t.t.t.,F,F,F,FpMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** s7 s7 s7 s7nnnn*=*=*=*=@C@C̐̐@@;f?;f?Ó´?Ó´?RV??RV??i@i@.>r,@.>r,@MK1@MK1@~}c@~}c@&3T@&3T@!^Q@!^Q@% 7@% 7@ &@ &@3N,F@3N,F@9B.?9B.?KKKKKKKKCCCC????5?5?5?5?!=!=!=!=뽸뽸뽸%=%=%=%=O]O]O]O]>>>>P*P*P*P*L>L>L>L>>>>>r1?r1?r1?r1?FFFF@@ @ @pA-DT!<@         .@std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]c_shape is required if c_data is providedvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const onnxruntime::TensorShape*, T*) [with T = onnxruntime::MLFloat16; ptrdiff_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hc_shape != nullptrvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const onnxruntime::TensorShape*, T*) [with T = float; ptrdiff_t = long int]onnxruntime::GemmBase::GemmBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_base.hinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)left.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcastright.NumDimensions() == 2left[i] >= 0right[i] >= 0 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr >; _Alloc = std::allocator > >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr >&]void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const onnxruntime::TensorShape*, T*) [with T = double; ptrdiff_t = long int]N11onnxruntime8GemmBaseEZNK11onnxruntime4GemmIdE17ComputeActivationEPdlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime4GemmIfEEN11onnxruntime4GemmIdEEZN11onnxruntime10IAllocator13MakeUniquePtrIvEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPvE_ZNK11onnxruntime4GemmIfE17ComputeActivationEPflPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime13BufferDeleterE /onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=N11onnxruntime7HardmaxIfEEleft_num_dims and right_num_dims must be >= 1/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueonnxruntime::common::Status onnxruntime::MatMulComputeHelper::Compute(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&, bool, bool, bool, bool, bool)left_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsMatMul dimension mismatchM_ == 1 && N_ == 1 was false/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_SGEMM_DATA_PARAMS; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_SGEMM_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime6MatMulIfEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIlEEmath/matmul_helpN11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundIdEEKN11onnxruntime4SignE/onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13N11onnxruntime7SoftmaxIfEEN11onnxruntime7SoftmaxIdEESoftmaxCPU inputs N, D and N * D must be < std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int];TopKvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, unsigned int&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()op_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo&, int&)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&)op_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()value of k must not be negative] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatork argument [N11onnxruntime4TopKILi9EfEEN11onnxruntime4TopKILi9EdEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi10EdEEN11onnxruntime4TopKILi11EfEEN11onnxruntime4TopKILi11EdEEN11onnxruntime4TopKILi11EiEEN11onnxruntime4TopKILi11ElEE*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ 333333?input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k vk tensor should be a 1D tensor oatch, expected 1 input - the tensor to be procesArrayFeatureExtractorai.onnx.mlInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[) >=N11onnxruntime2ml23ArrayFeatureExtractorOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpISsEEBinarizerInput data with index: is NaNN11onnxruntime2ml11BinarizerOpIfEE%fCastMaponnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hinfo.GetAttr("map_form", &attr).IsOK()onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&)info.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_STRINGTO_INT64Invalid CAST_TO value of DENSE Expected DENSE or SPARSEInvalid PACK_MAP value of TO_FLOATconst T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]Negative index values are not permitted. First entry in map has index value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cccur_input == end_input || cur_input->first >= 0const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = std::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]stofonnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = float]stollonnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string; TTo = long int] Expected std::map or std::mapInvalid input type of value: Unexpected CAST_TO value of N11onnxruntime2ml7CastMapECategoryMapperInput of string must have output of int64Input of int64 must have output of string onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_strings", string_categories).IsOK()info.GetAttrs("cats_int64s", int_categories).IsOK()info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime2ml14CategoryMapperEDictVectorizeronnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.hinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, long int>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, float>]N11onnxruntime2ml16DictVectorizerOpISslEEN11onnxruntime2ml16DictVectorizerOpISsfEEN11onnxruntime2ml16DictVectorizerOpISsdEEN11onnxruntime2ml16DictVectorizerOpIlSsEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIldEEstring_vocabularint64_vocabularyFeatureVectorizerinputdimensions attribute must be providedonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hstatus.IsOK() && !input_dimensions_.empty()Number of inputs (input_tensor_ptr != nullptrInvalid input type:) does not match number of inputdimensions values (virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()N11onnxruntime2ml17FeatureVectorizerEImputerExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()Empty value of imputed values.std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Empty input dimensions.virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constInvalid typeN11onnxruntime2ml9ImputerOpEimputed_value_flimputed_value_inreplaced_value_freplaced_value_iLabelEncoderkeys_stringsvalues_stringskeys_floatsvalues_int64skeys_int64svalues_floatsInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)classes_stringsdefault_stringdefault_int64onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.hinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()However, the number of key is std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_valuesonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = std::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string]N11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml14LabelEncoder_2IfSsEEN11onnxruntime2ml14LabelEncoder_2ISsfEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2ISsSsEEN11onnxruntime2ml14LabelEncoder_2IffEEN11onnxruntime2ml14LabelEncoder_2IlSsEEN11onnxruntime2ml14LabelEncoder_2ISslEEN11onnxruntime2ml14LabelEncoder_2IllEELinearClassifiervoid onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SOFTMAXSOFTMAX_ZERO!coefficients_.empty()LOGISTICNONEonnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.ccScores output is incorrect size. Expected:void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span, ptrdiff_t, ptrdiff_t, ptrdiff_t, const std::vector&, const std::vector&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) constscores_output_data.size() >= scores_output_sizeInput shape needs to be at least a single dimension.void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = int]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = long int]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = double]Unsupported input element type of shape_size == out.size()N11onnxruntime2ml16LinearClassifierEZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_z@@3ֳclasslabels_striclasslabels_intsLinearRegressoronnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.ccinfo.GetAttr("targets", &num_targets_).IsOK()info.GetAttrs("coefficients", coefficients_).IsOK()Input shape had more than 2 dimension. Dims=Unsupported data type of N11onnxruntime2ml15LinearRegressorEonnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of N11onnxruntime2ml10NormalizerEOneHotEncoderUnknown Category and zeros = 0.One and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cctmp_cats_int64s.empty() || tmp_cats_strings.empty()num_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string]N11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpISsEEEmpty scale in attributes!scale_.empty()Scale size: () != (onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccscale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int]Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_N11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIiEESVMClassifierLINEARRBFPOLYonnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.hinfo.GetAttrs("kernel_params", kernel_params).IsOK()coefficients_.size() > 0onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.ccinfo.GetAttrs("rho", rho_).IsOK()proba_.size() == probb_.size()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0N11onnxruntime2ml9SVMCommonEN11onnxruntime2ml13SVMClassifierE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfLm18446744073709551615EEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_@ ף;vectors_per_clasSVMRegressoronnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.ccinfo.GetAttr("n_supports", &vector_count).IsOK()Invalid argumentUnexpected mode:ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPfE_N11onnxruntime2ml12SVMRegressorIfEEml/svmregressor.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = absl::lts_20220623::InlinedVector, 3, std::allocator > >; _Alloc = std::allocator, 3, std::allocator > > >; std::vector<_Tp, _Alloc>::reference = absl::lts_20220623::InlinedVector, 3, std::allocator > >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = absl::lts_20220623::InlinedVector, 6, std::allocator > >; _Alloc = std::allocator, 6, std::allocator > > >; std::vector<_Tp, _Alloc>::reference = absl::lts_20220623::InlinedVector, 6, std::allocator > >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::ScoreValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::ScoreValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]TreeEnsembleClassifier/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]ZsZsZsZsZsZsZsZsZsZshZsZsZsZsZs'\sZs[sZs[sZs=[sZsZsZsZs_s_ss_ss_ss_ss_ss_spsfsfssfs+sfssfssfsEsfssssXssHss8ss(sssssssssssMssssss/s"t"tt"tt"tt"tt"tt"t8t0t0t`t0tt0tt0tVt0tt0ttX dims is empty.void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hthis->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]predictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >; int64_t = long int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == 2void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20220623::InlinedVector, 6, std::allocator > >; int64_t = long int]base_valuesnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuesclass_idsclass_treeidsclass_nodeidsclass_weightsclasslabels_stringsnodes_hitrates_as_tensornodes_values_as_tensorclass_weights_as_tensornodes_missing_value_tracks_trueclasslabels_int64sbase_values_as_tensorTreeEnsemble only works on 1D, 2D tensors.void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = float; ThresholdType = float; OutputType = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.hOne path in the graph requests feature features. but input tensor has void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = float; ThresholdType = float; OutputType = float]Unknown aggregation function in TreeEnsemble.onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = float; ThresholdType = float; OutputType = float]computeonnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = int; ThresholdType = float; OutputType = float; std::string = std::basic_string; int64_t = long int]nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_modes.size() < std::numeric_limits::max()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement&](coor.node_id >= 0 && coor.node_id < n_nodes_)A node cannot point to itself: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement*&]n_targets_or_classes > 0AVERAGESUMMINbase_values.empty()BRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQ is already there. in tree Node (truenode).Unable to find node (falsenode). (weights).Target is missing for leaf truenode_ids[i] != ifalsenode_ids[i] != ibase_values_as_tensorInittarget_weights_as_tensortarget_weightstarget_treeidstarget_nodeidstarget_idspost_transformn_targetsaggregate_functiononnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = long int; ThresholdType = float; OutputType = float; std::string = std::basic_string; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = double; ThresholdType = double; OutputType = float; std::string = std::basic_string; int64_t = long int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement*&]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const string&, const std::vector&, const std::vector&, int64_t, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector >&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const string&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&) [with InputType = float; ThresholdType = float; OutputType = float; std::string = std::basic_string; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]classlabels_int64sonnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMaxIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPoo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is specified but is empty.' must be a vector.Attribute 'n_dims == 1'.(for attribute 'Unexpected type (n_elements > 0onnxruntime::common::Status onnxruntime::ml::GetNumberOfElementsAttrsOrDefault(const onnxruntime::OpKernelInfo&, const string&, onnx::TensorProto_DataType, size_t&, onnx::TensorProto&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_helper.ccproto.data_type() == proto_type' has one dimension but is empty.onnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const string&, onnx::TensorProto_DataType, std::vector&) [with TH = double; std::string = std::basic_string](std::is_same::value)GetVectorAttrsOrDefault not implemented for type GetVectorAttrsOrDefaultonnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const string&, onnx::TensorProto_DataType, std::vector&) [with TH = float; std::string = std::basic_string](std::is_same::value)TreeEnsembleRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double]target_weightsbase_values_as_tensorN11onnxruntime2ml21TreeEnsembleRegressorIfEEN11onnxruntime2ml21TreeEnsembleRegressorIdEEZipMapMust provide classlabels_strings or classlabels_int64s but not both.onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector, float> >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map, float>; _Alloc = std::allocator, float> >; std::vector<_Tp, _Alloc>::reference = std::map, float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]T* OrtValue::GetMutable() [with T = std::vector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::map&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Input features_per_batch[] != number of classlabels[N11onnxruntime2ml8ZipMapOpEclasslabels_int6No kernel shape is set.kernel_shape_[dim] > 0onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.hinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()Pad should be smaller than kernel.pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Unsupported pooling size. inferred output shape:Input dimension cannot be less than 3.Index tensor shape should be same as that of the input data tensor to unpool./onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccShape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape:N11onnxruntime9MaxUnpoolEInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != dimension != Invalid input scale: Invalid input B: Invalid input mean: Invalid input var: Training mode only supports spatial BNonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.his_spatial_onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double]Invalid number of outputs for BN trainingonnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = float]!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = double]N11onnxruntime9BatchNormIfEEN11onnxruntime9BatchNormIdEEfff?std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IAllocator; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::IAllocator]Divide by zerostatic void SafeIntExceptionHandler::SafeIntOnDivZero()X num_dims does not match W num_dims.Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group. X: C: kernel channels: group: M: Unknown AutoPadType Stringonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.hauto_pad == AutoPadType::NOTSETNot enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.ComputePadAndOutputShapeInferPadsAndOutputShapeInvalid input shape: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. kernel_shape: channels_last: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.ccoutput and sum shape must matchN11onnxruntime4ConvIfEE`InlinedVector::at(size_type)` failed bounds checkvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const TensorShapeVector&, const TensorShapeVector&, const TensorShapeVector&, const TensorShapeVector&, int64_t, onnxruntime::ConvAttributes::ConvPadVector*, onnxruntime::TensorShapeVector*) const*out_size >= 0dim_size > 0filter number not equal to input channel number.Input channels is not divisible by group.group count is <= 0 filter_number: num_input_channels: PrepareForComputeDoConvTranspose/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccN11onnxruntime13ConvTransposeIfEEratio input should have a single value.float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.hratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fgsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]X and mask should have the same shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float]!mask || mask->Shape() == X_shapefloat onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double]N11onnxruntime7DropoutIffEEN11onnxruntime7DropoutIfdEEN11onnxruntime7DropoutIdfEEN11onnxruntime7DropoutIddEEFlattenonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hinfo.GetAttr("axis", &axis_).IsOK()The rank of input tensor must be >= axisvirtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisN11onnxruntime7FlattenEInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. onnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.ccN11onnxruntime12InstanceNormIfEEN11onnxruntime9LayerNormEonnxruntime::LayerNormImpl::LayerNormImpl(const onnxruntime::OpKernelInfo&, bool, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/layer_norm_impl.ccop_kernel_info.GetAttr("axis", &axis_).IsOK(). Size of scale and bias (if provided) must match this. Got scale size of Size of X.shape()[axis:] == and bias size of ComputeImplN11onnxruntime13LayerNormImplE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIffEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIddEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIdfEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_LpNormalizationonnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = double]N11onnxruntime6LpNormIfEEN11onnxruntime6LpNormIdEEonnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr("size", &size).IsOK()info.GetAttr("beta", &beta_).IsOK()size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fonnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4N11onnxruntime3LRNIfEEN11onnxruntime8functors4PowxIfEE t@void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()auto_padstridesceil_modedilationscount_include_padkernel_shape[dim] > 0onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const string&, int)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()onnxruntime::LpPoolV18::LpPoolV18(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.hvoid onnxruntime::PoolAttributes::InferOutputSize(gsl::span, onnxruntime::TensorShapeVector*, onnxruntime::TensorShapeVector*) constvoid onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constinput_dims.size() >= 2Unsupported AutoPad Type.Invalid input shape. Only N can be zero. Got:onnxruntime::TensorShapeVector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, onnxruntime::TensorShapeVector*) constinput_shape.Size() > 0 || input_shape[0] == 0/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cckernel_shape num_dims is not compatible with X num_dims.Unsupported kernel dimension : Unsupported pooling size : N11onnxruntime8PoolBaseEN11onnxruntime12LpPool1DTaskIfEEN11onnxruntime12LpPool2DTaskIfEEN11onnxruntime12LpPool3DTaskIfEEN11onnxruntime10Pool1DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool2DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool3DTaskIfNS_6LpPoolEEEN11onnxruntime17AveragePool1DTaskIfEEN11onnxruntime17AveragePool2DTaskIfEEN11onnxruntime17AveragePool3DTaskIfEEN11onnxruntime9MaxPoolV8EN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime14AveragePoolV19IfEEN11onnxruntime4PoolIfNS_6LpPoolEEEN11onnxruntime9LpPoolV18IfEEN11onnxruntime13MaxPool1DTaskIfEEN11onnxruntime13MaxPool2DTaskIfEEN11onnxruntime13MaxPool3DTaskIfEEN11onnxruntime13MaxPool1DTaskIdEEN11onnxruntime13MaxPool2DTaskIdEEN11onnxruntime13MaxPool3DTaskIdEEN11onnxruntime13MaxPool1DTaskIaEEN11onnxruntime13MaxPool2DTaskIaEEN11onnxruntime13MaxPool3DTaskIaEEN11onnxruntime13MaxPool1DTaskIhEEN11onnxruntime13MaxPool2DTaskIhEEN11onnxruntime13MaxPool3DTaskIhEE   MaxRoiPoolonnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.hinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scale_ > 0onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.ccR->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizeN11onnxruntime7RoiPoolIfEEonnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()N11onnxruntime6ShrinkE_StringNormalizer:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::Locale(const string&)::/onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.ccWCHAR_TInput contains invalid utf8 charsSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)attribute case_change_action is not setattribute case_change_action has invalid valueStopword contains invalid utf8 charsDuplicate stopwords not allowedEmpty stopwords not allowed!sw.empty()p.secondwstr != wconv_errorConversion Erroren_US.UTF-8Conversion ErrorN11onnxruntime16StringNormalizerEis_case_sensitivcase_change_actiTfIdfVectorizertypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::TfIdfVectorizer::Impl; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::TfIdfVectorizer::Impl&]Input shape must have either [C] or [B,C] dimensions with B > 0.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]p.first->second->id_ == 0Duplicate ngram detected, size: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = gsl::details::span_iterator; Map = std::unordered_map > >; size_t = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.ccsize_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::basic_string; ForwardIter = __gnu_cxx::__normal_iterator >*, std::vector > > >; Map = std::unordered_map >, std::unique_ptr > >, std::hash >, std::equal_to > >; size_t = long unsigned int]mode is requiredTFIDFmode: min_gram_length is requiredimpl_->min_gram_length_ > 0 >= max_skip_count is requiredimpl_->max_skip_count_ >= 0 <= must be of equal size but ngram_indexes size: Got weights of size: !pool_strings.empty()pool_int64s-grams(items % ngram_size == 0)TFonnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: Non-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()std::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings must not be empty if specifiednon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole Negative ngram_indexes values are not allowedN11onnxruntime15TfIdfVectorizerE*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_GGGGstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr&]NonMaxSuppressioncenter_point_box only support 0 or 1onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h0 == center_point_box_ || 1 == center_point_box_iou_threshold must be in range [0, 1].static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccboxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.output != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SelectedIndex; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SelectedIndex&]void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Sequence = std::vector; _Compare = std::less]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_queue.hvirtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionEcenter_point_boxobject_detection/non_max_suppresGetThresholdsFroRoiAlignstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]avgInvalid mode of value sampling_ratio_ >= 0will be different. specified. It should be either avg or maxonnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was The existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign Null input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchN11onnxruntime12RoiAlignBaseE*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEE coordinate_transOptionalHasElementOptionalGetElementOptionalOptional op must have a TypeProto in the 'type' attribute if the attribute is presentonnxruntime::Optional::Optional(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.hattr->has_tp()PropagateInputOrtValueToFirstOutput/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::OpKernelInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::OpKernelInfo&]can only be of type(tensor) or (seq(tensor))The TypeProto attribute in the Optional op N11onnxruntime8OptionalEN11onnxruntime18OptionalHasElementEN11onnxruntime18OptionalGetElementEOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptaTrying to use OptionalGetElement on an optional type OrtValue which contains no T3ConvIntegerMust be a scalar or 1D tensor or size 1.virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/conv_integer.ccIsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)N11onnxruntime11ConvIntegerEonnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/dynamicquantizelinear.ccx_ptr != nullptrZN11onnxruntime24GetQuantizationParameterIhLb0ELb0ELi0EEEvPKflRfRT_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime21DynamicQuantizeLinearIhEE@@p@@`@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.MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/matmul_integer.ccIsScalarOr1ElementVector(a_zero_point)MatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_GEMM_QUANT_DATA_PARAMS; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_GEMM_QUANT_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime17MatMulIntegerBaseEN11onnxruntime13MatMulIntegerET4onnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/qlinearconv.ccprepacked_buffers[0].get() == nullptronnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = signed char]QLinearConv : input zero point must be a scalar or 1D tensor of size 1static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = signed char; int64_t = long int; uint8_t = unsigned char]IsScalarOr1ElementVector(X_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be sameW_zero_point_data[i] == W_zero_point_valueQLinearConv : input scale must be a scalar or 1D tensor of size 1static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = unsigned char; int64_t = long int]IsScalarOr1ElementVector(X_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = signed char; int64_t = long int]static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = unsigned char; int64_t = long int; uint8_t = unsigned char]ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE0_N11onnxruntime11QLinearConvIhEEN11onnxruntime11QLinearConvIaEE@saturatex_zero_point must be null or a scalar or 1D tensor or size 1.void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&)/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear.cczero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })DequantizeLinear into BFLOAT16 is not implemented yet.DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]Unsupported input type.onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime16DequantizeLinearIaEEN11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIiEEN11onnxruntime16DequantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime16DequantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime16DequantizeLinearINS_10Float8E5M2EEEN11onnxruntime16DequantizeLinearINS_14Float8E5M2FNUZEEEN11onnxruntime14QuantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime14QuantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime14QuantizeLinearINS_10Float8E5M2EEEN11onnxruntime14QuantizeLinearINS_14Float8E5M2FNUZEEEp@`@????????????????oooooooooooooooo77777777@@@@wwwwwwwwwwwwwwww::::;;;;;;;;````std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&]QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear_matmul.ccIsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]27MLAS_QGEMM_OUTPUT_PROCESSOR35MLAS_QGEMM_REQUANT_OUTPUT_PROCESSORN11onnxruntime13QLinearMatMulEmust be overloaded.void onnxruntime::ValidateMustBeOverloaded()/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccReduction on all axes, output size should be 1.void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1Axes input is nullaxes_tensor != nullptrvoid onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor*)An axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1Only works on matrices with two dimensions.void onnxruntime::ValidateFastReduceKR(const gsl::span&, const onnxruntime::Tensor&)fast_shape[0] == output.Shape().Size()fast_shape.size() == 2Output size mismatch.void onnxruntime::ValidateFastReduceRK(const gsl::span&, const onnxruntime::Tensor&)fast_shape[1] == output.Shape().Size()Only works on matrices with three dimensions.void onnxruntime::ValidateFastReduceKRK(const gsl::span&, const onnxruntime::Tensor&)fast_shape[0] * fast_shape[2] == output.Shape().Size()fast_shape.size() == 3void onnxruntime::ValidateFastReduceRKR(const gsl::span&, const onnxruntime::Tensor&)void onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape&, int64_t)select_last_indexonnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = true]/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = false]N11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime16ReduceKernelBaseILb1EEEZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMaxIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm184467440737095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1onnxruntime19ReduceAggregatorSumIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime16ReduceAggregatorIffE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFfPKfEESF_IFvRfSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIiiE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFiPKiEESF_IFvRiSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIddE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFdPKdEESF_IFvRdSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIllE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFlPS7_EESF_IFvRlSG_lEEEUlllE_noop_with_empty_select_last_inde  @GRUlinear_before_resethidden_sizeactivationsactivation_alphaactivation_betaclipclip_ > 0.fforwardreverselayoutlayout_ == 0onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast(num_directions_) * 2Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.Can not get unpacked span from prepacked weightsgsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [with T = float]!is_prepacked_void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = gsl::details::span_iterator; TSpanBIter = gsl::details::span_iterator; TSpanCIter = gsl::details::span_iterator]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]cur + size <= endT* onnxruntime::rnn::detail::SafeRawPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]offset + size <= size_t(span.size())T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = gsl::details::span_iterator; TSpanBIter = gsl::details::span_iterator; TSpanCIter = gsl::details::span_iterator]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::rnn::detail::ActivationFuncs::Entry&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]GRU operator does not support double yetInvalid data type for GRU operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPiE_??onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.hactivation_func_names.size() == static_cast(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccPrePackN11onnxruntime8LSTMBaseEN11onnxruntime13DeepCpuLstmOpEactivation_alphaInput X must have 3 dimensions only. Actual:Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {Input initial_c must have shape {Input B must have shape {}. Actual:Input P must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.ccRNN/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.ccAffineonnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()FffffEN11onnxruntime3RNNIfEEPFffffEExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector::const_iterator&, const const_iterator&, std::vector::const_iterator&, const const_iterator&, float&, float&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccvoid onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)weights.quant_para_affineleakyreluthresholdedreluscaledtanhhardsigmoidsoftplussoftsignInvalid activation function of void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)Invalid LSTM merge activation function of void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)Input W must have shape {Input R must have shape {std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::rnn::detail::ActivationFuncs::Entry&]%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y; A7.A #<L>??const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = const float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIfEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIhEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPhE_ConcatFromSequenceMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.hGot nullptr for sequence input.virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccN11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceESplitToSequenceSequenceConstructSequenceEraseSequenceInsertSequenceAtSequenceEmptySequenceLengthUnsupported 'dtype' value: virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccM1,g= 1int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&)Invalid sequence index () specified for sequence of size (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector&)int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)Split should be > 0split_scalar > 0onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float]Invalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string]SplitToSequence operator does not support N11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEDFTSTFTUnsupported bit size.T onnxruntime::bit_reverse(T, unsigned int) [with T = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/dft.ccdft_bluestein_z_chirpdiscrete_fourier_transformT onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/utils.hUnsupported input data type of tensor->Shape().Size() == 1dft_length must be a scalar value.dft_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::common::Status onnxruntime::discrete_fourier_transform(onnxruntime::OpKernelContext*, int64_t, bool, bool)signal shape must end in 1 (real) or 2 (real, imaginary).onnxruntime::common::Status onnxruntime::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = float; U = float]signal_components == 1 || signal_components == 2If both frame_length and window are set, then the size of the window must be equal to the frame_length.Ensure that the dft size is smaller than the signal.onnxruntime::common::Status onnxruntime::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = float; U = std::complex]Unsupported 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.virtual onnxruntime::common::Status onnxruntime::STFT::Compute(onnxruntime::OpKernelContext*) constonnxruntime::common::Status onnxruntime::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = double; U = double]onnxruntime::common::Status onnxruntime::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = double; U = std::complex]frame_length == window_lengthwindow_size < signal_sizeshort_time_fourier_transformN11onnxruntime3DFTEN11onnxruntime4STFTE@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?-DT!@-DT!-DT! @@I@BlackmanWindowHannWindowHammingWindowMelWeightMatrixT onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = float]lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned char; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/window_functions.cclowest_index >= 0 && lowest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = float; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = double; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = signed char; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = int; int64_t = long int]N11onnxruntime26VariableOutputDataTypeBaseEN11onnxruntime10HannWindowEN11onnxruntime13HammingWindowEN11onnxruntime14BlackmanWindowEN11onnxruntime15MelWeightMatrixE@F@$@8>d! ? ף=> >stodAttribute to is not set.onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccAttribute saturate is only used for cast to float 8 types.-INF%.8gstoull*N11onnxruntime12_GLOBAL__N_14CastEOOOOAAAAxxxxxxxxxxxxxxxx<<<<||||||||||||||||pppppppppppppppp8888Col2Imonnxruntime::Col2Im::Col2Im(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.hstrides_.empty()dilations_.empty()pads_.empty()size of 'dilations' attribute, if provided, should equal to the number of image dimmensions.onnxruntime::common::Status onnxruntime::Col2Im::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.ccdilations_.size() == image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.strides_.size() == image_dim_numberN11onnxruntime6Col2ImIfEECompressvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()N11onnxruntime8CompressEonnxruntime::StridedCopy >(onnxruntime::concurrency::ThreadPool*, std::basic_string*, const TensorShapeVector&, const onnxruntime::TensorShape&, const std::basic_string*, const TensorShapeVector&)::/onnxruntime_src/onnxruntime/core/framework/copy.hlast >= firstonnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, unsigned int*, const TensorShapeVector&, const onnxruntime::TensorShape&, const unsigned int*, const TensorShapeVector&)::onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, long unsigned int*, const TensorShapeVector&, const onnxruntime::TensorShape&, const long unsigned int*, const TensorShapeVector&)::onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, short unsigned int*, const TensorShapeVector&, const onnxruntime::TensorShape&, const short unsigned int*, const TensorShapeVector&)::onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, unsigned char*, const TensorShapeVector&, const onnxruntime::TensorShape&, const unsigned char*, const TensorShapeVector&)::counter.current_offset == last/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cconnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) constRanks of input data are different, cannot concatenate them. expected rank: Non concat axis dimensions must match: Axis has mismatched dimensions of Cannot concatenate scalars got: input_rank == reference_rankData type mismatchsrc and dst must have same shape and not be rank 0.void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const TensorShapeVector&, const onnxruntime::TensorShape&, const T*, const TensorShapeVector&) [with T = std::basic_string; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()copy shape must have non-negative sizetotal_num_elements_to_copy >= 0void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const TensorShapeVector&, const onnxruntime::TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned int; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const TensorShapeVector&, const onnxruntime::TensorShape&, const T*, const TensorShapeVector&) [with T = long unsigned int; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const TensorShapeVector&, const onnxruntime::TensorShape&, const T*, const TensorShapeVector&) [with T = short unsigned int; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]void onnxruntime::StridedCopy(onnxruntime::concurrency::ThreadPool*, T*, const TensorShapeVector&, const onnxruntime::TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned char; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]src and dst types must matchonnxruntime::common::Status onnxruntime::DispatchStridedCopy(onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, std::ptrdiff_t, const TensorShapeVector&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >; std::ptrdiff_t = long int; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]dst.DataType() == src.DataType()N11onnxruntime6ConcatEZN11onnxruntime11StridedCopyISsEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyISsEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2022062313InlinedVectorIlLm5ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long int&; std::size_t = long unsigned int]ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_N11onnxruntime6ExpandIfEEN11onnxruntime6ExpandIdEEN11onnxruntime6ExpandIaEEN11onnxruntime6ExpandIsEEN11onnxruntime6ExpandIiEEN11onnxruntime6ExpandIlEEN11onnxruntime6ExpandIhEEN11onnxruntime6ExpandItEEN11onnxruntime6ExpandIjEEN11onnxruntime6ExpandImEEN11onnxruntime6ExpandIbEEN11onnxruntime6ExpandINS_9MLFloat16EEEinvalid expand sEyeLikeN11onnxruntime7EyeLikeEEyeLike : Input tensor dimensionTindMissing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.hindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccN11onnxruntime10GatherBaseEN11onnxruntime6GatherEZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_Gather Tind type not supported iGatherElementsGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hIndex out of rangeint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = int; int64_t = long int; size_t = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = long int; int64_t = long int; size_t = long unsigned int]GatherElements op: Unsupported tensor type, size:void onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with Tin = int; int64_t = long int]GatherElements op: Out of range value in index tensorvoid onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with Tin = long int; int64_t = long int]N11onnxruntime14GatherElementsE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clISsKSsEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clISsKSsEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_GatherElements op: Cannot operate on scalar inpup: Rank of input 'data' needs to be equal to rank of input 'indip: Data type of input 'data' should match the data type of the oGatherNDinvalid index found, index = GatherNDBase PrepareForCompute: Input count mismatchvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrindices tensor data type not supportedN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE*ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_indices tensor must has rank larlast dimension of indices must not be larger than rank of input bicubiczerosborderreflectionpadding_mode "bilinear" not supported, expect bilinear, nearest or bicubiconnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.hmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"" not supported, expect zeros, border or reflectionpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = float] does not match input batch size onnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.ccOnly 4-D tensor is supportedGrid batch size grid_dims[0] == N, expect 2Last dimension of grid: grid_dims[3] == 2ZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime10GridSampleIfEE@@??@@@p@@?@Unable to get an allocator/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEFailed to obtain detect_positiveonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeN11onnxruntime5IsInfENull input ptrN11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNIdEEN11onnxruntime5IsNaNINS_9MLFloat16EEE||||||||MeanVarianceNormalizationX and Y must both have M * N elements.No valid axes are specified. This is not handled now./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.ccconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = absl::lts_20220623::InlinedVector >; _Dp = std::_Optional_base >, false, false>]N11onnxruntime25MeanVarianceNormalizationEnormalize_variantensor/mean_variance_normalizatiComputeMeanVarianceNormalizationonnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccX input is required!failed to get first output!onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsOneHotDepth is negative./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = Eigen::internal::TensorBlockScratchAllocator::Allocation; _Alloc = std::allocator::Allocation>; std::vector<_Tp, _Alloc>::reference = Eigen::internal::TensorBlockScratchAllocator::Allocation&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlfiEE>Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of reflectedgewrapInvalid 'mode' attribute valueonnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.hInvalid 'pads' attribute valueInput tensor has no dimensionsonnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const PadsVector&, const PadsVector&, const onnxruntime::Mode&, T) [with T = long unsigned int; onnxruntime::PadsVector = absl::lts_20220623::InlinedVector >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc'pads' has wrong number of valuesdata_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const PadsVector&, const PadsVector&, const onnxruntime::Mode&, T) [with T = unsigned int; onnxruntime::PadsVector = absl::lts_20220623::InlinedVector >]Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) constpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Axes tensor should be a 1D tensor Pads tensor size should be equal to twice the input dimension count Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const PadsVector&, const PadsVector&, const onnxruntime::Mode&, T) [with T = unsigned char; onnxruntime::PadsVector = absl::lts_20220623::InlinedVector >]axes_tensor_dims.size() == 1pads_size == 2 * data_rankN11onnxruntime7PadBaseEN11onnxruntime3PadEAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hThe input tensor must not have any dynamic (-1) dimensions. Input shape:onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hA dimension cannot be less than -1, got At most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0input_shape_size != -1requested_shape[i] >= -1unknown_dim == -1, requested shape:input_shape_size == sizeA shape tensor must be a vector tensor.virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constshapeTensor->Shape().NumDimensions() == 1N11onnxruntime7ReshapeEN11onnxruntime9Reshape_1E Resize operatorUpsample operatorScalesValidation/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsamplebase.hScale value should be greater than or equal to 1.Scale value should be greater than 0.'Linear' mode only support: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the ParseScalesDatascales size should be greater than 0.all values in axes should be less than rank of the datamode attribute is . It can only be (default) or antialiasUpsampleBasestretchkeep_aspect_ratio_policynot_largernot_smallerkeep_aspect_ratio of [] is not supported!extrapolation_valuepytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetriccoordinate_transform_mode:[] is not supportted!yet this opset round_prefer_floorround_prefer_ceilnearest_mode:[cubic_coeff_aexclude_outside and anti-aliasing is set to onnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&)info.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)info.GetAttrs("scales", scales_).IsOK()onnxruntime::AspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const string&)onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&)`tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attributeonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&)exclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to (.7h66D36N11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIaEEN11onnxruntime6ResizeIaEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEtensor/upsamplebReverseSequenceonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()info.GetAttr("time_axis", &time_axis).IsOK()time_axis and batch_axis must have different values but both are . Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axis. Value must be in range [0,Invalid sequence length: gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]sequence_lens shape must be {batch_size}. Got:virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc. batch_size=Unknown tensor type of :2겘3GٱXܮ6[:2:2uuN11onnxruntime17ReverseSequenceOpEData type is not supported in thaddScatterElementsScatterCPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccnoneCPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.mulmin vs . Input rank=Indices dim= at pos= is greater than input dim=data type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=Indices type is not supported.N11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbSsEEEEECPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.ScatterNDCPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.input tensor and indices tensor must has rank larger than 0. last dimension of indices must not be larger than rank of input tensorupdates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = N11onnxruntime9ScatterNDEZNK11onnxruntime23ScatterNDDispatchTargetIfEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIdEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIlEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetImEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIiEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIjEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIsEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetItEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIaEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIhEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_9MLFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_8BFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIbEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetISsEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_N11onnxruntime5ShapeESizeN11onnxruntime4SizeEMissing or invalid starts and ends attributeonnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()Unexpected element size of void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*)onnxruntime::SliceImpl >(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&):: >&)>/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccoutput == output_endonnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&)::&)>Starts must be a 1-D arrayEnds must be a 1-D arrayFillVectorsFromInputStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got 'axes' has duplicates'axes' has an axis outside of the tensor dimension count'step' value cannot be 0Cannot slice scalarsN11onnxruntime9SliceBaseEN11onnxruntime6Slice1EN11onnxruntime7Slice10EFillVectorsFromISpaceDepth ops require a 4-D input. Provided rank: Attribute blocksize is not set.onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()DepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&)CRDDCRconstexpr const value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) const [with _Tp = long int; long unsigned int _Nm = 6; std::array<_Tp, _Nm>::const_reference = const long int&; std::array<_Tp, _Nm>::size_type = long unsigned int]/opt/rh/devtoolset-11/root/usr/include/c++/11/array/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in DepthToSpace op: Unsupported input type in SpaceToDepth op: N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthEN11onnxruntime12DepthToSpaceEe@SpaceToDepth requires input height to be a multiple of block_sizuires input width to be a multiple of block_sizeDepthToSpace requires input deptle of (block_siz*>Invalid value in 'split' attribute. All values must be > 0onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&, uint32_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })If 'num_outputs' is specified, the 'split' input should not be provided.Invalid num_outputs value of Axis= NumOutputs= Input shape=. Size of dimension being split is Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was The split tensor must be a vector tensor.virtual onnxruntime::common::Status onnxruntime::SplitImpl::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccsplit_tensor->Shape().NumDimensions() == 1N11onnxruntime9SplitBaseEN11onnxruntime9SplitImplEN11onnxruntime10Split_1_13EN11onnxruntime8Split_18E. shape= must be 1 instead of Dimension of input input_shape[i] == 1static onnxruntime::TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const TensorShapeVector&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hvirtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeEInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.ccN11onnxruntime4TileEperm size: does not match input rank: [ perm: does not align with rank of input data: Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, gsl::span, const gsl::span&, size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.ccnaxes > 0bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]Transpose of element size not supported in this build. Size=onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.num_axes > 0Transpose not implemented for empty tensors.void onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const string*, std::string*)(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span&, const string*, std::string*)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span&, const uint8_t*, uint8_t*, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constMismatched data types between input and output Tensors. N11onnxruntime13TransposeBaseEN11onnxruntime9TransposeETriluonnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.hinfo.GetAttr("upper", &temp).IsOK()k should be a 1-D or 0-D tensor.virtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.ccIsScalarOr1ElementVector(k)N11onnxruntime5TriluEUniquestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = long int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const long int&]Unsupported tensor type of N11onnxruntime6UniqueEMissing/Invalid 'axes' attribute valueonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensorN11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEResize: input tensor's rank does not match the output tensor's rank.axes should be less than output_dims.size()ParseSizesDataParseScalesDataAndAdjustOutputSizeInput dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) [with _Tp = float; long unsigned int _Nm = 4; std::array<_Tp, _Nm>::reference = float&; std::array<_Tp, _Nm>::size_type = long unsigned int]Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccRank of input and output tensor should be same.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeUpsample: unexpected modeBaseComputeUpsampleNearestInvalid roi input index.Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.N11onnxruntime21FilterParamsAntiAliasIiEEN11onnxruntime23BilinearParamsAntiAliasIiEEN11onnxruntime24TriLinearParamsAntiAliasIiEEN11onnxruntime22BiCubicParamsAntiAliasIiEEN11onnxruntime21FilterParamsAntiAliasIfEEN11onnxruntime23BilinearParamsAntiAliasIfEEN11onnxruntime24TriLinearParamsAntiAliasIfEEN11onnxruntime22BiCubicParamsAntiAliasIfEEZN11onnxruntime16UpsampleBilinearIhEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime16UpsampleBilinearIaEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime17UpsampleTrilinearIaEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime16UpsampleBilinearIiEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime16UpsampleBilinearIfEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime19HandleExtrapolationIhiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIaEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPaE_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime19HandleExtrapolationIaiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime19HandleExtrapolationIifEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime19HandleExtrapolationIffEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_@A@DJ @@5555 N11onnxruntime5WhereIhEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereIdEEN11onnxruntime5WhereISsEERegisterQuantizationKernels/onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterCpuContribKernelsCropAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder ( input dimensions instead) + bottomBorder (Input's height () + rightBorder (Input's width () + scale_[0] () + scale_[1] (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEECropAndResize specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1Null crop_size_ptrZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKSsPKiPS1_EUllE_EEvSG_lOS8_lEUllE_N11onnxruntime7contrib13CropAndResizeIfEEextrapolation_vaDynamicSliceonnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo&) [with T = float]info.GetAttr("scale", &scale_).IsOK()N11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEExpandDimsAn axis tensor must be a scalar tensor.virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.haxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDims Axis is ].Axis must be within range [N11onnxruntime7contrib10ExpandDimsEonnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()N11onnxruntime7contrib14FusedConvFloatEFusedGemm/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib9FusedGemmIfEETransposeMatMulImageScaleronnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.hinfo.GetAttrs("bias", bias_).IsOK()) does not match the number of channels (Bias size (N11onnxruntime7contrib11ImageScalerIfEEInverseN11onnxruntime7contrib7InverseE*ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib9LayerNormILb0EEEN11onnxruntime7contrib9LayerNormILb1EEESparseToDenseMatMulCOO indices must be 2-D, got: is out of bounds of lhs_right: is out of bounds of out_left: COO k index: COO m index: Currently supporting only 2-D matricesCan not multiply A and B as inner dimension does not match. inner_A: Expecting COO 2-D indices shape/onnxruntime_src/onnxruntime/contrib_ops/cpu/math/sparse_dense_matmul.ccExpecting the same number NNZ == size of Inner indices vs inner_B: Expecting 2xValues == indicesOuter size must be M + 1N11onnxruntime7contrib19SparseToDenseMatMulE/onnxruntime/contrib_ops/cpu/math/sparse_dense_mCurrently support only COO and CMatMulFpQ4Second input of MatMulFpQ4 must be a 1D blob!virtual onnxruntime::common::Status onnxruntime::contrib::MatMulFpQ4::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_fpq4.ccblob_shape.NumDimensions() == 1Right hand side of MatMulFpQ4 must be a 2D matrix!Operator MatMulFpQ4 not yet supported on this hardware platform.Quantized and packed blob size differ from expected!std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_Q4_GEMM_DATA_PARAMS; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_Q4_GEMM_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]b_shape.NumDimensions() == 2buf_size > 0(size_t)blob_len == buf_sizeN11onnxruntime7contrib10MatMulFpQ4EMaxpoolWithMaskN11onnxruntime7contrib15MaxpoolWithMaskEN11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEEcount_include_patrib_ops/cpu/maxpool_with_mask.hMurmurHash3virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_byteskeysinput_num_bytes % 4 == 0N11onnxruntime7contrib11MurmurHash3Eonnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.hinfo.GetAttr("channels_last", &channels_last_).IsOK()invalid channel countchannels_ > 0onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&)info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)pool_attrs_.kernel_shape.size() == 2scales_.size() == 4' for NCHWc UpsampleUnsupported mode 'onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&)info.GetAttrs("scales", scales_).IsOK()scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.ccX_rank == 4(channels % 4) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) constchannels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constX_shape.NumDimensions() == 4Unsupported convolution size.virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)N11onnxruntime7contrib13NchwcPoolBaseEN11onnxruntime7contrib12ReorderInputEN11onnxruntime7contrib13ReorderOutputEN11onnxruntime7contrib9NchwcConvEN11onnxruntime7contrib12NchwcMaxPoolEN11onnxruntime7contrib16NchwcAveragePoolEN11onnxruntime7contrib13NchwcUpsampleE*ZNK11onnxruntime7contrib12ReorderInput7ComputeEPNS_15OpKernelContextEEUllE_*ZNK11onnxruntime7contrib13NchwcUpsample7ComputeEPNS_15OpKernelContextEEUllE_QAttentiononnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.hinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&]4D mask in attention cpu kernel is not supportedApplyAttention/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime7contrib13AttentionBaseEN11onnxruntime7contrib16AttentionCPUBaseEZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE_ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE0_ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib10QAttentionIfEEmask_filter_valuqkv_hidden_sizespast_present_shatrib_ops/cpu/quantization/attent} for per-tensor/layer quantization or shape {} for per-channel quantization. Actual:Weight zero point must be zeroInput W_zero_point, 4*W_scaleDynamicQuantizeLSTM : Weight point must be constantRecurrent/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccN11onnxruntime7contrib19DynamicQuantizeLSTMEComputeCommon/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validonnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext*, const uint8_t*, const onnxruntime::TensorShape&, float, uint8_t, bool, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) constIsScalarOr1ElementVector(a_zero_point_tensor)N11onnxruntime7contrib24MatMulIntegerToFloatBaseEN11onnxruntime7contrib21DynamicQuantizeMatMulEN11onnxruntime7contrib20MatMulIntegerToFloatEMatMulInteger16onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_integer16.ccA != nullptr && B != nullptrN11onnxruntime7contrib15MatMulInteger16IssiEEonnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]N11onnxruntime7contrib11NhwcMaxPoolIaEEN11onnxruntime7contrib11NhwcMaxPoolIhEEntization/nhwc_mQEmbedLayerNormalizationSegment embedding zero point must be a scalar or 1D tensor of size 1/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qembed_layer_norm.cc*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfaEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfhEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib18EmbedLayerNormBaseEN11onnxruntime7contrib15QEmbedLayerNormIfEEWord embedding scale must be a scalar or 1D tensPosition embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tGamma scale must be a scalar or 1D tensor of sizBeta scale must be a scalar or 1D tensor of sizeWord embedding zero point must be a scalar or 1D tensor of size ng zero point must be a scalar or 1D tensor of sGamma zero point must be a scalar or 1D tensor oBeta zero point must be a scalar or 1D tensor ofinput index out QLinearLeakyReluZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_N11onnxruntime7contrib17QLinearLookupBaseIaEEN11onnxruntime7contrib16QLinearLeakyReluIaEEN11onnxruntime7contrib17QLinearLookupBaseIhEEN11onnxruntime7contrib16QLinearLeakyReluIhEEN11onnxruntime7contrib14QLinearSigmoidIaEEN11onnxruntime7contrib14QLinearSigmoidIhEEZN11onnxruntime7contrib16QLinearLeakyReluIaEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib16QLinearLeakyReluIhEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib14QLinearSigmoidIaEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIhEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ #<void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper]MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_binary_op.ccIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char]*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_N11onnxruntime7contrib10QLinearAddIaEEN11onnxruntime7contrib10QLinearAddIhEEN11onnxruntime7contrib10QLinearMulIaEEN11onnxruntime7contrib10QLinearMulIhEEEach input must be (tensor, scale, zero_point) tuple!onnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.ccinput_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @tensor_x_scale->IsDataType()Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext*) constinput_count_x3 >= 3 && input_count_x3 % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]N11onnxruntime7contrib13QLinearConcatE*ZN11onnxruntime7contrib13QLinearConcatC4ERKNS_12OpKernelInfoEEUlfE_*ZNK11onnxruntime7contrib13QLinearConcat7ComputeEPNS_15OpKernelContextEEUlfE_Input x_scale must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_scale)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)N11onnxruntime7contrib24QLinearGlobalAveragePoolEZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ntization/qlinear_global_averageQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_lookup_table.ccQlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function]ZN11onnxruntime7contrib23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_pool.ccQLinear Pooling unsupported pooling size!onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]N11onnxruntime7contrib18QLinearAveragePoolEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEEZN11onnxruntime7contrib16dequantize_arrayIaEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib16dequantize_arrayIhEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_QLinearSoftmaxopset must be existed in attributes of QlinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_softmax.ccvoid onnxruntime::contrib::{anonymous}::BuildLookupTableIfFixed(const onnxruntime::OpKernelInfo&, std::vector&, size_t, bool)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)N11onnxruntime7contrib14QLinearSoftmaxE*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIhEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIaEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_@o@QLinearWherestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned char&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)onnxruntime::contrib::QLinearWhere::QLinearWhere(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_where.ccinput_def_count == kExpected_input_countWrong input type encountered for zero point input def of x, y, ztensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()There must be Input scale is not float for quantized input x @ 2virtual onnxruntime::common::Status onnxruntime::contrib::QLinearWhere::Compute(onnxruntime::OpKernelContext*) constInput scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_z_scale->IsDataType()N11onnxruntime7contrib12QLinearWhereE*ZN11onnxruntime7contrib12QLinearWhereC4ERKNS_12OpKernelInfoEEUlfE_*ZNK11onnxruntime7contrib12QLinearWhere7ComputeEPNS_15OpKernelContextEEUlfE_ TATBTCTYZTYQGemmvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const onnxruntime::TensorShape*, T*) [with T = int; ptrdiff_t = long int]QGemm : scale of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point and scale of input b should have same shape sizeQGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : scale of y must be null or a scalar or 1D tensor of size 1/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccN11onnxruntime7contrib5QGemmEntization/quant_SampleOpN11onnxruntime7contrib8SampleOpIfEEonnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double]skip is expected to have 3 or 2 dimensions, got input is expected to have 3 or 2 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib13SkipLayerNormIfEEN11onnxruntime7contrib13SkipLayerNormIdEEskip is expected to have same shape as input or, a batch size of 1 or no batch size when input hlast two dimensions of skip needs to be same as std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::StringPiece; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::StringPiece&]Tokenizerattribute mark is not setmincharnum_ > 0!tokenexp.empty()separators must not be empty!separators.empty()Can not digest separators: Can not digest tokenexp: onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute pad_value is not setattribute mincharnum is not setattribute mincharnum must have a positive valueEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Input string contains invalid utf8 charsconst value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]Input string contains invalid utf8 chars: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedN11onnxruntime7contrib9TokenizerEBeamSearchgpt_subgraph_ == nullptrinit_decoderencoderSetupSubgraphExecutionInfoSetupSubgraphExecutionInfo should only be called once for each subgraph.virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search.ccinit_run_gpt_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]Encoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrwhisper_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_decoder_subgraph_ == nullptrGenerateNextToken/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_base.hInput 'input_features' is expected to have 3 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_ids' is expected to have 2 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got void onnxruntime::contrib::transformers::BeamSearch::Init(const onnxruntime::OpKernelInfo&)parameters_.model_type == IGenerationParameters::kModelTypeGpt || parameters_.model_type == IGenerationParameters::kModelTypeT5 || parameters_.model_type == IGenerationParameters::kModelTypeWhisperinfo.GetAttr("encoder", &proto).IsOK()info.GetAttr("decoder", &proto).IsOK()Node input should be a scalar. Got shape of min_lengthmax_lengthnum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.CheckInputsExecute/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_gpt.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_whisper.honnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_t5.hparameters->sequence_length == 1onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]Subgraph SessionState was not found for 'decoder' attribute.virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::Compute(onnxruntime::OpKernelContext*) constCreateFeedsFetchesManager must be called prior to execution of graph.decoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_Subgraph SessionState was not found for 'encoder' attribute.encoder_feeds_fetches_manager_typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::T5EncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::T5EncoderSubgraph&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::WhisperEncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::WhisperEncoderSubgraph&]decoder_session_stateencoder_session_stateModel type is not supported.FN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_EN11onnxruntime11controlflow18IControlFlowKernelEN11onnxruntime7contrib12transformers10BeamSearchEN11onnxruntime7contrib12transformers12GenerateBaseEN11onnxruntime7contrib12transformers14BeamSearchBaseIfEEN11onnxruntime7contrib12transformers13BeamSearchGptIfEEN11onnxruntime7contrib12transformers14BeamSearchBaseINS_9MLFloat16EEEN11onnxruntime7contrib12transformers13BeamSearchGptINS_9MLFloat16EEEN11onnxruntime7contrib12transformers12BeamSearchT5IfEEN11onnxruntime7contrib12transformers12BeamSearchT5INS_9MLFloat16EEEN11onnxruntime7contrib12transformers17BeamSearchWhisperIfEEN11onnxruntime7contrib12transformers17BeamSearchWhisperINS_9MLFloat16EEEPFN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEPFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEPFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEPFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEPFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_Evoid onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_parameters.ccinput_features shall have 3 dimensions. Got decoder_input_ids shall have 2 dimensions. Got ) shall be greater than input sequence length (max_length <= kMaxSequenceLengthnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got ) shall be be no more than num_beams (num_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got logits_processor shall be a non-negative integer, got context != nullptrdims.size() == 3decoder_dims.size() == 2max_length (max_length > sequence_length) shall be no more than num_return_sequences >= 1num_return_sequences (repetition_penalty > 0.0flogits_processor >= 0decoder_start_tono_repeat_ngram_trib_ops/cpu/transformers/beam_search_parametersvoid onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span&, gsl::span&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_scorer.cctop_k <= beams_used_virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Process(onnxruntime::contrib::transformers::ISequences&, gsl::span&, gsl::span&, gsl::span&)next_scores.size() == next_tokens.size()next_scores.size() == next_indices.size()Batch can only be done if all beams have been generatedbeam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)static_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()beam_idx == num_beams_virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(onnxruntime::contrib::transformers::ISequences&, gsl::span&, onnxruntime::Tensor*, onnxruntime::Tensor*)output_sequences != nullptrN11onnxruntime7contrib12transformers11IBeamScorerEN11onnxruntime7contrib12transformers16BeamSearchScorerEZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers15HypothesisScoreEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_ZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers14BeamHypothesesEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_N11onnxruntime7contrib12transformers14IConsoleDumperEN11onnxruntime7contrib12transformers22CpuTensorConsoleDumperEBeamSearch op: An implementation for the input type onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/generation_device_helper.ccinput_ids_shape.NumDimensions() == 2void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]element_type == DataTypeImpl::GetType()onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, gsl::span&, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]is_kv_cacheonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IBeamSearchState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]logits_shape.NumDimensions() == 3onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]input_features_shape.NumDimensions() == 3original_decoder_input_ids_shape.NumDimensions() == 2start_token_id >= 0onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IGreedySearchState*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_cpu_helper.hGreedySearchProcessLogitsSt15binary_functionIffbESt7greaterIfESt4lessIfE(knNot Implementedvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search.ccGreedySearch/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_base.hvoid onnxruntime::contrib::transformers::GreedySearch::Init(const onnxruntime::OpKernelInfo&)parameters_.model_type == IGenerationParameters::kModelTypeGpt/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_gpt.hvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::Compute(onnxruntime::OpKernelContext*) constFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEN11onnxruntime7contrib12transformers12GreedySearchEN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_22GreedySearchParametersEEEPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEPFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEvoid onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_parameters.cc@N11onnxruntime7contrib12transformers20ILogitsProcessorListEN11onnxruntime7contrib12transformers19LogitsProcessorListEN11onnxruntime7contrib12transformers16ILogitsProcessorIfEEN11onnxruntime7contrib12transformers32RepetitionPenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers28NoRepeatNGramLogitsProcessorIfEEN11onnxruntime7contrib12transformers24VocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PrefixVocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers24MinLengthLogitsProcessorIfEEN11onnxruntime7contrib12transformers26TemperatureLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PresencePenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers24TimestampLogitsProcessorIfEEvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling.ccSamplingvoid onnxruntime::contrib::transformers::Sampling::Init(const onnxruntime::OpKernelInfo&)virtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib12transformers8SamplingEN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_18SamplingParametersEEEvoid onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_parameters.ccseed >= 0min_tokens_to_kepresence_penaltySeed must be >= N11onnxruntime7contrib12transformers10ISequencesEN11onnxruntime7contrib12transformers9SequencesESetupbeam_widthpast_sequence_length/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_base.ccconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::_Optional_base]subgraph past state is expected to have 5 dimension, got subgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph output present_key_self_0 is expected to have 4 dimension, got subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]DecoderMaskedMultiHeadAttentionDecoderMaskedSelfAttentionN11onnxruntime7contrib12transformers8SubgraphECUDAExecutionProROCMExecutionPronsformers/subgraSetup must be called before CreateInitialFeedsonnxruntime::common::Status onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const std::vector&, int, int, gsl::span&, OrtValue&, const OrtValue*, std::vector&, const CreateGptInputsFunc&, const AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, onnxruntime::Stream*, int, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_gpt.ccsession_state_ != nullptrCreateInitialFeedsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)subgraph input 1 shall be named as position_ids, got: subgraph input 2 shall be named as attention_mask, got: subgraph input 3 shall be named as past_0, got: subgraph input 1 shall be named as present_0, got: subgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputposition_idspast_0Validatepresent_0N11onnxruntime7contrib12transformers11GptSubgraphEencoder_hidden_statesencoder_attention_maskdecoder_masked_attention shall use with past_present_share_buffernumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 0 shall be named as input_ids, got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.ccdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph output 0 shall be named as logits, got: decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph output shall have same data type as that of encoder_hidden_statesonnxruntime::common::Status onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)Invalid hidden_states expension: has_hidden_state_ == falseN11onnxruntime7contrib12transformers17T5DecoderSubgraphEph_t5_decoder.ccCreateInitialFeeonnxruntime::common::Status onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, const std::vector&, int, int, std::vector&, const CreateEncoderInputsFunc&, const AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_encoder.cccpu_allocator shouldn't be nullptrexpect 3 inputs, got:expect >=6 outputs, got:encoder_input_idsdecoder_input_idspresent_key_self_0present_value_self_0number of outputs expected to be 2 + 4 * layers, got:encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 2 shall be named as present_key_self_0, got: encoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph outputs 1, 2, ... shall have same data typeN11onnxruntime7contrib12transformers17T5EncoderSubgraphEph_t5_encoder.cc/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_decoder.ccdecoder subgraph input 1 shall be named as encoder_hidden_states, got: decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typeonnxruntime::common::Status onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)N11onnxruntime7contrib12transformers22WhisperDecoderSubgraphEph_whisper_decodonnxruntime::common::Status onnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, int, const std::vector&, std::vector&, const CreateWhisperEncoderInputsFunc&, const AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_encoder.ccexpect 2 inputs, got:encoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeN11onnxruntime7contrib12transformers22WhisperEncoderSubgraphEph_whisper_encodInput tensor to Unique op should be 1DN11onnxruntime7contrib6UniqueIfEEWordConvEmbeddingConv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute . char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute.Char embedding size does not match conv kernal size 2. embedding_size attribute: conv filter size: conv kernal size 1: conv_window_size attribute: Char embedding size: Conv kernal size 2 : /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccN11onnxruntime7contrib17WordConvEmbeddingEconv_window_sizechar_embedding_shӥhӥhӥhUnsupported typeEluNo attribute with name:''is defined.Attribute name and type don't match for '/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.hCreateunknown kernel type/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.cconnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Celu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]N11onnxruntime8functors7SigmoidIfEEN11onnxruntime8functors4TanhIfEEN11onnxruntime8functors3EluIfEEN11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEEN11onnxruntime8functors11HardSigmoidIfEEN11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEEN11onnxruntime8functors9LeakyReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEEN11onnxruntime8functors4ReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEEN11onnxruntime8functors4ReluIdEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEEN11onnxruntime8functors4ReluIaEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEEN11onnxruntime8functors4ReluIiEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEEN11onnxruntime8functors4SeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEEN11onnxruntime8functors7SigmoidIdEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEEN11onnxruntime8functors8SoftplusIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEEN11onnxruntime8functors8SoftsignIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEEN11onnxruntime8functors4TanhIdEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEEN11onnxruntime8functors4CeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEEN11onnxruntime8functors15ThresholdedReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEEN11onnxruntime8functors18ParametricSoftplusIfEEN11onnxruntime8functors10ScaledTanhIfEEAA-rB-rB-rB-rB-r-r-r-r;?;?;?;?1111^9^9^9^9CCCCgiP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>O@O@O@O@OOOO9999>@9@IfFailed to create output tensor for If output 'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.ccnum_subgraph_outputs == static_cast(num_outputs)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]Failed to create output tensor for std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const OrtValue*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const OrtValue* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]then_branchelse_branch' attribute.virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for 'typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::If::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::If::Info&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FeedsFetchesManager&]virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtDevice*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtDevice*&]info == nullptrvoid onnxruntime::If::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()N11onnxruntime2IfE*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceR8OrtValueRbE_Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedLoopstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&]Inconsistent shape in loop output for output. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]All scan outputs MUST be tensorsvoid onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector&, std::vector&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cclast_outputs[j + 1].IsTensor()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TypeProto*&]Graph in 'body' attribute of Loop should have onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)static_cast(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs inputs. Found: but has 'Loop' node has virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)info_ == nullptrConcatenateLoopOutputvoid onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("body", &proto).IsOK()'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Subgraph SessionState was not found for 'body' attribute.virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) consttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Loop::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Loop::Info&]feeds_fetches_manager_FN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEN11onnxruntime4LoopEPFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEcontrolflow/loopScan<8> spec does not support transpose of output. This should never be called.Scanonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<8>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<8>::Info&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&]void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()Subgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Output OrtValue has not been created for loop state variable output onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::scan::detail::OutputIterator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::scan::detail::OutputIterator&]typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = const OrtValue&]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::scan::detail::LoopStateVariable&]p_mlvalueCreateLoopStateVariables dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid scan input: has batch size of has length of sequence_lens length of did not match batch size of ValidateInputInvalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]feeds_fetches_manager_ && info_FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEN11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorEonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<9>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<9>::Info&]final_output_mlvalue_. Output tensor rank was TransposeOutputAttempt to retrieve final output before it was set.const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputs but expected output_mlvalueonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&)SetupInputs but input '' dimension Invalid value in scan_input_axes for input . Input tensor rank was onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]N11onnxruntime4ScanILi9EEE*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_ scan_input_direcscan_output_direscan_output_axesMisuse of LoopStateVariable. Attempt to move beyond end of sequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cciteration_num_ < sequence_len_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()OrtValue& onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = OrtValue&] inputs but Scan was only given The subgraph in 'body' requires onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)num_variadic_inputs == num_subgraph_inputs' was Number of entries in 'Invalid values in 'void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, onnxruntime::TensorShapeVector&, size_t)directions.size() == num_entries'. 0 == forward. 1 == reverse. is not compatible with Mismatch between expected shape and shape from first outputCreateFeedsFetchesManagerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer::Iterator&]Failed to create output tensor for output #AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocatoronnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)!is_concrete_shape_AllocateFinalOutputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::scan::detail::LoopStateVariable&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]IterateSequenceSubgraph must have the shape set for all outputs but did not.AllocateOutput*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceRSD_RbE_QuickGeluonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus]ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9QuickGeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEEN11onnxruntime7contrib4GeluIfEEN11onnxruntime7contrib9QuickGeluIfEE#?AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{Attention query layer weight shape error! Expected:{Attention v weight shape error! Expected:{Attention layer weight shape error! Expected: {/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc}, actural: ], while found!, am_attn_size}, Got:}, Got: }. Got: , aw_attn_size}. Got:virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib17DeepCpuAttnLstmOpEN11onnxruntime7contrib16AttentionWrapperIfEE row[ rows[ [seqno=constexpr const value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) const [with _Tp = std::unique_ptr >; long unsigned int _Nm = 3; std::array<_Tp, _Nm>::const_reference = const std::unique_ptr >&; std::array<_Tp, _Nm>::size_type = long unsigned int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccconstexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) [with _Tp = std::unique_ptr >; long unsigned int _Nm = 3; std::array<_Tp, _Nm>::reference = std::unique_ptr >&; std::array<_Tp, _Nm>::size_type = long unsigned int]ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib9AttentionIfEEExpect to have present state output when past state input is givenonnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.ccInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'input' is expected to have 3 dimensions, got Input 'bias' is expected to have 1 dimension, got Input 'weights' is expected to have 2 dimensions, got hidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Input 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of past_sequence_length tensor must be of one element when past_present_share_buffer is setwhen past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setbias_dims[0]= v_hidden_size= k_hidden_size= q_hidden_size=Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_ex' with 2D data shall have shape batch_size x total_sequence_leex' with 3D datae batch_size x sequence_length x total_sequence_ex' with 4D datae batch_size x 1 x max_sequence_length x max_seq shall have is_unidirectional seAttention cannot have both past and relative_posInput 1 dimension 0 should have same length as dimension 2 of inInput 'bias' dimension 0 should have same length as dimension 1 of input 'weight attribute should have 3 elementuse_approximation/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cconnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false]N11onnxruntime7contrib8BiasGeluIfLb0EEEN11onnxruntime7contrib8BiasGeluIfLb1EEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_y"=y"=y"=y"=*BL?*BL?*BL?*BL?Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got same length as the last dimensioBifurcationDetectoronnxruntime::contrib::BifurcationDetector::BifurcationDetector(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bifurcation_detector.hinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ >= min_ngram_size_min_ngram_size_ > 0max_ngram_size_ > 0virtual onnxruntime::common::Status onnxruntime::contrib::BifurcationDetector::Compute(onnxruntime::OpKernelContext*) constsrc_tokens_len >= prev_suffix_match_idx_datapred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)N11onnxruntime7contrib19BifurcationDetectorEonnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.ccZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib14EmbedLayerNormIfEEinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of input_ids and position_ids shall have same sequeposition_ids's first dimension shall be 1 or batInput 0 and 1 shall have same shInput 0 and 7 (mask) shall have word_embedding and position_embedding shall have same dimension do_rotaryqkv_hidden_sizespast_present_share_bufferonnxruntime::contrib::MultiHeadAttention::MultiHeadAttention(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/multihead_attention.ccInput 'query' is expected to have 3 or 5 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' dimension 0 should be batch_size, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length)Input 'past_key' dimension 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall be both present or both absentInput 'query' is expected to have 3 dimensions when key is given, got Input 'key' is expected to have 3, 4, or 5 dimensions, got Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'key' shall have same dim 2 (hidden_size)Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect 'value' be none when 'key' has packed kv format.Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyExpect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv'bias' is not allowed for packed kv. Input 'key_padding_mask' shape shall be (batch_size) or (batch_size, kv_sequence_length)Input 'value' is expected to have 3 or 4 dimensions, got Input 'query' and 'value' shall have same dim 0 (batch_size)Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'relative_position_bias' dimension 0 should be batch_size or 1, got AddBiasReshapeAddBiasTransposeMaybeTransposeToBNSHAndAddBiasPacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUZN11onnxruntime7contrib14AddBiasReshapeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_ZN11onnxruntime7contrib16AddBiasTransposeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_N11onnxruntime7contrib18MultiHeadAttentionIfEETidNGramRepeatBlockonnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/ngram_repeat_block.hinfo.GetAttr("ngram_size", &ngram_size_).IsOK()onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) const::token_id < vocab_sizevirtual onnxruntime::common::Status onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeN11onnxruntime7contrib16NGramRepeatBlockEZNK11onnxruntime7contrib16NGramRepeatBlock7ComputeEPNS_15OpKernelContextEEUlllE0_CDistonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()sqeuclideanonnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = double]The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:N11onnxruntime7contrib5CDistIfEEN11onnxruntime7contrib5CDistIdEEConvTransposeWithDynamicPadsN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEunimplemented activation: activation_params count mismatchnot support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc is not in (0, Real memory steps void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]mem_steps <= max_memory_steps_ && mem_steps > 0N11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEECudaCudaPinnedOpenVINO_GPUDMLHipHipPinnedSpecified device is not supported./onnxruntime_src/onnxruntime/core/framework/allocator.ccN11onnxruntime12CPUAllocatorEReceived invalid value of arena_extend_strategy St14default_deleteIN11onnxruntime10IAllocatorEESt14default_deleteIN11onnxruntime8BFCArenaEESt14default_deleteIN11onnxruntime16StreamAwareArenaEESt19_Sp_counted_deleterIPN11onnxruntime16StreamAwareArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEe/framework/allocudaMallochipMallocCould not find Region for onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cch < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const/onnxruntime_src/onnxruntime/core/framework/bfc_arena.hp_int < base_int + memory_size_p_int >= base_intstd::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator) [with _Key = long unsigned int; _Val = long unsigned int; _KeyOfValue = std::_Identity; _Compare = onnxruntime::BFCArena::Bin::ChunkComparator; _Alloc = std::allocator; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree, onnxruntime::BFCArena::Bin::ChunkComparator, std::allocator >::iterator; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator = std::_Rb_tree, onnxruntime::BFCArena::Bin::ChunkComparator, std::allocator >::const_iterator]void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binBFCArenaCreating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy: Creating bins of max chunk size with following configs: initial_chunk_size_bytes: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int, int64_t)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle)!c->in_use() && (c->bin_num == kInvalidBinNum)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use() && c1->stream == c2->streamc2->prev == h1onnxruntime::BFCArena::ChunkHandle onnxruntime::BFCArena::Coalesce(onnxruntime::BFCArena::ChunkHandle)!c->in_use()void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::DeallocateRawInternal(void*)h != kInvalidChunkHandlevoid onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t)onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)!chunk->in_use()std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_num BFC Arena shrunk by bytes. Could not find Region for: entry != regions_.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = void*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = void*&] The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*) Free ChunkDumpMemoryLogAllocator:: Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_useDiff between in-use and requested bytes is Summary of in-use chunks by size: AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)Available memory of ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at is smaller than requested bytes of Incorrect arena extend strategy.onnxruntime::BFCArena::Extend(size_t)::onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t)0 == memory_size % kMinAllocationSizeFailed to allocate memory for requested buffer of size typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::synchronize::Notification; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::synchronize::Notification&]ReserveReserving memory in BFCArena for virtual void* onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()N11onnxruntime8BFCArenaEN11onnxruntime16StreamAwareArenaEe/framework/bfc_Config value is longer than maximum length: Config with key [] already exists with value []. It will be overwrittenConfig key is empty or longer than maximum lengte/framework/confCopyTensors/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccCopySparseTensorsCopyTensorCopyTensorAsyncvirtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&) constsrc.SizeInBytes() == dst.SizeInBytes()N11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferETensor size mismatchThere's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.ccdata_transfer registered is nullptr.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::utils::data_types_internal::TypeNode&](null)doubleuint8uint16uint32uint64bfloat16Float8E4M3FNFloat8E4M3FNUZFloat8E5M2Float8E5M2FNUZLěԛ䛺$4DTLLdtGetDeleteFuncGetElementTypevirtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const/onnxruntime_src/onnxruntime/core/framework/data_types.ccthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())Not implementedvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constexpected a registered ONNX typestatic void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto*, onnx::TypeProto&)/onnxruntime_src/include/onnxruntime/core/framework/data_types.hvalue_proto != nullptrstatic void onnxruntime::data_types_internal::SequenceTypeHelper::Set(const onnx::TypeProto*, onnx::TypeProto&)elem_proto != nullptrstatic void onnxruntime::data_types_internal::OptionalTypeHelper::Set(const onnx::TypeProto*, onnx::TypeProto&)Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)4H44x44hbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto&, const onnx::TypeProto&)4 44Ĺ4Tvirtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const4`4404Ȼxbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)D4ԼD4D4D4T佺bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())virtual bool onnxruntime::OptionalTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->optional_type())Only ONNX MLDataType can be registeredvoid onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)We do not expect duplicate registration of types for: is not supportedt4$Ddt4t4$Ddsparse tensor type 4<\|44<l is not currently registered or supportedMLDataType for: N11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime16OptionalTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime10TensorTypeINS_12Float8E4M3FNEEEN11onnxruntime10TensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime10TensorTypeINS_10Float8E5M2EEEN11onnxruntime10TensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeISsEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime16SparseTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime16SparseTensorTypeINS_10Float8E5M2EEEN11onnxruntime16SparseTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime18SequenceTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime18SequenceTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime18SequenceTensorTypeINS_10Float8E5M2EEEN11onnxruntime18SequenceTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12OptionalTypeINS_6TensorEiEEN11onnxruntime12OptionalTypeINS_6TensorEfEEN11onnxruntime12OptionalTypeINS_6TensorEbEEN11onnxruntime12OptionalTypeINS_6TensorESsEEN11onnxruntime12OptionalTypeINS_6TensorEaEEN11onnxruntime12OptionalTypeINS_6TensorEhEEN11onnxruntime12OptionalTypeINS_6TensorEtEEN11onnxruntime12OptionalTypeINS_6TensorEsEEN11onnxruntime12OptionalTypeINS_6TensorElEEN11onnxruntime12OptionalTypeINS_6TensorEdEEN11onnxruntime12OptionalTypeINS_6TensorEjEEN11onnxruntime12OptionalTypeINS_6TensorEmEEN11onnxruntime12OptionalTypeINS_6TensorENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_6TensorENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E5M2FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqEiEEN11onnxruntime12OptionalTypeINS_9TensorSeqEfEEN11onnxruntime12OptionalTypeINS_9TensorSeqEbEEN11onnxruntime12OptionalTypeINS_9TensorSeqESsEEN11onnxruntime12OptionalTypeINS_9TensorSeqEaEEN11onnxruntime12OptionalTypeINS_9TensorSeqEhEEN11onnxruntime12OptionalTypeINS_9TensorSeqEtEEN11onnxruntime12OptionalTypeINS_9TensorSeqEsEEN11onnxruntime12OptionalTypeINS_9TensorSeqElEEN11onnxruntime12OptionalTypeINS_9TensorSeqEdEEN11onnxruntime12OptionalTypeINS_9TensorSeqEjEEN11onnxruntime12OptionalTypeINS_9TensorSeqEmEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E5M2FNUZEEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeIbEEN11onnxruntime17PrimitiveDataTypeISsEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_12Float8E4M3FNEEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E4M3FNUZEEEN11onnxruntime17PrimitiveDataTypeINS_10Float8E5M2EEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E5M2FNUZEEEZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_?<CleanUp/onnxruntime_src/onnxruntime/core/framework/device_stream_collection.ccvoid onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, onnxruntime::Stream*)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Stream*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::Stream* onnxruntime::DeviceStreamCollectionImpl::GetStream(size_t) conststd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::Stream* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]stream_idx < num_streams_void onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)N11onnxruntime6StreamEN11onnxruntime11synchronize12NotificationEN11onnxruntime17DummyNotificationEN11onnxruntime11DummyStreamEUnloading DSO Failed to unload DSO: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while destructing CustomOpsLoader with message: A dso with name has already been loaded.LoadExternalLibCaught exception while loading custom ops with message: N11onnxruntime11ExLibLoaderEe/framework/ex_lExpected shape from model of does not match actual shape of Memory pattern planner is not enabled on this execution framework. size=constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::OrtValuePatternPlanner; _Dp = std::_Optional_base]TraceAllocation for ort_value_idx=invalid index int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast(offset) < node_values_size_const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const/onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_allocator is nullptrallocator.get() != nullptronnxruntime::StreamAwareArena* onnxruntime::AsStreamBasedAllocator(onnxruntime::AllocatorPtr)/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccvoid onnxruntime::ExecutionFrame::TraceFree(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceFreeTraceFree for ort_value_idx=ReleaseMLValueImplonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer(OrtValue&, int, onnxruntime::MLDataType, const OrtDevice&, const onnxruntime::TensorShape&, bool)Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.!is_strided_tensorvoid onnxruntime::IExecutionFrame::Init(gsl::span, gsl::span, const std::unordered_map&, const std::function&)>&, gsl::span)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Could not find OrtValue with idx 'T* OrtValue::GetMutable() [with T = onnxruntime::SparseTensor]Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valuestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]AllocateMLValueTensorSelfOwnBufferHelper, block in memory pattern size is: , fall back to default allocation behavioronnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtDevice&, const onnxruntime::TensorShape&)alloc && alloc.get() != nullptrtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IStreamCommandHandleRegistry; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IStreamCommandHandleRegistry&]For ort_value with index: but the actual size is: Failed to get allocator for onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.AllocateAsPerAllocationPlanInvalid allocation kind: AllocateReusedOrtValueIfNotAllocatedHelper Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&)shape && tensor.Shape() == *shapeshape && sp_tensor.DenseShape() == *shapenode_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, gsl::span)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::ExecutionFrame::ExecutionFrame(gsl::span, gsl::span, gsl::span, gsl::span, const std::unordered_map >&, const onnxruntime::DeviceStreamCollection*, const onnxruntime::SessionState&)buffers_.find(location) == buffers_.end()Allocation of memory pattern buffer for ExecutionFrame returned nullptr failed. Error:N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameE*ZN11onnxruntime14ExecutionFrameC4EN3gsl4spanIKiLm18446744073709551615EEENS2_IK8OrtValueLm18446744073709551615EEES4_S7_RKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS5_RbEESt4hashImESt8equal_toImESaISt4pairIKmSL_EEEPKNS_22DeviceStreamCollectionERKNS_12SessionStateEEUlRKSsE_e/framework/execVerifyOutputSizeAllocateMLValueTensorPreAllocateIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creatorvirtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, onnxruntime::HashValue&) const/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccmetadef_id_generator_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]N11onnxruntime18IExecutionProviderECandidate for fallback CPU execution: std::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer&, const onnxruntime::IExecutionProvider::IKernelLookup&, gsl::span)/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.ccORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodekernel_info != nullptrGetCpuPreferredNodesfloat8e5m2fnuz*ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKNS_18IExecutionProvider13IKernelLookupEN3gsl4spanIKmLm18446744073709551615EEEEUlRKNS_7NodeArgEmE0_e/framework/fallback_cpu_capabilGetCpuPreferredNvoid onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)/onnxruntime_src/onnxruntime/core/framework/func_kernel.hCreate state function failed. Return value:API VERSION InlineNodes/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Node*&] cannot be used with this model due to its ONNX opset not being supported by the layout transformer.std::visit: variant is valuelessprovider_type must be specified.onnxruntime::KernelLookup::KernelLookup(onnxruntime::ProviderType, gsl::span >, const onnxruntime::IKernelTypeStrResolver&)/onnxruntime_src/onnxruntime/core/framework/kernel_lookup.htypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IndexedSubGraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IndexedSubGraph&] was inserted using the NHWC format as requested by 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.!provider_type_.empty()GetCapabilityForEPNode '' OpType: with domain:, but was not selectedPartitionOnnxFormatModelImplThe Function Style fusion is deprecated.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ComputeCapability; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ComputeCapability&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::GraphViewer&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&] did not return correct number of compiled functionsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeComputeInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeComputeInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]PartitionOrtFormatModelImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::PartitionOrtFormatModelImpl(const onnxruntime::{anonymous}::PartitionParams&, onnxruntime::KernelRegistryManager&, onnxruntime::IExecutionProvider&)::CompilationEntry; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::PartitionOrtFormatModelImpl(const onnxruntime::{anonymous}::PartitionParams&, onnxruntime::KernelRegistryManager&, onnxruntime::IExecutionProvider&)::CompilationEntry&]single_node_compute_func should have 1 element.PartitionOnnxFormatModelPartitionstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IExecutionProvider; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::IExecutionProvider]N11onnxruntime18IExecutionProvider13IKernelLookupEN11onnxruntime14FunctionKernelEN11onnxruntime12KernelLookupE*ZN11onnxruntimeL28PartitionOnnxFormatModelImplERNS_5GraphERNS_11FuncManagerERNS_21KernelRegistryManagerERNS_14KernelRegistryERNS_18IExecutionProviderENS_16GraphPartitioner4ModeERiRKSt8functionIFNS_6common6StatusES1_RbS9_RKSD_IFvRKS0_EEEESM_EUlS3_RKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISV_EEE0_*ZN11onnxruntimeL27PartitionOrtFormatModelImplERKNS_12_GLOBAL__N_115PartitionParamsERNS_21KernelRegistryManagerERNS_18IExecutionProviderEEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISE_EEE_e/framework/graph_partitioner.ccGetCapabilityForNo provider specstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]onnxruntime::KernelDefBuilder& onnxruntime::KernelDefBuilder::VariadicAlias(int, int)/onnxruntime_src/onnxruntime/core/framework/kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0 Version mismatch. node_version: kernel start version: kernel_end_version: MatchKernelDefTypestype_proto != nullptr),Kernel found kernel (node_version: /onnxruntime_src/onnxruntime/core/framework/kernel_registry.ccbool onnxruntime::{anonymous}::MatchKernelDefTypes(const onnxruntime::Node&, const std::unordered_map, std::vector >&, const onnxruntime::IKernelTypeStrResolver&, std::string&)static_cast(first_arg_idx) < actual_inputs.size()This op has been implemented only for the following types ( but the node in the model has the following type ( in the supported version range However the types are incompatible. Op with name ( domain ( and type ( kernel is not supported in Kernel not found Encountered following errors: ( However the types are incompatible.Op type (kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions. (node found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution ProviderFailed to find kFailed to find op_id: ' for node type 'Failed to find args for kernel type string 'If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.Kernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string./onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver.ccRegisterOpSchemaOp schema must be available.RegisterGraphNodeOpSchemasResolveKernelTypeStrargs is null.Duplicate entry for op id: Duplicate entry for kernel type str: N11onnxruntime22IKernelTypeStrResolverEN11onnxruntime21KernelTypeStrResolverEN11onnxruntime29OpSchemaKernelTypeStrResolverEFlatBuffers 1.12.0e/framework/kernel_type_str_resoResolveKernelTypRegisterNodeOpScop_kernel_type_str_args is null.kernel_type_str_kernel_type_str SaveKernelTypeStrResolverToBuffer/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver_utils.ccktsrLoadKernelTypeStrResolverFromBufferFailed to verify KernelTypeStrResolver flatbuffers data.AddLayoutTransformationRequiredOpsToKernelTypeStrResolverktsrL tT<|8$com.microsoft:NhwcMaxPool:1 D :Transpose:1lZT :Identity:1\4 :Squeeze:13H @ :Identity:14h<VP4 :Gather:13@4 :Gather:11d8& \< :Unsqueeze:13vpaxes :Unsqueeze:11 (80tHcom.microsoft:QLinearConv:1l T4y_scalex_scale  T3< T2dl@w_scaleh T1X :Squeeze:11X :Identity:19( :Transpose:13P>8t :Identity:16 V :Identity:13  :Unsqueeze:1<*$`8 :Gather:1vpTind  :Squeeze:1  T const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccnullptr != type_protokNU\8cjqx6ϭ=onnxruntime::NodeIndexInfo::Init(const onnxruntime::GraphNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init > >(const onnxruntime::ValidNodes >&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::onnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_type_proto is not of type map!static std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_map_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kMapTypetype_proto is not of optional typestatic std::unique_ptr OrtOptionalTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_optional_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypetype_proto is not of type sequence!static std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_sequence_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeThe type is not tensor, sparse tensor, sequence, map or optional type,4`Ծ׾־־`Ծ@վOrtValue is TensorSequence type but has no element Tensor DataType.static std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_typeinfo.ccTensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typetensor_data_type != nullptrG44G4G4پؾG4`׾4This TypeProto does not have ValueCase setstatic std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto&)Ӳ4ܾdz4dz4 Xdz4߾ܾ߾TempSpace allocator not foundCPU allocator not foundvirtual OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&)/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccvirtual OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int). Num args is Invalid arg_num of arg_num < arg_counts.size()virtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_N11onnxruntime15OpKernelContextEAttribute name and type don't matchNo attribute with this name is defined.GetAttrs failed. Expect values.size()= is defined.No attribute with name: but is of type: expected to be of type: Attribute: is expected to have type: Requested attribute: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper >; _Alloc = std::allocator > >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper >&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)/onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&]!using_counters_current <= buffer_size_std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemoryPattern&]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int]Can't slice a non-tensor OrtValue. Type was static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]OrtValue has not been allocated so can't be sliced.Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsTensor()ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizestatic onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]AddInitializer/onnxruntime_src/onnxruntime/core/framework/session_options.ccAn OrtValue for this name has already been added: Expecting same size spansinit_num == values.size()AddExternalInitializersonnxruntime::common::Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span >, gsl::span)St23_Sp_counted_ptr_inplaceIN11onnxruntime14LibraryHandlesESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEReceived nullptrReceived OrtValue is not a tensor. Only tensors Buffer containing the initializer must be owned constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]SetGraphAndCreateKernels must be called prior to GetExecutionInfo.const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const/onnxruntime_src/onnxruntime/core/framework/session_state.ccnode_index_info_.has_value()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]KernelUseSharedPrePackedBuffersThe kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weightsconst onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]CreateKernelsPruneRemovableAttributesfor node '' ('').removed removable attributes '), among attributes: failed at retrieving the removable attributesstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Failed to find input name in the mapping: Could not find an implementation for VerifyEachNodeIsAssignedToAnEpImpl) node with name 'VerifyEachNodeIsAssignedToAnEpSome nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.Node placements All nodes placed on []. Number of nodes: Node(s) placed on [PopulateKernelCreateInfovoid onnxruntime::AccumulateAllNestedSubgraphsInfo(const onnxruntime::SessionState&, const string&, size_t, onnxruntime::SubgraphsKernelCreateInfoMaps&)subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()Compute_Create_State_ for attribute Entry exists in node void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()CreateSubgraphSessionState!op_type.empty()+ used in the node: which is of op type: onnxruntime::SessionState::PrepackConstantInitializedTensors(onnxruntime::InlinedHashMap, long unsigned int>&, const std::unordered_map, const OrtValue*>&)::allocator_for_caching.get() != nullptr doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be emptyUsing cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the containerUsing an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)output_names_to_nodeinfo.empty()CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.FinalizeSessionStateImplsession.disable_prepacking Attribute:p_op_kernelsession.node_partition_config_fileconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ITensorAllocator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ITensorAllocator&]Missing session state for subgraph. Node:'onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string&, const onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, onnxruntime::InlinedHashMap, long unsigned int>&, const onnxruntime::InlinedHashMap, OrtDevice>&, bool)entry != node_to_subgraph_ss.second.cend()OuterScopeNodeArgLocationAccumulatorFinalizeSessionStateduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextEN11onnxruntime28IStreamCommandHandleRegistryEN11onnxruntime31StreamCommandHandleRegistryImplE*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapISs9OrtDeviceSaISt4pairIKSsSD_EEEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapISs9OrtDeviceSaISt4pairIKSsSD_EEEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRKNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRNS_14InlinedHashMapISsmSaISt4pairIS1_mEEEERKNSC_ISs9OrtDeviceSaISD_IS1_SI_EEEEbEUlS2_iRK8OrtValueRKNS_11OrtCallbackEbbE0_e/framework/sessVerifyEachNodeIs/onnxruntime_src/onnxruntime/core/framework/session_state_utils.ccDeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires , Got DeserializeTensorProtoExtDataTensorProtoToTensorFailed to copy tensor to input with name is not used by any node.SaveInputOutputNamesToNodeMappingSaving initialized tensors.SaveInitializedTensorsOrtValue index: Can not trace string tensor bytes for Deserialize tensor OrtValue indexes should have been populated.onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string&, const onnxruntime::GraphViewer&, const AllocatorPtr&, const onnxruntime::OrtValueNameIdxMap&, const std::vector&, onnxruntime::ITensorAllocator&, const SaveTensorFunction&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&, const MemoryProfileFunction&)ort_value_name_idx_map.MaxIdx() > -1Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied ( from initializer_allocation_order not found among initialized tensorsentry != initialized_tensors_to_allocate.end()entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates Skipping entry for missing optional value at idx Using user supplied initializer with name (Done saving initialized tensorsSt8functionIFvPvEEN11onnxruntime19session_state_utils19ExtDataValueDeleterE*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE_*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE0_St19_Sp_counted_deleterIPvSt8functionIFvS0_EESaIvELN9__gnu_cxx12_Lock_policyE2EEshape.Size() musFailed memory sistring tensor is not supported for copying betweion_state_utils.SaveInputOutputNamesToNodeMappinSaveInitializedTsession.use_device_allocator_forAllocateBufferValues size must be less than total buffer size: SparseTensor Allocation failed for size: the ort_value must contain a constructed sparse tensorstatic const onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/sparse_tensor.ccstatic onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue&)this tensor already has populated sparse_indicesMust contain Coo format. Got: onnxruntime::SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constFormat() == SparseFormat::kCooExpecting to contain one index, got: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Tensor&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]format_data_.size() == 1UMust contain Csr format. Contains: onnxruntime::SparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constFormat() == SparseFormat::kCsrcExpecting two indices. Got: format_data_.size() == 2UMust contain BlockSparse format. Got: onnxruntime::SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constFormat() == SparseFormat::kBlockSparseExpecting one index. Got: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Tensor&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]ValidateCsrIndicesdense shape must 2-D. Got: Expecting inner index size: the same as values size: rows: Inner and Outer indices must either be both zero or non-zeroOuter index count must be rows + 1 or zero. Got: This method should follow a call to constructor that supplies the allocatoronnxruntime::SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)Sparse format must not be set. Already contains format: Format() == SparseFormat::kUndefinedallocator_ != nullptrMakeCsrDataValidateBlockSparseShapesExpecting index blocks: Expecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2 to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}onnxruntime::SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)MakeBlockSparseDataThis method does not expect allocator to be setNot expecting an allocator setCopyData dst_size: Must have the same size. Got src_size: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Use MakeCsrStringsUse MakeBlockSparseStringsCopyDestination should be emptyMust have the same shapeThis instance should not be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeUnable to find a data transfer for copying from device type: to device type: Expecting data type to be set as stringIndex size: values_count == index_size must be equal to or twice the values size: std::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constonnxruntime::SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)MakeCooDataUse MakeCooStringse/framework/sparCurrently do not support dims higher than 2 dimensions: Expecting indices to be equal the number of values or be twice as many/onnxruntime_src/onnxruntime/core/framework/sparse_utils.ccSparseCooToDenseTensorUnsupported element size: Invalid index: > dense_size: Expecting inner indices to be same as nnz. Got: onnxruntime::common::Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const onnxruntime::DataTransferManager&, const onnxruntime::SparseTensor&, const AllocatorPtr&, const AllocatorPtr&, onnxruntime::Tensor&)inner_num == src.Values().Shape().Size()Outer indices must be M + 1. Got: outer_num == (rows + 1)SparseCsrToDenseTensorstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned char&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = short unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = short unsigned int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = unsigned int&]DenseTensorToSparseCooDenseTensorToSparseCsrInput must be ofUnable to convert strings tensor to a sparse tensor that is not SparseCooToDenseSupport 2-D matrDenseTensorToSpasor that not on tensor size overflowsize_t onnxruntime::Tensor::SizeInBytes() const/onnxruntime_src/onnxruntime/core/framework/tensor.ccStrided tensor is supported for training only for now.static size_t onnxruntime::Tensor::CalculateTensorStorageSize(onnxruntime::MLDataType, const onnxruntime::TensorShape&, gsl::span)tensor failed memory size calculationstrides.empty()shape.Size() must >=0void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t, gsl::span)Tensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t, gsl::span)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr, gsl::span)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, std::shared_ptr, ptrdiff_t, gsl::span)Trace/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hWeight buffer for initializer 'Get preallocated buffer for initializer 'Mem pattern for initializer ' is not found' failedFailed to get allocator for location: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]duplicated locationFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEInvalid dimension of dimension <= num_dims for SizeFromDimension. Tensor has int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constInvalid tensor shape slice argument.onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= values_.size()Tensor type is undefinedUnsupported indices_format passedconst onnxruntime::Tensor& {anonymous}::GetIndicesTensor(const OrtValue&, OrtSparseIndicesFormat)/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.ccthe ort_value must contain a constructed tensor or sparse tensorOrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)Argument is not a tensor UnpackTensor: the pre-allocate size does not match the size in protoInvalid TensorProtoI*IJIJ *IJ *II IJIJIJIJy]AA]%%A%]%yyAUnpackTensor: the pre-allocated size does not match the raw data size, expected data overflowTensor does not have external data to read from./onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.ccExternal data type cannot be UNDEFINED or STRING. external data size mismatch. Computed size: GetExternalDataInfo*/_ORT_MEM_ADDR_/*TensorProto: , external_data.length: onnxruntime::common::Status onnxruntime::utils::GetExtDataFromTensorProto(const onnxruntime::Env&, const char*, const onnx::TensorProto&, void*&, SafeInt&, onnxruntime::OrtCallback&)utils::HasExternalData(tensor_proto) are out of bounds or can not be read in full.GetExtDataFromTensorProtoGetFileLength for GetFileContentExternal initializer: size to read: given file_length: ReadExternalDataForTensornullptr == p_dataUnpackTensorWithExternalDataImpl) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackInitializerDataUnsupported type: 0P Pp0p!p!!CopySparseDataSparse Indices raw data size does not match expected.Sparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensoronnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, const onnxruntime::Path&, gsl::span, std::function)indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid SparseTensor indices. Should be rank 0 or 1. Got:SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Element_size of: TensorProtoToTensor() tensor shape mismatch! can not be writen into Tensor type string tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProto type KKIRJGJlHI(GEKHJyGJKKH_IFF(FTensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated bufferTensorProtoToMLValueConstantNodeProtoToTensorProtoUnsupported attribute value type of onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&, const string&)Constant node: has no data attributes in 'Constant' node '5^D_4a`5`_`55\`Must have a valid data typeHasDataType(dense_proto) data_type: onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&)DenseTensorToSparseTensorProto*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_External data type cannot be STRe/framework/tensGetExternalDataIUnpackTensorWithExternalDataImplUnpackInitializeInvalid SparseTensor indices. Should one of the following types: int8, int16, inonnxruntime::common::Status onnxruntime::DispatchStridedCopy(onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, std::ptrdiff_t, const TensorShapeVector&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >; std::ptrdiff_t = long int; onnxruntime::TensorShapeVector = absl::lts_20220623::InlinedVector]TransposeSingleAxisOutwards/onnxruntime_src/onnxruntime/core/framework/transpose_helper.ccvoid onnxruntime::TransposeSingleAxisOutwards(gsl::span, const onnxruntime::Tensor&, onnxruntime::Tensor&, size_t, size_t, const onnxruntime::TensorShape*, onnxruntime::concurrency::ThreadPool*)CalculateStaticCopyInfoForFeed/onnxruntime_src/onnxruntime/core/framework/utils.ccSNPEExecutionProviderInternalTestingExecutionProviderbool onnxruntime::utils::FinalizeCopyInfoForFeeds(gsl::span, std::vector&)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(gsl::span&, std::vector&)fetch_alloc_info.size() == copy_info.size()Failed to find allocator for device onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, onnxruntime::Stream*, std::vector*, std::vector*)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SrcDstPair; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IDataTransfer::SrcDstPair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SparseSrcDstPair; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::IDataTransfer::SparseSrcDstPair&]allocator != nullptrBatchOrCopyMLValueCopyOneInputAcrossDevices, this is not supported yet.copy_info.size() == num_feedsCopyInputsAcrossDevicesExecuteGraphImplCopyOutputsAcrossDevices while parent graph node running on device: Subgraph has nodes running on device: void onnxruntime::utils::UpdateWithParentStream(onnxruntime::DeviceStreamCollection&, onnxruntime::Stream*)onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, gsl::span, std::vector&, gsl::span, gsl::span)feed_streams.size() == num_feedsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]const OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::SessionState&, std::string_view)const OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, std::string_view)exec_plan_ptrFindDeviceForValueCalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfoExecuteGraphstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const OrtDevice*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtDevice*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Unsupported OrtValue type to copy between deviceDeviceBasedPartitionerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = absl::lts_20220623::InlinedVector >; _Alloc = std::allocator > >; std::vector<_Tp, _Alloc>::reference = absl::lts_20220623::InlinedVector >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]invalid value index: against size int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&)nullptr != tensor_type_basevoid onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap&, onnxruntime::NodeIndex)entry != kernel_create_info_map.cend()hkkXllHljjxkkstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Allocation Plan: Execution Plan: Index out-of-range!Start logic stream: on device: End logic stream : GenerateDeallocationPlanstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]::operator[](std::array<_Tp, _Nm>::size_type) const [with _Tp = unsigned char; long unsigned int _Nm = 400; std::array<_Tp, _Nm>::const_reference = const unsigned char&; std::array<_Tp, _Nm>::size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) [with _Tp = char; long unsigned int _Nm = 512; std::array<_Tp, _Nm>::reference = char&; std::array<_Tp, _Nm>::size_type = long unsigned int]const value_type& std::basic_string<_CharT, _Traits, _Alloc>::back() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&]incomplete UTF-8 string; last byte: 0x\u%04x\u%04xinvalid UTF-8 byte at index : 0x!empty()\ufffd�l܎ č 4cannot use insert() with ymonnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&)Can not find the node ComputeReuseCountp_providerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&, const KernelCreateInfoMap&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtDevice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtDevice&]void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const onnxruntime::GraphViewer&, const InitializedTensorSet&, const KernelCreateInfoMap&, const string&, size_t, std::vector >&)specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()Only tensors are supported for external outputs for now.onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::PlannerImpl::OrtValueInfo; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::PlannerImpl::OrtValueInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]!IsNonTensor(*node_output)cannot use operator[] with a string argument with thcannot create object from initializer listconstexpr const value_type& std::array<_Tp, _Nm>::operator[](std::array<_Tp, _Nm>::size_type) const [with _Tp = nlohmann::detail::dtoa_impl::cached_power; long unsigned int _Nm = 79; std::array<_Tp, _Nm>::const_reference = const nlohmann::detail::dtoa_impl::cached_power&; std::array<_Tp, _Nm>::size_type = long unsigned int]{}{ [][ "bytes": [], "subtype": {"bytes":[],"subtype":null}@X PdevicesSaveConfigCaught exception during saving DeviceBasedPartitioner config: C575+55555555std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = absl::lts_20220623::InlinedVector, 6, std::allocator > >; _Alloc = std::allocator, 6, std::allocator > > >; std::vector<_Tp, _Alloc>::reference = absl::lts_20220623::InlinedVector, 6, std::allocator > >&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = absl::lts_20220623::InlinedVector, 6, std::allocator > >; _Alloc = std::allocator, 6, std::allocator > > >; std::vector<_Tp, _Alloc>::reference = absl::lts_20220623::InlinedVector, 6, std::allocator > >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::DeviceBasedPartitioner::PartitionGraph(const onnxruntime::GraphViewer&, const onnxruntime::ExecutionProviders&, std::vector > >&, onnxruntime::ExecutionOrder)" in node-stream mapFailed to find node "iter != node_stream_map.end()lkBlllWlSm>m)mmlllmmm}mhm-lJn5n n nmmmllll_nykdkk|j|jn|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|jk|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|j|jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjnjjjjjjjjjjjjnjjtn,k,k,klkgggggxkgggfkgggggggkgggkgTkjAoAo o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o oo o o o o o o o o ooo o ooooooooooovnrnxrnnnnnnnn6rnnnnnnnrnnnnnqnnnnnnqnq8uvwwHxypyypzH{8u8u8u8u|ԞD$D LԞԞԞԞDPartitioner type is not DeviceBasedPartitionerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = signed char; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = signed char&]Number of streams does not equal to number of device types!void onnxruntime::DeviceBasedPartitioner::Initialize()node_names_by_stream_.size() == device_types_.size()CreateGraphPartitionerUse DeviceBasedPartition as defaultCaught exception when reading partition config file: void onnxruntime::PlannerImpl::PartitionIntoStreams(const onnxruntime::logging::Logger&, const onnxruntime::ExecutionProviders&, const PathString&)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)id >= 0 && static_cast(id) < ort_value_info_.size()Should not have entry in kernel create info with nullptr for kernel_defonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeValueLocation()Can not find the execution provider p_kernel_defComputeValueLocationOptimizeReusePlanForMultiStreamonnxruntime::common::Status onnxruntime::PlannerImpl::BuildExecutionPlan(const onnxruntime::ExecutionProviders&, const onnxruntime::IStreamCommandHandleRegistry&)execution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]BuildExecutionPlanCreatePlanComputeReusePlan reused by as inputonnxruntime::PlannerImpl::ComputeValueLocation()::found_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location map N11onnxruntime17IGraphPartitionerEN8nlohmann6detail23output_adapter_protocolIcEEN11onnxruntime22DeviceBasedPartitionerEZN11onnxruntime11PlannerImpl17ComputeReuseCountEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl20ComputeValueLocationEvEUlRKNS_7NodeArgEmE_ZZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvENKUlmE_clEmEUlmE_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlRKNS_7NodeArgEmE0_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE1_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE2_ZN11onnxruntime11PlannerImpl24GenerateDeallocationPlanEvEUlRKNS_7NodeArgEmE_N8nlohmann6detail21output_string_adapterIcSsEESt23_Sp_counted_ptr_inplaceIN8nlohmann6detail21output_string_adapterIcSsEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EE   000102030405060708091011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798990001020304050607080910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989900010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw 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, >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = std::unordered_map, onnxruntime::FuncManager::FuncInfo>; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = std::unordered_map, onnxruntime::FuncManager::FuncInfo>]Can't use func with null ptr*ZN11onnxruntime11FuncManager8GetFuncsERKSsRPKNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_*ZN11onnxruntime11FuncManager8GetFuncsERKSsRPKNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_*ZN11onnxruntime11FuncManager8GetFuncsERKSsRPKNS_15NodeComputeInfoEEUlPvE1_e/framework/libronnxruntime::HashValue onnxruntime::PrePackedWeights::GetHash() const/onnxruntime_src/onnxruntime/core/framework/prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()Unsupported device allocator in the context of pre-packed weights caching: onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const string&)/onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.cc*ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKSsEUliE_Number of streams: Begin executionExecuteThePlan/onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc_kernel_time_fence_after{"]}_fence_before does not.entry != nullptr node. Name:'' Status Message: launch kernel with idx All implicit inputs should have OrtValue instances by now. onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&, onnxruntime::Stream*)/onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hAsync Kernel Support is not implemented yet.onnxruntime::common::Status onnxruntime::ExecuteKernel(onnxruntime::StreamExecutionContext&, onnxruntime::NodeIndex, size_t, const bool&, onnxruntime::SessionScope&)Non-zero status code returned while running N11onnxruntime23OpKernelContextInternalE*ZN11onnxruntime14ExecuteThePlanERKNS_12SessionStateEN3gsl4spanIKiLm18446744073709551615EEENS4_IK8OrtValueLm18446744073709551615EEES6_RSt6vectorIS7_SaIS7_EERKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS7_RbEESt4hashImESt8equal_toImESaISt4pairIKmSR_EEERKNS_7logging6LoggerEPKNS_22DeviceStreamCollectionERKbbbEUlvE_SequentialExecute/framework/sequential_executor.input_type_shapeoutput_type_shapthread_schedulinN11onnxruntime21SimpleTensorAllocatorEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::StreamExecutionContext::CountDownBarrier; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::StreamExecutionContext::CountDownBarrier&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]onnxruntime::Stream* onnxruntime::StreamExecutionContext::GetDeviceStream(size_t)/onnxruntime_src/onnxruntime/core/framework/stream_execution_context.ccidx < device_stream_map_->NumStreams()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::const_reference = const std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = std::atomic; _Dp = std::default_delete []>; typename std::add_lvalue_reference<_Tp>::type = std::atomic&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::SequentialExecutionPlan::ReleaseAction&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::StreamExecutionContext::RecycleNodeInputs(onnxruntime::NodeIndex)frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()ort value releasedstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void onnxruntime::RunSince(size_t, onnxruntime::StreamExecutionContext&, onnxruntime::SessionScope&, const bool&, size_t)since == endvoid onnxruntime::StreamExecutionContext::CountDownBarrier::Set(int32_t)/onnxruntime_src/onnxruntime/core/framework/stream_execution_context.hv >= 0*ZN11onnxruntime18ScheduleDownstreamERNS_22StreamExecutionContextEmbRKbRNS_12SessionScopeEEUlvE_e/framework/stream_execution_conRecycleNodeInputExiting due to terminate flag being set to true.checksummodel format error! Need a key for the external or! Need a value for the externaor! Missing 'locError parsing node:Attribute expected to have tensor or sparse tensor type[TypeInferenceError] expected to have tensor or sparse type expected to have tensor typekey and value cache shall be 4 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensionsInputs 0 shall be 2 dimensionsqkv_hidden_sizes should have 3 elementsInvalid bias shapeInputs 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 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsError unexpected extra input in node:Number of attention headsmask_indexrelative_position_biaspresentWhether 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.The 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)Input tensor with shape (batch_size, sequence_length, input_hidden_size)Merged Q/K/V weights with shape (input_hidden_size, hidden_size + hidden_size + v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) for input projectionAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, total_sequence_length) or (batch_size, sequence_length, total_sequence_length), or index with shape (batch_size) or (2 * batch_size) or (3 * batch_size + 2)past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)additional add to QxK' with shape (batch_size, num_heads, sequence_length, total_sequence_length)When past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).3D output tensor with shape (batch_size, sequence_length, v_hidden_size)past state for key and value with shape (2, batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).Constrain input and output types to float tensors.Constrain mask index to integer types/onnxruntime_src/onnxruntime/core/graph/contrib_ops/bert_defs.ccInput tensor with shape (token_count, input_hidden_size)In packing mode, it specifies the offset of each token(batch_size, sequence_length).A tensor with shape (batch_size + 1). It specifies the cumulative sequence length.A tensor with shape (batch_size, num_heads, sequence_length, sequence_length)or (1, num_heads, sequence_length, sequence_length).It specifies the additional bias to QxK'2D output tensor with shape (token_count, v_hidden_size)token_offsetcumulative_sequence_lengthPackedAttentionQuery with shape (token_count, hidden_size) or packed qkv with shape (token_count, num_heads, 3, head_size)Key with shape (token_count, hidden_size)Value with shape (token_count, v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) from input projectionOffset of each token before packing, with shape (batch_size, sequence_length).It specifies the additional bias to QxK'. The shape is (batch_size, num_heads, sequence_length, sequence_length) or (1, num_heads, sequence_length, sequence_length)output tensor with shape (token_count, v_hidden_size)Constrain input and output to float tensors.Constrain mask, offset and sequence length to integer typesqueryPackedMultiHeadAttentionInput tensor with shape (batch_size, 1, input_hidden_size)Mask values of shape (batch_size, total_sequence_length)past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size). The first `batch_size * num_heads * max_sequence_length * head_size` elements correspond to keys and the next `batch_size * num_heads * max_sequence_length * head_size` elements correspond to values. The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.The beam width that is being used while decoding.If not provided, the beam width will be assumed to be 1.A buffer of shape [batch_size, beam_width, max_output_length] where an [i, j, k] entry specifieswhich beam the 'k' th token came from for the 'j' th beam for batch 'i' in the current iterationcache_indirectionCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Query with shape (batch_size, 1, hidden_size) or packed QKV with shape (batch_size, 1, 2 * hidden_size + v_hidden_size)Key with shape (batch_size, 1, hidden_size) for self attention or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionValue with shape (batch_size, 1, v_hidden_size) for self attention or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionMask values of shape (batch_size, total_sequence_length) or (batch_size, kv_sequence_length)past state for key with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.past state for value with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). When past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).Cross Attention doesn't need this input.past state for key with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).past state for value with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).past_keypast_valuepresent_keypresent_valueQuery with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape (batch_size, kv_sequence_length, num_heads, 3, head_size)Key with shape (batch_size, kv_sequence_length, hidden_size), or packed KV with shape (batch_size, kv_sequence_length, num_heads, 2, head_size), or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size)Value with shape (batch_size, kv_sequence_length, v_hidden_size), or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size)Key padding mask with shape (batch_size) or (3 * batch_size + 2) or (batch_size, kv_sequence_length)relative position bias: addition to QxK' with shape (batch_size, num_heads, sequence_length, total_sequence_length) or (1, num_heads, sequence_length, total_sequence_length)past state for self attention key with shape (batch_size, num_heads, past_sequence_length, head_size)past state for self attention value with shape (batch_size, num_heads, past_sequence_length, head_size)present state for cross attention key with shape (batch_size, num_heads, kv_sequence_length, head_size)or present state for self attention key with shape (batch_size, num_heads, total_sequence_length, head_size)present state for cross attention value with shape (batch_size, num_heads, kv_sequence_length, head_size)or present state for self attention value with shape (batch_size, num_heads, total_sequence_length, head_size)Constrain mask to integer typesOne sided attention windows length W, or half of total window length3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)Attention mask with shape (batch_size, sequence_length)Global attention flags with shape (batch_size, sequence_length)3D output tensor with shape (batch_size, sequence_length, hidden_size)windowglobal_biasglobalConstrain to integer types3D input tensor with shape (sequence_length, batch_size, hidden_size), hidden_size = num_heads * head_size3D input tensor with shape (total_sequence_length, batch_size, hidden_size)2D input tensor with shape (hidden_size, hidden_size)2D input tensor with shape (hidden_size, 2 * hidden_size)2D input tensor with shape (batch_size, total_sequence_length)input tensor with shape (batch_size, num_heads, sequence_length or total_sequence_length, head_size)If static_kv = true, cross-attention; else self-attentionIf use_past = true, use cache; else no cacheIf has_layer_state = true, layer_state = {} or [a,b]; else layer_state = None3D output tensor with shape (sequence_length, batch_size, hidden_size)output tensor with shape (batch_size, num_heads, new sequence_length, head_size)Constrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.q_weightkv_weightstatic_kvuse_pasthas_layer_statehas_key_padding_mask or nothas_key_padding_masknew_key_cachenew_value_cacheDecoderAttentionThe epsilon value to use to avoid division by zero.The mask index tensor type for shape inference (0: None, 1: 1D mask_index)2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)2D position ids with shape (batch_size, sequence_length) or (1, sequence_length)1D mask_index tensor with shape (batch_size)sum of word_embedding and position_embedding without layer normalizationConstrain input and output integer tensors typesConstrain input and output float tensors types.mask_index_typesegment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embeddingembedding_sumbias tensorConstrain input and output types to float or half tensors.Max distancemax_distanceDefault value is 0.is_bidirectionalbias_tablequery_lengthThe length of key.key_length2D input tensor with shape (num_buckets, num_heads), COL-major(See UT for example)The length of query. Self Attention requires query_length = key_length4D output tensor with shape (1, num_heads, sequence_length, sequence_length)Constrain sequence_length to int tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size) or (1, sequence_length, hidden_size) or (sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.Sum of the input and skip inputs (and bias if it exists) with shape (batch_size, sequence_length, hidden_size).Constrain mean and inv_std_var to float tensors.skipinv_std_varinput_skip_bias_sum3D input tensor with shape (batch_size, sequence_length, hidden_size)Or 2D input tensor with shape (token_count, hidden_size)3D output tensor with shape (batch_size, sequence_length, hidden_size)Or 2D output tensor with shape (token_count, hidden_size)Sum of the input and skip inputs (and bias if it exists)with shape (batch_size, sequence_length, hidden_size) or (token_count, hidden_size).SkipSimplifiedLayerNormalizationThe NGram size.scores_out2D input tensor with shape (batch_size, sequence_length)2D input tensor with shape (batch_size, vocab_size)2D output tensor with shape (batch_size, vocab_size)Constrain indices to integer typesConstrain scores input and output types to float tensors.The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.Predicted token ids from aggressive decodingDecoder input ids after merging predicted tokensmin_ngram_sizemax_ngram_sizeEncoder input ids.src_tokensDecoder input ids.cur_tokensPrevious suffix match indexprev_suffix_match_idxpred_tokensnew suffix match indexConstrain to integer types.GemmFastGeluInput tensor with shape (batch_size, sequence_length, hidden_size)Number of non-padding tokens in each sequence with shape (batch_size).output tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens, and those of padding tokens. Its shape is (batch_size, sequence_length)Cumulated sequence lengths. Its shape is (batch_size + 1)Max sequence length without padding. Its shape is (1)Constrain sequence_token_count and token_offset to integer typessequence_token_countcumulated_seq_lenmax_seq_lenRemovePaddingInput tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens and paddings. Its shape is (batch_size, sequence_length)output tensor with shape (batch_size, sequence_length, hidden_size)Constrain token_offset to integer typesRestorePaddingtensor with shape (batch_size, seq_len, num_heads x head_size) or (token_count, num_heads x head_size)1-d tensor with shape (num_heads x head_size)tensor with shape (1, num_head, seq_len, seq_len)gemm weight for the gated_ur_linear, shape (head_size, D), D is divisible by 2bias for the gated_ur_linear, shape (D)tensor of shape (1, num_heads, 1, 1)offset of each token with shape (batch_size, seq_len)output tensor with shape (batch_size, num_heads, seq_len, seq_len)query_layerquery_biasrel_poseco_aGatedRelativePositionBiasX_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) *ZN11onnxruntime7contrib11GetOpSchemaINS0_24Attention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30PackedAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39PackedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41DecoderMaskedSelfAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_46DecoderMaskedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33MultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DecoderAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23FastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35RelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31NGramRepeatBlock_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34BifurcationDetector_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GemmFastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28RemovePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29RestorePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_40GatedRelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_̼+̼+F?&TpxAD?inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensorfirst input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput 0 shall be 3 dimensionsInput rank must be >= 2. expected to have tensor or sparse tensor type: isinf_onlyisnan_onlyBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions. expected to have rank > but has rank Dimension mismatch in unification between 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64model_typeInputs 0 shall be 3 dimensions in whisper graphFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarInput axis is invalid: output_shapeoutput_padding'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesIncompatible dimensionsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorShapeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorShapeProto*&]pooled_sizeAttribute 'pooled_size' must be >= 1.max_output_boxesAttribute 'max_output_boxes' must be >= 1.transBtransAFirst input does not have rank 2Second input does not have rank 2 expected to have rank The inner-most 2 dimensions must have the same size (mat_w: != mat_h:Input tensors of wrong rank (0).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!Incompatible dimensions for matrix multiplicationblk_quant_typetransBatchAtransBatchBHalfOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) Alpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)Mean2D = ReduceMean (XU)MeanOfSquare = ReduceMean (Square)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Normalized = Div (Deviation, StdDev)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedV, Scale2D)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)NumReducedAxes = Neg (Axis1D)XShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()X2D = Flatten (X)XU = Cast (X2D)Square = Mul (XU, XU)Deviation = Sub (XU, Mean2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)The input data as Tensor.The output./onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.ccThe normal input data.The bias input data that is a 1D tensor.Alpha value.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.A 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.A 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.padding_idxscale_grad_by_freqTorchEmbeddingBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.upperapply softmax to elements for dimensions axis or highertrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1is_inner_broadcastThe bias (or mask) as Tensor.BiasSoftmax(Optional) Seed to the random generator, if not specified we will auto generate one.The bias input, a vector with the same shape as last dim of data OR same shape with dataThe residual input, must have the same shape as dataThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.seedresidualtraining_modeThe output mask of dropout.Constrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.If true, check only for Inf, -Inf.Constrain the output to a boolean tensor.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.If true, check only for NaN.Input tensors to check.IsAllFinitepadding_modeGridThree interpolation modes: bilinear (default), nearest and bicubic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.Input offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.4-D tensor of shape (N, C, H_out, W_out).Constrain input types to all tensor types.Constrain output types to float tensors.The id of the end-of-sequence tokenThe id of the token that indicates decoding starts.model type: 0 for GPT-2; 1 for encoder decoder like T5The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeThe sequence used as a prompt for the generation in the encoder subgraph. Shape is (batch_size, sequence_length)The maximum length of the sequence to be generated. Shape is (1)The minimum length below which the score of eos_token_id is set to -Inf. Shape is (1)Number of beams for beam search. 1 means no beam search. Shape is (1)The number of returned sequences in the batch. Shape is (1)Exponential penalty to the length. Default value 1.0 means no penalty.Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences.Shape is (1,)The parameter for repetition penalty. Default value 1.0 means no penalty. Accepts value > 0.0. Shape is (1)Mask of vocabulary. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (vacab_size)Mask of vocabulary for first step. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (batch_size, vocab_size)Custom attention mask. Shape is (batch_size, sequence_length)The forced input id sequence for the decoder subgraph. Shape is (batch_size, initial_sequence_length)Specific logits processor for different types of beamsearch models. Default value 0 means no specific logit processor. Accepts value >= 0. Shape is (1)Word IDs of generated sequences. Shape is (batch_size, num_return_sequences, max_sequence_length)Final beam score of the generated sequences. Shape is (batch_size, num_return_sequences)Processed beam scores for each vocabulary token at each generation step.Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam.Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)Constrain input type to float or int tensors.eos_token_idThe id of the padding tokenpad_token_iddecoder_start_token_idno repeat ngrams sizeno_repeat_ngram_sizeearly stop or notearly_stoppingrepetition_penaltyprefix_vocab_masklogits_processorsequences_scoresConstrain to float 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 sequence used as a prompt for the generation. Shape is (batch_size, sequence_length)Word IDs of generated sequences. Shape is (batch_size, max_sequence_length)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 samplingModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartPresence penalty mask. Shape is (batch_size, vocab_size)Seed for random number generator. Shape is (1)Filtered logits as input to the mutinomial function for debug purpose. Shape is (batch_size, vocab_size)temperaturetop_pmin_tokens_to_keeppresence_penaltyIf 1 custom sampling logiccustompresence_maskfiltered_logitsConstrain 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 tensorsinput tensor of size n in the signal dimnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)must be 0, normalization currently not supportedmust be 1, only one sided FFTs supportedoutput tensor of size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partssignal_ndimnormalizedonesidedRfftinput tensor with size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partsnumber of dimensions comprising the signaloutput tensor with size n in the signal dimIrfftinput_0input_1ComplexMulComplexMulConjConstrain input and output types to float tensorsactivation_paramsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionStrings to tokenizeTokenized stringsmarkpad_valuetokenexpseparatorsmincharnumInput/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 ignoredN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Quantization type1-dimensional data blobShape information of BB_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 multiplicationMatrix multiply resultsWhether 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_axisFusedMatMulActivation2-dimensional sparse matrix A. Either COO or CSR formatN-dimensional dense matrix Bsparse_tensor(float)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)An input tensor to hash.32-bit hash value.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.Tensor of rank r >= 1.Tensor of rank q >= 1.Tensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the 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.Specify batchs of sequence words to embeddingSpecify embedding vector of charconv_window_sizechar_embedding_sizeSpecify weights of convSpecify bias of convConstrain to tensor(int32).Constrain to tensor(float).Input tensor.Tensor after padding.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayTensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.2D matrix with shape (M,N)2D matrix with shape (K,N)The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.roisbatch_indicescrop_sizeScale tensor.Bias tensor.Output data tensor.InvStdDevEfficientNMS_TRTThe boxes input tensor.The scores input tensor.The anchors input tensor.anchorsnum_detectionsdetection_boxesdetection_scoresdetection_classesBackground class ID.background_classbox_codingBox IOU threshold value.iou_thresholdMax detections to output.plugin_versionscore_activationScore threshold value.score_thresholdMultilevelCropAndResize_TRTfeature_map_0feature_map_1feature_map_2feature_map_3patchesImage size.image_sizePooled size.PyramidROIAlign_TRTDisentangledAttention_TRTc2c_attentionc2p_attentionp2c_attentiondisentangled_attentionMaximum relative distance, k.spanfactorSnpepayload of the SNPE DLC file.DLCsnpe_versiontarget_devicenotesThe bit-packed output mask.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput data tensor from the previous layer.Saved inverse standard deviation used during training to speed up gradient computation.Constrain input X type to float tensors.Type of Mean and InvStdDev tensors.Constrain output Y, scale and bias type to float tensors.Constrain mean and inv_std_var to be float tensors.Constrain output Y and scale type to float tensors.The num_detections output tensor.The detection_boxes output tensor.The detection_scores output tensor.The detection_classes output tensor.Encoding type for the boxes or anchors inputs.Version number of the TRT plugin.Activation function to apply to the scores input.The first feature map input tensor.The second feature map input tensor.The third feature map input tensor.The fourth feature map input tensor.The cropped patches output tensor.content-to-content attention tensor, QcKc^T.content-to-position attention tensor, QcKr^T.position-to-content attention tensor, KcQr^T.The disentangled attention output tensor.Scaling 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.(Optional) SNPE version used to convert the model.(Optional) Target device like CPU, DSP, etc.(Optional) Some notes for the modelList of tensors for SNPE DLC inputOne or more outputs, list of tensors for DLC outputConstrain input and output types to uint8, uint16, float tensors.Constrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.*ZN11onnxruntime7contrib11GetOpSchemaINS0_19Gelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24QuickGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_22Inverse_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29TorchEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20Trilu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26BiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33BitmaskBiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26IsAllFinite_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GridSample_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25BeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GreedySearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23Sampling_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MaxpoolWithMask_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25ExpandDims_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Tokenizer_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MatMulInteger16_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MatMulFpQ4_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30TransposeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26FusedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36FusedMatMulActivation_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34SparseToDenseMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26MurmurHash3_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23GatherND_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_18Pad_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_21Unique_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28CropAndResize_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_;f?The epsilon value to use to avoid division by zeroThe number of groups of channels. It should be a divisor of the number of channels CActivation after group normalization: 0 for None, 1 for Swish1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Input data tensor. Dimensions are (N x H x W x C) when channels_last is 1 or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data1D gamma tensor for normalization with shape (C), where C is number of channels1D beta tensor for normalization with shape (C), where C is number of channelsThe output tensor of the same shape as XConstrain input X and output Y types to float tensors.Constrain gamma and beta to float tensors./onnxruntime_src/onnxruntime/core/graph/contrib_ops/diffusion_defs.ccGroupNormInput tensor. Dimensions are (N, S, D), where N is the batch size, S are image size, and D is hidden dimensionBias tensor. Dimensions are (D), where D is the same hidden dimension as input tensorThe output tensor with dimensions (N, S, D/2)BiasSplitGeluInput tensor. Dimensions are (N, S, C), where N is the batch size, S is image size H*W, and C is number of channelsBias tensor. Dimensions are (C)Residual tensor. Dimensions are (N, S, C)The output tensor with dimensions (N, S, C)BiasAddbias shall be 1 dimension*ZN11onnxruntime7contrib11GetOpSchemaINS0_28BiasSplitGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_Tensor must have at least 3 dimensions to convert between channels first and channels last.Output tensor must have at least 3 dimensionsN4onnx16InferenceContextEN11onnxruntime7contrib20NhwcInferenceContextE*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_118RegisterNHWCSchemaERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNCHWSchemaWithNHWCDomainERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNHWCSchemaWithActivationERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_tensor rank too smallinvalid scales dimensioninvalid scales valueFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_PFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE2_e/graph/contrib_ops/nchwc_schemaInput data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into.inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type./onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccx_scalex_zero_pointy_scaley_zero_pointInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.Scale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'. It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to signed/unsigned int8 tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input scale. It's a scalar, which means a per-tensor/layer quantization.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.w_scalew_zero_pointNhwcConvTensor to be added to the output, must be the same shape and format as the output tensor.*ZN11onnxruntime7contrib11GetOpSchemaINS0_26NhwcMaxPool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39QLinearGlobalAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33QLinearAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QLinearConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvENKUlRN4onnx8OpSchemaEE_clES3_EUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvEUlRN4onnx8OpSchemaEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28NhwcFusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_Input's shape must be 4-D) + bottom_border () + right_border () + scale[0] () + scale[1] ('Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () needs to be greater than or equal to the left_border (Value of alphaValue of beta1D input tensor1D output tensor/onnxruntime_src/onnxruntime/core/graph/contrib_ops/onnx_deprecated_operators.ccBias applied to each channel, same size as C.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputThe scale to apply.A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.endsSliced data tensor.The shape of filled tensorThe filled tensorinput_as_shapeextra_shapeGivenTensorFillInput data to be scaledOutput data after scalingBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.The previous GRU hidden state.Unactivated gate outputs from forget, update, and output gates, pre-activation.Array of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.The new GRU hidden state calculated by this op.drop_stateshidden_prevgatesseq_lengthshiddenGRUUnitIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.across_channelsnormalize_varianceScaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wise should specify a shapeAttribute Negative values are not allowed in a shape specification*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GivenTensorFill_Onnx_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26GivenTensorFill_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_15Crop_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_Constrain input and output types to all tensor tFGFFFFFFFFXFGFFFFH`HHIF{name}{additionalDocumentation}First operand.A_scaleA_zero_pointSecond operand.B_scaleB_zero_pointC_scaleC_zero_pointInput A's scale. It's a scalar, which means a per-tensor/layer quantization.Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Input B's scale. It's a scalar, which means a per-tensor/layer quantization.Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Result, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors. , 'axis' must be in []. Its actual value is: not specifiedValue of attribute should be of integer type and specify a type. does not specify a valid type. expected to have: or UNDEFINED. Got: Shape of quantized tensor must be 1D.Required attribute axis is missingAll inputs to Concat must have same rankCan't merge shape info. Both source and target dimension have values but they differ. Source=axis must be in [-rank, rank) Dimension=Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.axis must be in [-rank, rank-1]. input rank was 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.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors./onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.ccN-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.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.N-D full precision input tensor to be quantized.Constrain the input to float and bfloat.Constrain shape and strides to uint64.bfp_typeblock_dim1-D, contiguous BFP dataShape of xStrides of xConstrain y to uint8.QuantizeBFPThe datatype to dequantize to.1-D, contiguous, raw, BFP data to be de-quantized.strides of the original tensor.Constrain y to float and bfloat16.dtypeshape of the original tensor.de-quantized tensor.Constrain the input to uint8.DequantizeBFPAn input tensor.Reduced output tensor.reducedReduceSumIntegerConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.b_scaleb_zero_pointScale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationdata_scaledata_zero_pointreduced_scalereduced_zero_pointQLinearReduceMeanCoefficient of leakage.X_scaleX_zero_pointY_scaleY_zero_pointInput X's scale. It's a scalar, which means a per-tensor/layer quantization.Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output Y's scale. It's a scalar, which means a per-tensor/layer quantization.Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.apply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.Zero point tensor for input 'X'.It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. opsetThe input tensorinput_forgetsequence_lensinitial_hinitial_cR_scaleR_zero_pointY_hY_cSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Which axis to concat onY's scale.T8Y's zero point.TVConcatenated tensorList of tensors/scale/zero_point for concatenationConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8). When True (nonzero), yield x, otherwise yield yTensor of shape equal to the broadcasted shape of condition, X, and YconditionX's scale.X's zero point.Z's scale.z_scaleZ's zero point.z_zero_pointScale of quantized input 'A'. It is a scalar,which means a per-tensor quantization.Zero point tensor for input 'A'. It is a scalar.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Its type is int32_t and must be quantized with zero_point = 0 and scale = alpha / beta * a_scale * b_scale.Scale of output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Zero point tensor for output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.input_scaleweight_scaleinput_zero_pointweight_zero_pointCorresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)3D input tensor with shape (batch_size, sequence_length, input_hidden_size)2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.scale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.zero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to int8 tensors.Zero point for word embeddingsZero point for position embeddingsZero Point for segment embeddingsZero Point for 1D gamma tensorConstrain input and output types to float32 tensors.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantScale for word embeddingsword_embedding_scaleScale for position embeddingsposition_embedding_scaleScale for segment embeddingssegment_embedding_scaleScale for 1D gamma tensorgamma_scaleScale for 1D beta tensorbeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointZero Point for 1D beta tensorbeta_zero_pointLayerNorm Outputlayernorm_outMask Index Outputmask_index_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.TODO: input tensor of (ROWS, COLS). if less than 2d, will broadcast to (1, X). If 3d, it is treated as (B, ROWS, COS)Constrain Scale to float32 typesorder_inputorder_outputscale of the inputscale_inputConstrain to float typesQuantizeWithOrdercublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrdercublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y and optional matrix C2-dimensional matrix B. Transposed if order_B is ORDER_COL.scale of the input B. Scalar or 1-D float32.1d bias, not scaled with scale_Y.3d or 2d matrix C. if 2d expand to 3d first. Shape[0] should be 1 or same as A.shape[0] Constrain bias and scales to float32order_AcublasLt order of matrix Border_Border_Y3-dimensional matrix Ascale of the input A.scale_Ascale_Bscale of the output Y.scale_Yscale_CQOrderedMatMulHidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixscale of the input, scalar value (per tensor) currently.scale of the gemm - scalar (per-tensor quantization)2D input tensor with shape (input_hidden_size, hidden_size), where hidden_size = num_heads * head_sizescale of the weight (scalar for per-tensor quantization or 1-D of dims [hidden_size] for per-channel quantization)scale of the softmax result - scalar (per-tensor quantization)scale of the gemm - scalar (per-tensor quantization). Also this is the output scale for the operator.Attention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).additional add to QxK' with shape (batch_size, num_heads, sequence_length, sequence_length).Constrain scales to float32 tensors.order_weightcublasLt order of global biasscale_Q_gemmscale_K_gemmscale_V_gemmscale_Q_weightscale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale_QKT_softmaxscale_values_gemmQOrderedAttentioncublasLt 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 tensor, i.e., gamma vector.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization scale must be float tensors.quantization tensor must be int8 tensors.order_Xscale of the quantized Xscale_XQOrderedLayerNormalizationcublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.N-dimensional input Ascale of the input Ascale of the output YOutput of the GeluConstrain scales to float32QOrderedGelu1D input tensor with shape (3 * hidden_size), fp32 only currently.reserved. (not used as add bias need float value in cublasLt for normal order.)scale of the output for fused kqv gemmBe compatible with float version.order_global_weightscale of the weightscale_weightscale_biasscale_qkv_gemmscale of the global_weightscale_global_weightscale of the global_qkv_gemmscale_global_gemmscale of the outputscale_outputQOrderedLongformerAttentionInput data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axisQLinearConvTranspose*ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QuantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DequantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QuantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28DequantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MulInteger_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36DynamicQuantizeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35MatMulIntegerToFloat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QLinearReduceMean_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QLinearSoftmax_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28QLinearConcat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27QLinearWhere_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20QGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25QAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QuantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34DequantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QOrderedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41QOrderedLayerNormalization_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35QLinearConvTranspose_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_̼+ Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDolimitdelta1-D Tensor of the range.Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.Can not get shape initializer data!Unsupported non-raw-data data type!delta in Range operator can not be zero!Unsupported type:*ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionsThe past input shall be 5 dimensionsConstantstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int],8,,H,h8ForThisAndAllSubgraphs/onnxruntime_src/onnxruntime/core/graph/graph.cc) : ("") -> () Graph attribute inferencing failed: TypeProto must have shape for this to runconst onnx::TensorShapeProto& onnxruntime::utils::GetShape(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&]This is an invalid model. The sum of input arg count is not equal to size of input defs in node (const T* onnxruntime::ConstPointerContainer::at(size_t) const [with Container = std::vector; onnxruntime::ConstPointerContainer::T = onnxruntime::NodeArg; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hindex < data_.size()Serialization of fused function body is not currently supported, Removable attributes were removed before the node is saved.does not have the graph for key Node [] op_type [], could not find NodeArg fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [ReplaceInitializedTensorImplFailed to find existing initializer with name Trying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.graph_proto_ is not in sync with name_to_initial_tensor_onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)existing_entry != mutable_initializers.pointer_end()Input to set must exist.input->Exists()void onnxruntime::Graph::SetInputs(gsl::span)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::fbs::EdgeEnd; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::fbs::EdgeEnd&]Invalid node indexes specified when adding edge.Invalid source node arg slot specified when adding edge.void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.LoadEdgesFromOrtFormatinput index: input edgesoutput edges is not the same as this node's index:Invalid node indexes specified when removing edge.void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]No Graph instance was found for attribute std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Graph*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Graph*&]Some nodes are not included in the topological sort, graph have a cycle.void onnxruntime::Graph::KahnsTopologicalSort(const std::function&, const std::function&) const_token_Error: Duplicate definition-site for ( must be either specified in graph inputs or graph initializers.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = gsl::not_null&]Graph state to be loaded into must be empty.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph ctor should have created NodeArg for initializer. Missing:) 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. Subgraph output () does not exist in the graph.This is an invalid model. Graph output (node_argThis is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; std::deque<_Tp, _Alloc>::reference = long unsigned int&]This is an invalid model. Error: the graph is not acyclic.InitInputsInitializersOutputsTensor element type mismatch. Source and target must both be tensorsError merging shape info for output. '. Falling back to lenient merge. , or optional typed entities , or sparse tensorsMergeShapeInfo' source: target:Output:Type mismatch. Current= Input=UpdateTypeAndShape . Got: Optional Type mismatch. Expected: This is an invalid model. Model input () does not have type information.Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer ' has element type does not match. but different TensorProto.existing->second == &tensorAddInitializedTensor already has tensor with name void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)This is an invalid model. Node () does not have type information set by parent node.This is an invalid model. Type Error: Type 'std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]) does not match expected type () input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types (InferAndVerifyTypeMatchNode () output arg () type inference failedType Error: Type () of output arg () of node (Node:) Op (UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&)node.GetAttributeNameToMutableSubgraphMap().empty()InjectExternalInitializedTensorsReplaced external initializer: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&] as it still has output edges.bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node BuildConnectionsInvalid model. Node input ' Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()nullptr != func_meta_defgsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]LoadFromOrtFormatSerialization error. Graph attribute was serialized without Graph instancestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Node::LoadFromOrtFormat, input_arg_counts is missingDuplicate initializer (dense or ConstantNode): '' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): '. Please, fix your model.Node index is out of rangegraph_proto cannot be nullgraph_proto != nullptr' Model is invalid.utils::HasName(sparse_tensor)Initializer onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, bool)Duplicate constant node sparse initializer name: 'Sparse initializer must have a name. This model is invalidDuplicate sparse_tensor_initializer: 'This is an invalid model. Tensor does not have type information. appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.NoOponnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&)Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::OpSchema; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::OpSchema&]absl::container_internal::raw_hash_map<>::atsparse_tensor_names_ not in sync with name_to_initial_tensor_void onnxruntime::Graph::RemoveInitializedTensor(const string&)sparse_tensor_names_.count(tensor_name) == 0graph_proto_ is not in sync with name_to_initial_tensor_.!foundOuter scope node arg name ''was added but does not exist. void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constRemovable attributes were removed before the conversion is started.void onnxruntime::Node::ToProto(onnx::NodeProto&, bool) conststd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&]VerifyNodeAndOpMatchFatal error: ) attribute (, Error ) is not a registered function/op) is required but not specified.This is an invalid model. In Node, PerformTypeAndShapeInferencingSize mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. inputs but subgraph has inputs and requires Subgraph input missing type.InferAndVerifySubgraphTypesResolveShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&)outer_scope_node_args_consumed.empty()_inline_InlineFunction in inlined function: Constant node name: insert_result.second in inlined subgraph: Initializer name: Node has no function body and cannot be inlined.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&)callnode.TryGetFunctionProto(inlined_fp) conflicts with graph initializer. Check Specializing code.Failed to convert dense initializer to sparseonnx::GraphProto onnxruntime::Graph::ToGraphProto() constonnx::GraphProto onnxruntime::Graph::ToGraphProtoWithExternalInitializers(const string&, const PathString&, size_t) constToGraphProtoWithExternalInitializersexternal_stream.is_open()Cannot find NodeArgs for [Removing initializer 'Removing NodeArg 'void onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set >*)initializer_node_arg != nullptrCleanUnusedInitializersAndNodeArgs'. It is not used by any node and should be removed from the model.'. It is no longer used by any node.outer_scope_node_arg != nullptrFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEN4onnx15GraphInferencerEN4onnx24FunctionBodyBuildContextEN4onnx28FunctionBodyBuildContextImplEN11onnxruntime5GraphEN11onnxruntime19GraphInferencerImplEN11onnxruntime20InferenceContextImplEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_*ZN11onnxruntime5Graph49PopulateNodeArgToProducerConsumerLookupsFromNodesEvEUlRKNS_7NodeArgEbE_e/graph/graph.ccInjectExternalInitializedTensorsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]SaveInitializerOrtFormat/onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: ^OQRPTXRX[T`YNMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.l\$UNDEFINEDINTFLOATSINTSSTRINGSGRAPHSSPARSE_TENSORSPARSE_TENSORSe/graph/graph_flatbuffers_utils.LoadInitializerO*/_ORT_MEM_ADDR_LoadSparseInitiaEmpty graph proto from deserialization of ORT foLoadAttributeOrtvoid onnxruntime::GraphViewerToProto(const onnxruntime::GraphViewer&, onnx::GraphProto&, bool, bool)/onnxruntime_src/onnxruntime/core/graph/graph_proto_serializer.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Attempting to get an input that does not exist.const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccindex >= 0 && static_cast(index) < inputs.size()Attempting to get an output that does not exist.const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)index >= 0 && static_cast(index) < outputs.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&] Implicit input name cannot be safely updated to in one of the subgraphs.itr != node_args.end()Attempting to get index by a name which does not exist:int onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const string&, bool)void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) ImplicitInputs: ExplicitInputs:. Index:Invalid input index for node ->Failed since multiple edges matched:std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::graph_utils::GraphEdge&]Can only add a new input at the end of the current ones.void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)num_explicit_inputs == static_cast(target_input_idx)Initializer with same name exists. Name:onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)!graph.GetInitializedTensor(new_initializer.name(), existing)void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Node must only have one used outputbool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)e/graph/graph_utCanUpdateImplicitInputNameInSubgInvalid ExecutionOrderconst std::vector& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const/onnxruntime_src/onnxruntime/core/graph/graph_viewer.ccnodeargIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*)graph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: *ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_N11onnxruntime11NodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_N11onnxruntime19PriorityNodeCompareEtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Graph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Graph&]Protobuf parsing failed.ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()/onnxruntime_src/onnxruntime/core/graph/model_load_utils.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]/onnxruntime_src/onnxruntime/core/graph/model.cc is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Graph is null. Invalid ORT format model.ModelProto does not have a graph.onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unsupported model IR version: ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::SchemaRegistryManager; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnxruntime::SchemaRegistryManager]Missing model IR version., max supported IR version: No graph was found in the protobuf.Failed to load model with error: Load less than 0. failed. File doesn't existLoad model system error number is less than 0.Protobuf serialization failed.SaveModel is less than 0.SaveWithExternalInitializersSaveModelWithExternalInitializersSt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EELoadFromOrtFormastd::pair, onnx::AttributeProto>, false, true>, bool> onnxruntime::utils::SetNodeAttribute(onnx::AttributeProto, onnxruntime::NodeAttributes&)/onnxruntime_src/onnxruntime/core/graph/node_attr_utils.ccutils::HasName(attribute)AttributeProto must have a name.basic_string_view::substrInvalid OpIdentifier string: %s: __pos (which is %zu) > __size (which is %zu)Failed to parse since_version from LoadOpIdentifierOrtFormat/onnxruntime_src/onnxruntime/core/graph/op_identifier_utils.cce/graph/op_identSaveRuntimeOptimizationRecordToOrtFormat/onnxruntime_src/onnxruntime/core/graph/runtime_optimization_record_container.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::RuntimeOptimizationRecord; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::RuntimeOptimizationRecord&]LoadRuntimeOptimizationRecordFromOrtFormatAttempting to load runtime optimization records for a previously loaded optimizer: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]*ZN11onnxruntimeL40SaveRuntimeOptimizationRecordToOrtFormatERN11flatbuffers17FlatBufferBuilderERKNS_25RuntimeOptimizationRecordERNS0_6OffsetINS_3fbs25RuntimeOptimizationRecordEEEEUlmE_e/graph/runtime_optimization_record_container.ccDomain already set in registrystd::deque<_Tp, _Alloc>::const_reference std::deque<_Tp, _Alloc>::operator[](std::deque<_Tp, _Alloc>::size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::const_reference = const std::shared_ptr&; std::deque<_Tp, _Alloc>::size_type = long unsigned int]RegisterOpSchemaInternal, but it its domain is not/onnxruntime_src/onnxruntime/core/graph/schema_registry.cc, but it its version is higherthan the operator set version RegisterOpSetN4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime27OnnxRuntimeOpSchemaRegistryEN11onnxruntime21SchemaRegistryManagerEe/graph/schema_rRegisterOpSchemaQWMWmemory_seq_lensAWCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. initializer != nullptr is not found or is not constant initializer.onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&)/onnxruntime_src/onnxruntime/core/graph/function.ccN11onnxruntime8FunctionEN11onnxruntime12FunctionImplEseq(tensor(uint32))std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::InlinedHashMap, std::basic_string >; _Alloc = std::allocator, std::basic_string > >; std::vector<_Tp, _Alloc>::reference = onnxruntime::InlinedHashMap, std::basic_string >&]Number of actual parameters cannot exceed number of formal parametersvoid onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = false]/onnxruntime_src/onnxruntime/core/graph/function_utils.ccactuals.size() <= formals.size()void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = true]Tinall_tensor_types_ir4TAggregatedTypesinput_arg->Type() != nullptrstd::unique_ptr onnxruntime::function_utils::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, bool) is not found in model local functionsstd::unique_ptr onnxruntime::function_utils::CreateSchema(const string&, const string&, const onnxruntime::InlinedHashMap, const onnx::FunctionProto*>&, const std::unordered_map, int>&, const onnxruntime::SchemaRegistryManager&, const onnxruntime::logging::Logger&, bool), domain: The given function name: onnx_func_protoNo opset registered for domain onnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto&, std::unique_ptr&, const onnxruntime::InlinedHashMap, int>&, const onnxruntime::InlinedHashMap, int>&)::static_cast(i) < node_op_schema->inputs().size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair, std::basic_string >; _Alloc = std::allocator, std::basic_string > >; std::vector<_Tp, _Alloc>::reference = std::pair, std::basic_string >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] in function opset imports.it != opset_imports.end()domain_version != -1Too many inputs for op Toutstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::InlinedHashMap, std::basic_string >; _Alloc = std::allocator, std::basic_string > >; std::vector<_Tp, _Alloc>::reference = onnxruntime::InlinedHashMap, std::basic_string >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = onnxruntime::InlinedHashMap, std::basic_string >; _Alloc = std::allocator, std::basic_string > >]*ZN11onnxruntime14function_utilsL22IOTypeConstraintHelperERKN4onnx13FunctionProtoERSt10unique_ptrINS1_8OpSchemaESt14default_deleteIS6_EERKNS_14InlinedHashMapISsiSaISt4pairIKSsiEEEESI_EUlRKNS1_9NodeProtoEE_*ZN11onnxruntime14function_utils12CreateSchemaERKSsS2_RKNS_14InlinedHashMapISsPKN4onnx13FunctionProtoESaISt4pairIS1_S7_EEEERKSt13unordered_mapISsiSt4hashISsESt8equal_toISsESaIS8_IS1_iEEERKNS_21SchemaRegistryManagerERKNS_7logging6LoggerEbEUlRNS4_16InferenceContextEE_dims[d_i] < d_maxbool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*)/onnxruntime_src/onnxruntime/core/util/math_cpu.ccGemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = float; Provider = onnxruntime::CPUMathUtil]void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil]?Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringInvalid processor interval: processor_id > 0ReadThreadAffinityConfig, skip affinity setting, thread_pool_size: Processor id must consist of only digits: std::vector > onnxruntime::concurrency::ReadThreadAffinityConfig(const string&)/onnxruntime_src/onnxruntime/core/util/thread_utils.ccstd::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must start from 1: processor_from > 0 && processor_to > 0processor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]Found invalid processor id in affinity string: Failed to read affinities from affinity stringFound out-of-range processor id in affinity string: Number of affinities does not equal to thread_pool_size minus one, affinities: std::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(onnxruntime::Env*, OrtThreadPoolParams)actual_num_affinities == static_cast(options.thread_pool_size) - 1custom join thread function not setSize of affinity string must be ??@@*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_>Quant GEMM format: AIsSigned(), BIsSigned() is not supported on this device*Z13MlasGemmBatchRK28MLAS_GEMM_QUANT_SHAPE_PARAMSPK27MLAS_GEMM_QUANT_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z18MlasSymmQgemmBatchRK28MLAS_GEMM_QUANT_SHAPE_PARAMSPK27MLAS_SYMM_QGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_0Abad dimensions*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_?@@@@@@@bad mlas activation kind | D| 4AQ5@.N3;A8X} <2?~>++{16?:>=4?~??AAAAQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} 2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;AAAA33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KUB5?f933{@33{@33{@33{@l?l?l?l?A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>¥¥¥r1r1r1r1mL:mL:mL:mL:e9 *>*>*>>>>>rB¥B@K;?r1:n/ <*=(*>>??µµµrBrBrBrB::::n/ (*>(*>(*>38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSORElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!i͉U5"ٕ֚ƚ/CZ21MlasQ4GemmBatchDriverI24MLAS_Q4_GEMM_DATA_PARAMSEv19MLAS_BLK_QUANT_TYPEmmmmPKT_PN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_DGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_    Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK0EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK1EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK2EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK4EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_ B?@*Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_Default logger already set. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&)/onnxruntime_src/onnxruntime/core/common/logging/logging.ccISink must be provided.onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int)default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.SystemonnxruntimeN@Failed to parse path root: Parse/onnxruntime_src/onnxruntime/core/common/path.ccstatic onnxruntime::Path onnxruntime::Path::Parse(const PathString&)/\e/common/path.ccWriting profiler data to file ] {"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : } }, onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()/onnxruntime_src/onnxruntime/core/common/profiler.ccenabled_void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)session_logger != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::profiling::EventRecord; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::profiling::EventRecord&]Maximum number of events reached, could not record profile event.Apie/common/profileEndTimeAndRecordonnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&)/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)SystemError[ONNXRuntimeError]SUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROR8zdNtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat&]DistributionDistributionEnqueueWaitWaitRevokeUnknownEvent4D$std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]unnamed_thread_poolstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >; std::vector<_Tp, _Alloc>::reference = std::chrono::time_point > >&]"num_run": thread::id of a non-executing threadLogStart must pair with LogEndvoid onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)/onnxruntime_src/onnxruntime/core/common/threadpool.cc!points_.empty()void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()"thread_id": "", "block_size": [], "core": Profiler not started yet{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function&)n >= 0Nested parallelism not supportedonnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*)!current_parallel_section.has_value()!ps_More work items than threadsn <= num_threads_ + 1void onnxruntime::concurrency::ThreadPoolTempl::RunInParallel(std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]/onnxruntime_src/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.hvoid onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(onnxruntime::concurrency::ThreadPoolParallelSection&, std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjlEUljE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE21RunInParallelInternalERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjbSt8functionIFvjEEEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE26ScheduleOnPreferredWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionERN4absl12lts_2022062313InlinedVectorIiLm11ESaIiEEEjjSt8functionIFvjEEEUlvE_N5Eigen19ThreadPoolInterfaceEN11onnxruntime11concurrency27ExtendedThreadPoolInterfaceEN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEE*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE0_*ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_?j@?A@{Gz?N11onnxruntime7logging8CLogSinkE\/Failed to get symbol with error: lib.soFailed to load library Got null library handleFailed to unload library with error: file "" failed: realpathcloseopenInvalid fd was supplied: fstatNegative thread index is not allowedonnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&)/onnxruntime_src/onnxruntime/core/platform/posix/env.cccustom_create_thread_fn returned invalid handle.pthread_attr_init failed, error code: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: index >= 0 error msg: cpuinfo_initialize failedremove() failed. Error code: , path: munmap failed. error code: Failed to close file descriptor - error code: cpu , mask: , error code: does not exist, skipping it for affinity settingpthread_setaffinity_np succeed for thread: constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]pthread_setaffinity_np failed for thread: . Specify the number of threads explicitly so the affinity is not set.DeleteFolder(): nftw() failed with error: file_path == nullptroffset < 0length > buffer.size()lseekreadFile: , offset: , length: ReadFileIntoBuffer - unexpected end of file. mmapN11onnxruntime9EnvThreadEN11onnxruntime3EnvE*N11onnxruntime12_GLOBAL__N_111PosixThreadE*N11onnxruntime12_GLOBAL__N_18PosixEnvEReceived negative size from state/platform/posixReadFileIntoBuffMapFileIntoMemorN11onnxruntime9TelemetryE is not a valid year is not a valid day is not a valid dateVIWEFN11onnxruntime7logging11OStreamSinkEN11onnxruntime7EnvTimeE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeESaveTensorTypeAndShapeOrtFormat/onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.ccSaveTypeInfoOrtFormatSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatWe do not support type [] for nowSaveValueInfoOrtFormat is missing type info.SaveValueInfoOrtFormat: value_info_proto for Model must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.LoadTensorShapeOrtFormatLoadTypeInfoOrtFormatLoadSequenceTypeOrtFormatLoadMapTypeOrtFormat is not supported currentlyNull tensor type info. Invalid ORT format model.dim_param value with no name. Invalid ORT format model.LoadTensorTypeAndShapeOrtFormatNull sequence type info. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.Null map type info. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.Null type info for . Invalid ORT format model.LoadValueInfoOrtFormate/flatbuffers/flLoadOpsetImportOLoadTypeInfoOrtFLoadTensorDimensLoadSequenceTypeLoadMapTypeOrtFoLoadValueInfoOrtSparse tensor indices (Sparse tensor () index value at position [] out of range.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.) has values, but NNZ is ] out of range [0, ] not in sorted order.Field '' of 'value_info' is required but missing.elem_typekey_typevalue_type' is required to be non-empty.Unrecognized type value case (value_info name: 949494X94pvoid std::basic_string<_CharT, _Traits, _Alloc>::pop_back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator]Warning: Model contains experimental ops:data_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_dataTensorProto ( tensor name: ) should not be empty.) should be file inside the , but the ') should be stored in , but it is not regular file.TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'setting data_type field (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field.) is stored externally but doesn't have a location.Location of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: ' points outside the directory, but it doesn't exist or is not accessible.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: Y4x x x x x x x x x x x x sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.) dimensions are not positive.ATenConstantFillop_typeNodeProto (name: , type: No opset import for domain '' appeared multiple times.ai.onnx.trainingNo Op registered for with domain_version of ) has zero input and zero output. is deprecated in domain_version of init' of node: OpType: ==> Context: Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.Tensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input ' is not output of any previous nodes.' has been used as output names multiple times.Bad node spec for node. Name: attrAttribute (name: type field and data field mismatch in attribute ) should not contain more than one value field.) should refer to attribute in parent node.N4onnx7checker15ValidationErrorEstd::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&]Condition for the ifOnly boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch./build/Release/_deps/onnx-src/onnx/defs/controlflow/defs.ccv_initialv_final_and_scan_outputsA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].initial_state_and_scan_inputsfinal_state_and_scan_outputsnum_scan_inputsscan_input_directionsscan_output_directionsscan_input_axesscan_output_axestensor(bfloat16)tensor(complex64tensor(complex12tensor(float8e4mtensor(float8e5moptional(seq(tensor(complex64)))sor(complex128))optional(tensor(float8e4m3fnuz))float8e5m2fnuz))seq(tensor(uint8seq(tensor(uint1seq(tensor(uint6seq(tensor(int8)seq(tensor(int16seq(tensor(int32seq(tensor(int64seq(tensor(floatseq(tensor(doublseq(tensor(strinseq(tensor(bool)seq(tensor(complAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.All Tensor types is invalid for a tensor of rank axis value but subgraphs produce If node has then_branch and else_branch produce different number of outputs. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TypeProto*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] then=Mismatched type for output else=Mismatched tensor element type for output optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(bfloat16))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))optional(seq(tensor(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4./build/Release/_deps/onnx-src/onnx/defs/controlflow/old.ccAll Tensor types up to IRv4.Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).Int64 tensorAll Tensor typesFinal N loop carried dependency values then K scan_outputsAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.All Tensor and Sequence types was not a tensor.Scan input outputs. Expected was notGraph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output Number of scan input axes specified () is not equal to number of scan inputs () is not equal to number of scan outputs (Number of scan output axes specified (Loop 'body' subgraph outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output Invalid data type Invalid tensor data type seq(optional(map(sparse_tensor(Unsuported type proto value case.G85lrG85G85sTtG85ulvqDataTypeUtils::FromDataTypeString - Received invalid data type string 'optionalopaqueInput to 'Range' op should be scalars (Tensor with only one element and shape empty)All inputs to 'Range' op must be of the same typeInput tensor must be 2-dimensionalAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorsparse_valuevalue_intvalue_intsvalue_stringvalue_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.X_greater = Greater (X_random, input)X_random = RandomUniformLike (input)output = Cast (X_greater)The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor.Constrain input and output types to all tensor types./build/Release/_deps/onnx-src/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics.Constrain input types.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionhighThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.sample_size(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors.Scalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors. { 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) }> } Scalar. Value to step by.The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.All values in input have to be in the range:[0, 1].The returned output tensor only has values 0 or 1, same shape as input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliOutput type must be int32 or int64Input tensor must have rank 2*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data./build/Release/_deps/onnx-src/onnx/defs/generator/old.cc*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Result tensor.First input operand for the bitwise operator.Second input operand for the bitwise operator.First input operand for the logical operator.Second input operand for the logical operator.Constrain input to boolean tensor.Constrain output to boolean tensor./build/Release/_deps/onnx-src/onnx/defs/logical/defs.ccxorgreaterlessConstrain input types to all (non-complex) tensors.Constrain input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).less_equal { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } greater_equal { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to integer tensors.Constrain input to integer tensors.*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx25BinaryBitwiseDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25BinaryBitwiseDocGeneratorEPKcEUlRNS_8OpSchemaEE_types to all numEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./build/Release/_deps/onnx-src/onnx/defs/logical/old.ccConstrain input types to all numeric tensors.Constrain input to float tensors.Constrain input to integral tensors.*ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_The 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. A scalar value indicating the length of the window.A {name} window with length: size. The output has the shape: [size].output_datatypeperiodicoutput = Identity (input)input_less_than_min = Less (input, min)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)Rank of input Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)equationList of tensors for data_0Output tensor.Weight rank must be 1.reductionTarget rank must be 1 less than the input rank.Input and target dimension value mismatch. broadcasting: (Invalid rank for ) vs (Wrong op_type name for running propagation: frame_length input must be scalar.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!num_mel_bins input must be scalar.dft_length input must be scalar.size input must be a scalar.size input must be greater than 0.inverseaxis attribute value is_onesided and inverse attributes cannot be enabled at the same timedft_length input must be a scalar.Invalid value for attribute axisK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements./build/Release/_deps/onnx-src/onnx/defs/math/defs.ccsubtractionWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors.Dividend tensorDivisor tensorRemainder tensordivisionConstrain input and output types to signed numeric tensors. { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } MishThe exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XOutput tensor (same size as X) { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } slopeValue of alpha.Value of beta. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Constrain input and output types to numeric tensors.Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsSoftmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) normalized exponentialLogSoftmax(input, axis) = Log(Softmax(input, axis=axis))log of softmaxHardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwisehardmaxThe softsign (x/(1+|x|)) values of the input tensor computed element-wise { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Tensor of shape [a_1, a_2, ..., a_n, r]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.ValuessortedThe sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.N-dimensional quantized matrix azero point of quantized input aN-dimensional quantized matrix bzero point of quantized input bzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.scale of quantized input ascale of quantized input bscale of quantized output yZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyexclusiveConstrain input and output types to floating-point tensors.Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typeslossNegativeLogLikelihoodLossEinsum expression string.OperandsConstrain input and output types to all numerical tensor types.The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).labelslog_probSoftmaxCrossEntropyLossIf onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.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.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimentions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.The length of the signal.If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. The Fourier Transform of the input vector.If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.Constrain scalar length types to int64_t.dft_lengthHannConstrain the input size to int64_t.Constrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } 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) } 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) } 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. The number of bands in the mel spectrum.The size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.Lower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.The desired top edge of the highest frequency band.The Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].Constrain to any numerical types.num_mel_binssample_ratelower_edge_hertzupper_edge_hertzConstrain to integer tensors.Constrain to float tensorsIf 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.Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.The number of samples to step between successive DFTs.A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value. A scalar representing the size of the DFT. It's an optional value.The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.Constrain signal and output to float tensors.signalframe_stepframe_lengthoutput = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels) 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) Shape3Dlog_prob = Identity (X_Log)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_ReduceMax = ReduceMax (input, axes) 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) weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)expanded_target = Unsqueeze (target, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) input_gather_element = GatherElements (input, transform_targets)const_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)const_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_zeroconst_oneconst_ignore_indexconst_zero_floatconst_one_float X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) input and zero_point pair is expected to have same type.FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx29CosineSumWindowOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29CosineSumWindowOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_26MelWeightMatrix_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15STFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Pass 1 to enable broadcastingconsumed_inputslegacy optimization attribute.If set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as ADescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeThe input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)./build/Release/_deps/onnx-src/onnx/defs/math/old.cclogsoftmax1 for the first maximum value, and 0 for all others Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CNumber of top elements to retrieveDimension on which to do the sort.Invalid value for attribute k'shape' input must be 1D tensorX_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_Logconst_zero_castedconst_one_castedexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64transform_targetssqueeze_maskweight_gather_tempweight_gather_temp_1input_gather_element_transform*ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. to numeric tens to all numeric The input tensor of rank >= axis.The output values with the same shape as the input tensor.*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_p value of the Lp norm used to pool over the input data.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).pooled_shapespatial_scaleConstrain input and output types to float and 8 bit tensors.The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Input tensor must have at least 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionMaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput shape must have either [C] or [1,C] dimensions where C > 0ngram_indexesngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis./build/Release/_deps/onnx-src/onnx/defs/nn/defs.ccaverageThe output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.a filterThe weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD dataDilation 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.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.Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.offset_groupDeformConvlp poolThis operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.momentumScale tensor of shape (C).Bias tensor of shape (C).input_meaninput_varrunning_meanrunning_varThe input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The output mask.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain input to only numeric types. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } lambdA tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overOutput tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Scaling parameter.The exponent.Input for n-gram extractionNgram results1-D tensor of floatsmax_gram_lengthmin_gram_lengthmax_skip_countpool_stringsngram_countsInput is ether string UTF-8 or int32/int64Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)UTF-8 strings to normalizeUTF-8 Normalized stringscase_change_actionis_case_sensitivestopwordslocalestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } 1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).The shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.The shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.Output tensor produced by rearranging blocks into an image.Constrain input and output types to all numeric tensor types.image_shapeblock_shapeThe 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.Constrain input types and output Y type to float tensors.Tensor to be normalized.Normalized tensor.The number of groups of channels. It should be a divisor of the number of channels `C`.Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.Scale tensor of shape `(num_groups)`.Bias tensor of shape `(num_groups)`.The output tensor of the same shape as `X`.num_groupsGroupNormalizationThe pads attribute cannot be used simultaneously with auto_pad attributeAttribute pads must have an even sizeinput must have rank 3.Invalid value() for attribute 'axis'This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.sinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalization%s:%u: %s: Assertion `%s` failed.Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)FloatEpsilonEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()C = Shape (X)GroupSize = Div (C, NumGroups)N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (X, NewShape)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)Mean = ReduceMean (X3D, Axes2)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)ScaleShape = Constant ()ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)NumGroupsAxes2Square = Mul (X3D, X3D)Deviation = Sub (X3D, Mean)ScaleT = Cast (scale)BiasT = Cast (bias)*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_21DeformConv_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Col2Im_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29LayerNormalization_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29GroupNormalization_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Stride along each spatial axis.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter./build/Release/_deps/onnx-src/onnx/defs/nn/old.ccThe output of each pooling window is divided by the number of elements exclude pad.The output of each pooling window is maximum number of elements exclude pad.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.is_testsaved_meansaved_var Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain mean and variance types to float tensors. It allows all float type for U.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).ExponentX_RMEX_squaredE_XsquaredVarianceX_varianceProcessed_STD*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx26LpPoolOpSchemaGenerator_11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_11EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./build/Release/_deps/onnx-src/onnx/defs/object_detection/defs.ccoutput_heightoutput_widthsampling_ratioAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.max_output_boxes_per_classselected_indicescenter_point_box*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_/build/Release/_deps/onnx-src/onnx/defs/object_detection/old.cc*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information.Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.The input element.Type of the element in the optional outputThe optional output enclosing the input element./build/Release/_deps/onnx-src/onnx/defs/optional/defs.ccThe optional input.A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.Constrain output to a boolean tensor.Output element in the optional input.OptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.*ZN4onnx11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_type to all tensConstrain output type to all optional tensor or optional sequenctype to optional tensor and optional sequence ty type to all tensor or sequence Input must be an optional-type value containing an element with type information.OptionalHasElement is expected to have 1 input./build/Release/_deps/onnx-src/onnx/defs/optional/old.cc*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_ column: (line: Error context: [ParseError at position Incomplete string literal.Value expected but not found.Integer value expected, but not found.String value expected, but not found.Identifier expected but not found.Expected character Error parsing TensorProto (expected a tensor type).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Unexpected literal type.Unhandled type: %dCB,,,,,އ,CP߅߅complex64complex128float8e4m3fnfloat8e4m3fnuzfloat8e5m2sparse_tensorstype_protosir_versionopset_importproducer_nameproducer_versionmodel_versiondoc_stringmetadata_propsExpecting primitive type as map key type.Unexpected type in sparse-tensor element type.Unexpected type.stolUnexpected attribute type.Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor./build/Release/_deps/onnx-src/onnx/defs/quantization/defs.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.'y_scale' determines the output type.Quantized output tensorOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { 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) } *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_32DynamicQuantizeLinear_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./build/Release/_deps/onnx-src/onnx/defs/quantization/old.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.'axis' must be in [-rank(indices), rank(indices)-1]/build/Release/_deps/onnx-src/onnx/defs/reduction/defs.ccsum squareproductlog sumlog sum exponentL1 normL2 norm*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ { 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) } { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The type of the output tensor is integer.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).The axis in which to compute the arg indices.Constrain input and output types to high-precision and 8 bit numeric tensors./build/Release/_deps/onnx-src/onnx/defs/reduction/old.cc*ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_ to high-precision numeric tensoaxes as an input and attribute cannot be specified at the same time.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).*ZZN4onnx29ReduceDocGenerator_opset13_18EPKcbbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEENKUlRS6_E_clESD_EUlRNS_16InferenceContextEE_*ZN4onnx29ReduceDocGenerator_opset13_18EPKcbbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEEUlRS6_E_The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].First input tensor must have rank 3The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0./build/Release/_deps/onnx-src/onnx/defs/rnn/defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0The 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].*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if noutput_sequenceOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0./build/Release/_deps/onnx-src/onnx/defs/rnn/old.cc Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_Attributes:(unnamed)(no doc)(no type)Outputs:(no documentation yet)Defined at std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]ai.onnx.preview.training typestr: , has unsupported type: has inconsistent type has unsupported type std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnx::OpSchema::FormalParameter&]std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnx::FunctionProto; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; std::__shared_ptr_access<_Tp, _Lp, , >::element_type = onnx::FunctionProto]' has been deprecated since version is marked single but has an empty string in the graphMismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'type_proto'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs'' is expected to have field 'type_protos'Operator ' not in range [min=) has input size , max= not in allowed input sizes.) has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input ) has more outputs ()'s output Unrecognized attribute: for operator has unknown expected typeRequired attribute '' is missing.) 5^^^bbaa b,bLblb^bbONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()): failed validating the check: Duplicate type constraint nameError parsing function body:Extra unparsed input unexpected.Attribute specification type mismatch.Cannot find a function builder that satisfies the requested opset version: op_type = , opset_version = N4onnx16OpSchemaRegistryE*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_St15_Sp_counted_ptrIPN4onnx13FunctionProtoELN9__gnu_cxx12_Lock_policyE2EEAttribute dtype should be of integer type and specify a type.Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input type for input at index 0 is null. Type info is expected.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.new_axis], Value=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[Invalid value of attribute 'axis'. Rank=Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Constrain output types to any tensor type./build/Release/_deps/onnx-src/onnx/defs/sequence/defs.ccEmpty sequence.Tensors.Sequence enclosing the input tensors.Constrain input types to any tensor type.Input sequence.input_sequencepositionConstrain to any tensor type.Input tensor to be inserted into the input sequence.Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).The tensor to splitLength of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationconcat_resultThe 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.Additional inputs to the graphConstrain input types to any sequence type.Constrain to any tensor or sequence type.additional_inputsOutput sequence(s)out_sequenceSequenceMap expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphInput 0 expected but not providedExpected a sequence type for input 0Expected 1 or more inputs.Expected 1 or more outputs. expected but not providedSequenceMap_loop_body_itercount_cond_in_cond_outSequenceMap__seqlen_cond_seqempty*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input type was nullInput element type of Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Output was expected to have tensor type. Got expected to have tensor or sparse tensor type. Got: unknownElement type of input Mismatch between number of source and target dimensions. Source= expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input expected to have map typeKey type of map input Value type of map input expected to have type but instead is nullInput was expected to have map type. Got Key type of map input was unknownValue type of map input was unknownInput was expected to have either tensor, sequence, optional or map type. Got Input was expected to have sequence type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Element type of optional input was unknown target= source=Mismatched type:Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type.target sequence type missing element type.source optional type missing element type.target optional type missing element type.source map type missing key type.target map type missing key type.Mismatched map tensor key type:source map type missing value type.target map type missing value type.Lshape input must be 1D tensor'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.. Expected range [Unexpected axis value: 'step' cannot be 0 for SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsdata tensor must have rank >= 1axis must be in [-r, r-1]axis must be in [-rank, rank-1].OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1batch_dimsBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorAll inputs to Concat must have same rank. Input 'input' must have rank >= 2'sequence_lens' must have rank of 1Invalid attribute perm {}, input shape = { and Output Attribute perm for Transpose has repeated value: does not match type of output: expected to have tensor or sparse tensor typeAxis is referred to more than once.CenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputsNumber of elements of input 'shape' () does not match the number of axes (std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::TensorShapeProto_Dimension*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TensorShapeProto_Dimension*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]indices must be in [-rank, rank-1].Input rank for starts and ends should be the same: (Invalid position of 0.Invalid dimension value: Target shape may not have multiple -1 dimensions.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesoutput = Cast (input)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.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./build/Release/_deps/onnx-src/onnx/defs/tensor/defs.ccInput tensor to be cast.The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.Output tensor produced by casting the first input tensor to have the same type as the second input tensor.target_type(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Specified shape for output.Reshaped data.reshapedShape of the input tensor(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.Input tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationOptional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.One or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.stepsA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Transposed output.transposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.updatesType of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Tensor of rank q + r - indices_shape[-1] - 1.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.Tensors with at least max(dims) dimensions.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Reshaped tensor with same data as input.squeezedOriginal tensorexpandedList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Blocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].blocksizeDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Input tensor of any shape.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.N-D tensorN-D tensor after resizingTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Constrain input 'X' and output 'Y' to all tensor types.Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` .Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated. This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double.roigridSupport 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'' = 0.5.4-D tensor of shape (N, C, H_out, W_out) of sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.Constrain input `X` and output `Y` types to all tensor types.Constrain grid types to float tensors.Tensor to copy input into.Constrain input and output types to all tensor, sequence, and optional types.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrain to boolean tensors.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.depthConstrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.detect_positivedetect_negativeWhen True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.batch_axisTensor of rank r >= 2.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'inverse_indicesThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported 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], ] ``` A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.Input to extract the centered crop from.1-D tensor representing the cropping window dimensions.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.input_dataOutput data.output_dataCenterCropPadBoth 'split' input and 'num_outputs' attribute were givenMismatch between number of splits (Mismatch between the sum of 'split' () and the split dimension of the input (Attribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been given) and outputs (Blocksize must be positiveInput tensor must be 4-dimensionalSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthk2x_shape = Shape (input_data)axes_input = Constant ()x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)padded_sh = Max(x_shape, shape)pad_amount = Sub(padded_sh, x_shape)pad_amount_left = Div(pad_amount, k2)pad_amount_right = Sub(pad_amount, pad_amount_left)pads = Concat (pad_amount_left, pad_amount_right)padded_input = Pad (input_data, pads, , axes_input)x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)sh_diff = Sub (x_shape2, shape)start_dims = Div (sh_diff, k2)end_dims = Add (start_dims, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)padded_input = Pad (input_data, pads)x_shape2 = Shape (padded_input)output_data = Slice (padded_input, start_dims, end_dims)*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_Attribute value for pads is requiredAttribute pads has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthrank must be greater than axis'axis' must be in [-rank(indices)-1, rank(indices)]Ranks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Target shape may not have multiple -1 dimensionsInvalid Target shape product of 0Invalid position of 0values in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicates/build/Release/_deps/onnx-src/onnx/defs/tensor/old.cclength of each output. Values should be >= 0.1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
    The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
    if coordinate_transformation_mode is `"half_pixel"`,
    `x_original = (x_resized + 0.5) / scale - 0.5`
    if coordinate_transformation_mode is `"pytorch_half_pixel"`,
    `x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
    if coordinate_transformation_mode is `"align_corners"`,
    `x_original = x_resized * (length_original - 1) / (length_resized - 1)`
    if coordinate_transformation_mode is `"asymmetric"`,
    `x_original = x_resized / scale`
    if coordinate_transformation_mode is `"tf_crop_and_resize"`,
    `x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
    Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
    If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
    `out_size[d] = sizes[i]`
    If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
    `scale = Min(sizes[i] / in_size[d])`
    `out_size[d] = round_int(scale * in_size[i])`
    If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
    `scale = Max(sizes[i] / in_size[d])`
    `out_size[d] = round_int(scale * in_size[i])`
    For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
    The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
    if coordinate_transformation_mode is "half_pixel",
    x_original = (x_resized + 0.5) / scale - 0.5,
    if coordinate_transformation_mode is "pytorch_half_pixel",
    x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
    if coordinate_transformation_mode is "align_corners",
    x_original = x_resized * (length_original - 1) / (length_resized - 1),
    if coordinate_transformation_mode is "asymmetric",
    x_original = x_resized / scale,
    if coordinate_transformation_mode is "tf_crop_and_resize",
    x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
    The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
    if coordinate_transformation_mode is "half_pixel",
    x_original = (x_resized + 0.5) / scale - 0.5,
    if coordinate_transformation_mode is "pytorch_half_pixel",
    x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
    if coordinate_transformation_mode is "align_corners",
    x_original = x_resized * (length_original - 1) / (length_resized - 1),
    if coordinate_transformation_mode is "asymmetric",
    x_original = x_resized / scale,
    if coordinate_transformation_mode is "tf_half_pixel_for_nn",
    x_original = (x_resized + 0.5) / scale,
    if coordinate_transformation_mode is "tf_crop_and_resize",
    x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. May only be set if 'scales' is set to an empty tensor.constant_valueSupported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0paddingsNew shapeNumber of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.tilesAxis along which to repeat.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.width_scaleheight_scale4-D tensor, [N,C,H,W]The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Constrain input and output types to all tensor and sequence types. 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) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False). 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 input is not evenly splittableInput axes has invalid data'step' cannot be 0*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.1-D tensor of axes that `pads` apply to. 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. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).) is not equal to the existing dim value (Dimension value inferred (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 int64Pads has incorrect number of values. Expected 2 * values. Got 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' () must be same as rank of input 'X' (Number of elements of input 'scales' (*ZZN4onnx15PadDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx15PadDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE0_keep_aspect_rati is undefined so it cannot be parsed.ParseData type mismatch for tensor: load external data into raw data for tensor: Cannot parse data from external tensors. Please does not match the actual sizeThe type of tensor: . Expected: expected size Data size mismatch. Tensor: cast_tocats_int64scats_stringsExactly one of 'cats_*' attributes must be provided.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./build/Release/_deps/onnx-src/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.map(int64, string)map(int64, float)map_formmax_mapThe input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
    One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input data_UnusedOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
    One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
    One and only one of the 'default_*' attributes must be defined.A dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)string_vocabularyint64_vocabularyA 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
    One and only one of the vocabularies must be defined.An integer vocabulary array.
    One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listinputdimensionsData to be processed.Imputed output dataValue(s) to change toimputed_value_floatsA value that needs replacing.replaced_value_floatValue(s) to change to.imputed_value_int64sreplaced_value_int64The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Input data. It can be either tensor or scalar.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.A list of ints.A list of floats.A string.An integer.A float.default_floatData to be classified.coefficientsA collection of intercepts.interceptsmulti_classclasslabels_intsClassification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
    One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Weights of the model(s).Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
    One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
    One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
    One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
    Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
    Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
    Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
    If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
    One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
    One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
    One and only one of the 'classlabels_*' attributes must be defined.kernel_typekernel_paramsvectors_per_classsupport_vectorsprob_aprob_brhoRegression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score.
    One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Chosen support vectorsone_classSupport vector coefficients.n_supportsInput of shape [N,F]N, Top class for each pointTree id for each node.Feature id for each node.The class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
    One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
    This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
    One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesThe node id of each weightThe weight for each targetThe total number of targets.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
    This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target.
    One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
    One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
    One and only one of the 'classlabels_*' attributes must be defined.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Input's shape should be 1D or 2D*ZN4onnx11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.A list of labels.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input./build/Release/_deps/onnx-src/onnx/defs/traditionalml/old.cc*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_, node name: (op_type:sequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SET12515D25D251515D25 252515 is out of bounds.Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension type case mismatch. existing= inferred=key type mismatch from MapProto. existing=type case unsupported. existing=type case unsupported for symbolic shape inference. inferred=,X-,,x,x,,,-x,-p.------ .-.H/........011010111010111H23H2H2 2 2H2H22 2`3X4`3`38383`3`3483GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Data for input already exists.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorProto*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]. No opset import for domain optype Cannot infer type and shape for node name Shape inference error(s): Graph has inputs but were provided were provided.Cannot find missing input: in initializers. Cannot use the same name as both a subgraph initializer and subgraph input: The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: FvRN4onnx22DataPropagationContextEEN4onnx22DataPropagationContextEN4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplEN4onnx15shape_inference26DataPropagationContextImplEPFvRN4onnx22DataPropagationContextEEN4onnx12assert_errorEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::NodeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::NodeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]K0KKK@KPKK`KpKJtĝD$,tTԜttDtttt̙lĘX$ld$< tܭdĪd0`@x 00000@00000ж0000ȲصXHȲȲȲȲȲسl$::max() - SerialArena::kBlockHeaderSize): SsN6google8protobuf8internal19ImplicitWeakMessageE/build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl.ccCan't BackUp() after failed Next().CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19ZeroCopyInputStreamEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io19OstreamOutputStreamE/build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: 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_): N6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io26CopyingOutputStreamAdaptorE/build/Release/_deps/protobuf-src/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Can't message of type "" because it is missing required fields: string length exceeds max sizeN6google8protobuf11MessageLiteE(cannot determine missing fields for lite messagXP(/build/Release/_deps/protobuf-src/src/google/protobuf/parse_context.hCan't happenܢD[libprotobuf %s %s:%d] %s N6google8protobuf14FatalExceptionEINFOWARNINGFATAL/build/Release/_deps/protobuf-src/src/google/protobuf/stubs/stringpiece.ccsize too big: details: 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899/build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamE/build/Release/_deps/re2-src/re2/re2.ccUnknown encoding Error reverse compiling 'Error parsing '': Error compiling 'pattern too large - compile failedInvalid RE2: startpos: endpos: text size: Unexpected re_anchor value: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRE2: invalid startpos, endpos pair. [std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]/opt/rh/devtoolset-11/root/usr/include/c++/11/bits/stl_stack.hunexpected error/build/Release/_deps/re2-src/re2/regexp.ccUnexpected op in Regexp::Equal: aT`T```t`t`\`\`\``,aT`T`T`T`T`T`T`Lala`std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Regexp*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]NumCapturesWalker::ShortVisit calledRegexp not destroyed./build/Release/_deps/re2-src/re2/walker-inl.hStack not empty.Bad reference count Walk NULLstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::RuneRange; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::RuneRange&]N3re26Regexp6WalkerIiEEN3re217NumCapturesWalkerEno errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \invalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupno argument for repetition operator@B/build/Release/_deps/re2-src/re2/bitstate.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Job; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Job&; std::size_t = long unsigned int]GrowStack() failed: njob_ = job_.size() = typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Prog::Inst; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Prog::Inst&; std::size_t = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = const char*; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = const char*&; std::size_t = long unsigned int]Unexpected opcode: $44$std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]/build/Release/_deps/re2-src/re2/compile.ccshould never happenCompiler::Copy called!void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]No ranges in char classMissing case in Compiler: (@H(x(XpN3re26Regexp6WalkerINS_4FragEEEN3re28CompilerEunhandled opcode: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = int&; std::size_t = long unsigned int]/build/Release/_deps/re2-src/re2/dfa.cc7pp|4||||DeadState in RunStateOnByteNULL state in RunStateOnBytecontext does not contain textFailed to analyze start state.StateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Resetunhandled in Prog::Fanout()/build/Release/_deps/re2-src/re2/nfa.cc in AddToThreadqstd::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::NFA::Thread; _Alloc = std::allocator; std::deque<_Tp, _Alloc>::reference = re2::NFA::Thread&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]k`,cc::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Splice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Splice&]/build/Release/_deps/re2-src/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of AddFoldedRange recurses too much.AnyBad hex digit iVC0 Bad call to ParseState::ParsePerlFlagsRE2: unexpected op: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Frame&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Splice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Splice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]unknown round: $4l$DLuLlLtLmLoNlMnMcNdPcN3re216RepetitionWalkerE[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]\d\D\s\S\w\W09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az 09std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&]--$.$.$....const value_type& std::basic_string<_CharT, _Traits, _Alloc>::front() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = short unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = short unsigned int&; std::size_t = long unsigned int] std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]/build/Release/_deps/re2-src/re2/simplify.ccCase not handled in ComputeSimple: 9SLRLRLRLRRR|R|R|RR SLRLRLRLRLRLRLRLRRLRCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is W[YY@W8WWZYWWYYYYYYWSimplifyWalker::ShortVisit calledMalformed repeat Simplify case not handled: kjHhHhHhHhhhhhh`iiHhHhHhHhHhHhHhHhiHhvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; 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