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Current ORT Version is: %s basic_string::_S_createlengths allocation failedstring buffer allocation failedbasic_string::_S_construct null not valid%s:%d: %s: Assertion '%s' failed. vector::_M_realloc_insert__builtin_expect(!this->empty(), true)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::basic_string&]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_vector.h__builtin_expect(__n < this->size(), true)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Invalid 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 libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundmax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesInvalid key found: was false.Stacktrace: static void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(onnxruntime::logging::Severity)/onnxruntime_src/include/onnxruntime/core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.static void SafeIntExceptionHandler::SafeIntOnOverflow()/onnxruntime_src/onnxruntime/core/common/safeint.hInteger overflowstd::unique_ptr {anonymous}::GetDataTransfer(const OrtDevice&, const OrtDevice&)/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccNot able to find appropriate IDataTransfer to copy sparse datavector::reservebasic_string::appendopaque(,OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)get() != pointer()typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IOBinding; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IOBinding&]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/unique_ptr.hbasic_string::resizebasic_string::replace%s: __pos (which is %zu) > this->size() (which is %zu)basic_string::assignT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/ort_value.hTrying to get a Tensor, but got: IsTensor()this API does not support stringslocation dimensions do not match shape sizeinvalid location rangecannot 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]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]Trying to get a TensorSeq, but got: IsTensorSequence()const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]Trying to get a SparseTensor, but got: IsSparseTensor()the ort_value must contain a constructed tensorUseCooIndicesOrtStatus* 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*)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*&]static 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.hMap is missing type entry for its value++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]static 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]Sequence is missing type entry for its elementstatic 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 shapeonnxruntime::SparseTensor& {anonymous}::ValidateFillInputArgs(OrtValue*, const onnxruntime::TensorShape&, const OrtMemoryInfo*)Strings can only reside in CPU memorytried 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)OrtStatus* OrtApis::FillSparseTensorBlockSparse(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int32_t*)tried Filling sparse tensor with negative value in block sparse indices shapeFillSparseTensorBlockSparsesize overflownot enough space: expected 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 failedinput name cannot be emptyNULL input supplied for input output name cannot be emptystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtValue; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string]/onnxruntime_src/include/onnxruntime/core/framework/tensor.hTensor type mismatch. T !=utils::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.input array doesn't equal tensor sizeconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]len >= 0 && static_cast(len) < std::numeric_limits::max()const T* onnxruntime::Tensor::Data() const [with T = float]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]const T* onnxruntime::Tensor::Data() const [with T = double]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]const T* onnxruntime::Tensor::Data() const [with T = long int]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]const T& OrtValue::Get() const [with T = std::vector, float> >]onnxruntime::DataTypeImpl::GetType() == type_const T& OrtValue::Get() const [with T = std::vector >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const Cont&, size_t) [with T = std::map, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Input is not of one of the supported sequence types.Input is not of type sequence or map.Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]Tensor must always contain primitive types. Found: value_type != nullptrValue type is not supported yet: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = 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]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.void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/TensorSeq.hTensor sequence must contain only primitive typeselem_type_ != nullptrOrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)Expecting all elements to be tensors. Got: in[idx]->IsTensor()Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.void onnxruntime::TensorSeq::Add(const OrtValue&)TensorSeq: tensor to be added has a different data type.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.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 = float; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = float&]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>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = double; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = double&]const T& OrtValue::Get() const [with T = std::map]const OrtValue& onnxruntime::TensorSeq::GetAt(size_t) consti < tensors_.size()vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagPFvPvESt14default_deleteIN11onnxruntime18IExecutionProviderEESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEN3gsl15narrowing_errorESt19_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*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_*ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_/\basic_string::substrstatic 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.static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger()Unload__pos <= size()std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/basic_string.hT* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.hPlease fetch output tensor with specified shape.p_ml_valueconst void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constTensor type mismatch.type == dtype_void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)onnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&)Required output at index is not present.output_ptrconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Missing Input: const T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor]Required input at index input_ptrconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::SparseTensor]basic_string::insertvoid onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)Tensor size () != new size (shape_.Size() == new_shape.Size()libonnxruntime_providers_shared.soEnsurevoid onnxruntime::ProviderSharedLibrary::Ensure()Provider_SetHostGetonnxruntime::Provider& onnxruntime::ProviderLibrary::Get()GetProviderOrtSessionOptionsAppendExecutionProvider_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_OpenVINO: 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&&)IsSameDataType(tensor)void onnxruntime::TensorSeq::Add(OrtValue&&)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = bool]libonnxruntime_providers_cuda.solibonnxruntime_providers_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_policyE2EE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 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.QNNSNPEXNNPACKAZUREVitisAIUnknown provider name. Currently supported values are 'SNPE', 'XNNPACK', and 'AZURE'matching node is missinginvalid node input count: , expect: invalid node output counttypename 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&]/onnxruntime_src/onnxruntime/core/session/standalone_op_invoker.ccRegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'' op_type:virtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) constinvalid arg_num.arg_num < static_cast(input_count_)Create_State_Release_State_InvokeOpAttribute type not supported yet.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&]standalone_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&]CreateOpkernel already mapped to existing nodeN11onnxruntime10standalone23StandAloneKernelContextESt23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899CPUExecutionProviderstd::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]SyncProviders/onnxruntime_src/onnxruntime/core/session/IOBinding.ccSynchronizeInputsSynchronizeOutputsBindInputonnxruntime::common::Status onnxruntime::IOBinding::BindInput(const string&, const OrtValue&)Size mismatch: index= it.second=mapped_feed_names_.size() == feed_names_.size()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)Size mismatchmapped_output_names_.size() == output_names_.size()execution_mode is not validgraph_optimization_level is not validOrtSessionOptions& OrtSessionOptions::operator=(const OrtSessionOptions&)/onnxruntime_src/onnxruntime/core/session/abi_session_options.ccnot implementedRegisterCustomOpsLibraryFailed 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 pointersEnv 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 available12OrtAllocatorN11onnxruntime10IAllocatorEN11onnxruntime16OrtAllocatorImplEN11onnxruntime34OrtAllocatorImplWrappingIAllocatorEN11onnxruntime34IAllocatorImplWrappingOrtAllocatorE::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 enoughComputeAsync is not implementedIsCompatible/onnxruntime_src/onnxruntime/core/session/custom_ops.ccinput count does not matchstd::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 be of optional type input to be of variadic type input to be of single type input to keep same homogeneity input to keep same arityoutput count does not match output to be of optional type output to be of variadic type output to be of single type output to keep same homogeneity output to keep same aritystd::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]onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&)Unsupported version '' in custom op 'custom op registered at runtimeonnx::OpSchema onnxruntime::CreateSchema(const string&, const OrtCustomOp*)Only the last input to a custom op may be marked variadic.i == input_count - 1all typesOnly the last output to a custom op may be marked variadic.i == output_count - 1There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.1 == undefinedCreateCustomRegistrystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::OpSchema&]N11onnxruntime8OpKernelEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_ESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime20CreateCustomRegistryEN3gsl4spanIKP17OrtCustomOpDomainLm18446744073709551615EEERSt10shared_ptrINS_14CustomRegistryEEEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime22CreateKernelCreateInfoERKSsPK11OrtCustomOpEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISB_EEE_N3Ort9ExceptionE22OrtDefaultCpuAllocatorCpuvector::_M_range_insertSchema error: No allocator for this device has been registered for sharing.Only CPU allocators can be shared between multiple sessions for now.An allocator for this device has already been registered for sharing.Only CPU devices are supported for now. Target=Mismatch between source and target type. Source=[ShapeInferenceError] Unsupported Source/Target type=com.microsoftcom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwcorg.pytorch.atenunknownTrying to register schema with name (domain: version: ) from file line , but it is already registered from file , but its domain is not known by the checker., but its version is not in the inclusive range [, ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).intra-opinter-optensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))seq(tensor(bfloat16))MemcpyFromHost/onnxruntime_src/onnxruntime/core/session/environment.ccXYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostException caught: FvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEEZN4onnx19RegisterOpSetSchemaINS_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_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_operator()/onnxruntime_src/onnxruntime/core/session/inference_session.ccstd::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 instancesession.strict_shape_type_inferenceGetModelInputsModel was not loadedModel was not loaded.GetOverridableInitializersEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.offFlush-to-zero and denormal-as-zero are GetModelOutputsGetModelMetadataNewIOBindingSession was not initializedSession not initialized.Unexpected input data type. Actual: ()) , expected: (ShrinkMemoryArenasUnable to shrink arena: OrtMemoryInfo:[ id: OrtMemType: OrtAllocatorType:Device:[DeviceType: MemoryType: DeviceId: error message: Output vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:Cannot find execution provider SetTuningResultsInvalid TuningResults (index= does not support TunableOp.Failed to load TuningResults (index=). Reason: Correctly set TuningResults for , enable TunableOp for using%Y-%m-%d_%H-%M-%S.jsonconst onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr&)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid rank for input: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long unsigned int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] index: Size mismatch: feed_names has elements, but feeds has Invalid Feed Input Name:Input with name: is not expected to be of type tensor.const onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalTensor(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.hProvided type is not an optional tensorIsOptionalTensor(type)ValidateInputs is not expected to be of type sparse tensor.sparse_tensor is not expected to be of type tensor sequence.const onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(onnxruntime::MLDataType)Provided type is not an optional sequence 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_directlystd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::TuningResults; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::TuningResults&]gpuUnsupported 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: cpu_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-10/root/usr/include/c++/10/bits/shared_ptr_base.hpost_layout_transform_step_.onnxonnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph&, bool):: mutable[json.exception.] FeedsFetchesInfo/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)AddPredefinedTransformersRunReplaying the captured CUDA Graph for this model with tag: onnxruntime::common::Status onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph()/onnxruntime_src/onnxruntime/core/session/inference_session.hIsGraphCaptured()Cached 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]Running with tag: disable_synchronize_execution_providersmodel_runStart the second Run() to capture the graph. The first one is for necessary memory allocation;The second one is for capturing the graph.Encountered unknown exception in Run()Provider has already been registered./onnxruntime_src/onnxruntime/core/framework/execution_providers.hAddReceived nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.DmlExecutionProviderHaving 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.session.disable_quant_qdqQDQ 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&]LoadWithLoaderThis session already contains a loaded model.Exception during loading: Unknown exceptionEncountered unknown exception in LoadWithLoader()model_loading_uri failed:ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().model_loading_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoMemcpyTransformerTransformGraphsession.debug_layout_transformationCastFloat16Transformerstd::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<>&]The only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&)ModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&)Could not finalize session options while constructing the inference session. Error Message: status.IsOK()graph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()session.force_spinning_stopConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinningsession--intra-opsession.dynamic_block_basestoiDynamic block base set to session.intra_op_thread_affinitiesAffinity string must not be empty!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fnsession.inter_op.allow_spinning-inter-opcustom 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()session.set_denormal_as_zeroFailed to load model because protobuf parsing failed.LoadOrtModelWithLoaderThis session has already been initialized.ORTMORT 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_arrayonnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const PathString&)Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, std::istream&)Could not parse model successfully while constructing the inference sessionst.IsOK()onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int)resultError during EndProfiling(): ~InferenceSessionUnknown error during EndProfiling()SaveToOrtFormatFailed to save ORT format model to file: InitializeInitializing session.Session has already been initialized.Adding default CPU execution provider.session.use_env_allocatorsThis session will use the allocator registered with the environment.session.save_model_formatoptimization.minimal_build_optimizations value of 'save' is only valid when saving an ORT format model.Invalid value for CUDAExecutionProviderThis 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 the graph nodes have not been partitioned to the CUDA EP.This session will use the CUDA Graph feature as requested by the user.PartitionOrtFormatModelApplyOrtFormatModelRuntimeOptimizationsUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerSerializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.Session successfully initialized.session_initializationException during initialization: Encountered unknown exception in Initialize()applyFN11onnxruntime6common6StatusERNS_5GraphERbRKNS_18IExecutionProviderERKSt8functionIFvRKS2_EEESt14default_deleteIN11onnxruntime5ModelEESt14default_deleteIN11onnxruntime20CPUExecutionProviderEESt18bad_variant_accessN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN8nlohmann6detail9exceptionEPFN11onnxruntime6common6StatusERNS_5GraphERbRKNS_18IExecutionProviderERKSt8functionIFvRKS2_EEESt19_Sp_counted_deleterIPN11onnxruntime20CPUExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE?@@B*ZN11onnxruntime16InferenceSession10InitializeEvEUlRKN4onnx8OpSchemaEE0_*ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_*ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_*ZN11onnxruntime16InferenceSession13LoadOnnxModelERKSsEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlRKS1_E1_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE0_*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_vector::_M_insert_auxout_of_rangeinvalid_iteratortype_errordiscardednullnumberbinarybooleanarrayobjectmap::atcannot use at() with key '' not foundparse_errorparse error at line , column end of inputnull literaltrue literal'{'string literal'['unknown token':'',''}'number literal']'false literal'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected cannot get valuetype must be string, but is std::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<>*&]void 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 valueiterator out of range__position != end()std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator) [with _Key = std::basic_string; _Val = std::pair, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st, nlohmann::basic_json<> > >; _Compare = std::less; _Alloc = std::allocator, nlohmann::basic_json<> > >; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree, std::pair, nlohmann::basic_json<> >, std::_Select1st, nlohmann::basic_json<> > >, std::less, std::allocator, nlohmann::basic_json<> > > >::iterator]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_tree.hcannot use erase() with type must be number, but is Sequential modeParallel modefalseThe Model Proto hasn't been checked for the ORT config json.session_optionsParseSessionOptionsFromModelProto/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_threadsintra_op_num_threads option in the model file must be an integerSetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to inter_op_num_threadsinter_op_num_threads option in the model file must be an integerSetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_modeexecution_mode option in the model file must be an integerSetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to graph_optimization_levelgraph_optimization_level option in the model file must be an integerSetGraphOptimizationLevelSetting 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_profilingenable_profiling option in the model file must be an integerSetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to 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 number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator]invalid string: ill-formed UTF-8 byteinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Fn_}iDxIQ""""""""""""""""""""""""""""""N--------------s888dinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literalobject keyobject separatornumber overflow parsing 'The Model Proto has already been checked for the ORT config json.ort_configParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: type must be object, but is validatorstype must be array, but is tuning_resultsParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelTuning results stored in the model file cannot be parsed. Error message: . Ignoring...N8nlohmann6detail11parse_errorEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail10type_errorEN8nlohmann6detail12out_of_rangeEApply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApplyTransformers/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccThis transformer is already registered ROCMExecutionProviderACLExecutionProviderArmNNExecutionProvider_RuleBasedTransformerLevelEliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutExpandEliminationCastEliminationNoopEliminationDivMulFusionFuseReluClipGemmSumFusionGemmTransposeFusionNotWhereFusionConvAddFusionConvMulFusionConvBNFusionClipQuantRewriteReluQuantRewriteonnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&)/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccUnsupported optimization level: GenerateRuleBasedGraphTransformerstd::unique_ptr onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&, const onnxruntime::InlinedHashSet >&)session.qdqisint8allowedsession.enable_quant_qdq_cleanupQDQFinalCleanupTransformeronnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const SatApplyContextVariant&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)session.disable_double_qdq_removerDoubleQDQPairsRemoverConstantSharingCommonSubexpressionEliminationMatMulAddFusionReshapeFusionQDQPropagationTransformerRocmBlasAltImplTransposeOptimizeroptimization.enable_gelu_approximationQDQS8ToU8TransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionGeluFusionLayerNormFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionGatherToSplitFusionGatherToSliceFusionMatmulTransposeFusionBiasGeluFusionBiasSoftmaxFusionBiasDropoutFusionSkipLayerNormFusionFastGeluFusionQuickGeluFusionMatMulScaleFusionMatMulActivationFusionGeluApproximationonnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)N11onnxruntime11RewriteRuleEYieldOpstd::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]onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const/onnxruntime_src/include/onnxruntime/core/graph/graph.hValidating no unexpected access using an invalid node_index. Got: Max:node_index < nodes_.size()N11onnxruntime17EliminateIdentityEstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::const_reference = const int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]bool onnxruntime::IsIsolatedFp16NodeOnCpu(const onnxruntime::Node&, onnxruntime::Graph&, const onnxruntime::KernelRegistry&)/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.ccidx < input_defs.size()Recurse/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.hApplyImplCastInsertedCast_cast node to cast from float16 to float32 on cpudtypevirtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()RemoveDuplicateCastTransformerN11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerE/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.ccReduceMeanSubDivSqrtPowfused LayerNorm subgraphs epsilonstash_typeSimplifiedLayerNormalizationN11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionE    with activation activation_/onnxruntime_src/onnxruntime/core/optimizer/matmul_activation_fusion.ccFusedMatMulSoftmax_FusedActivationFusedMatMulActivationactivationN11onnxruntime22MatMulActivationFusionE_FusedActivation /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add N11onnxruntime15MatMulAddFusionE /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.ccMatMulIntegerMatMulIntegerToFloatN11onnxruntime26MatMulIntegerToFloatFusionEstd::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&]std::optional onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.ccConstant initializer NodeArg shape should not be null. NodeArg: void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int]Unsupported data type: GetScalarConstantInitializerstd::optional > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)div_inputs.size() == 2mul_inputs.size() == 2_FusedMatMulAndScalestd::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() == 1alphaFused MatMul and ScaleN11onnxruntime17MatMulScaleFusionE 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-10/root/usr/include/c++/10/bits/stl_deque.hstd::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&)!node_consumers.empty()_transformedonnxruntime::Node* onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph&, onnxruntime::Node*, onnxruntime::InlinedHashMap&, std::deque&, bool&, bool&)cast != nullptrCreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodestransBatchAtransBatchBMatMul_With_Transposefused MatMul and Transpose transAtransBN11onnxruntime21MatmulTransposeFusionEReorderOutputchannels_laststridesdilationskernel_shapeSAME_UPPERSAME_LOWERVALIDreorderReorderInputConvnearestlinearcoordinate_transformation_modeasymmetricalign_cornersnearest_modefloor_nchwcUpsamplescalesgroupvoid onnxruntime::NchwcTransformerImpl::TransformConv(onnxruntime::Node&)/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.ccBuffer overflowoutput_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]reshape_bn_nchwcspatialbn_scalebn_BFusedConvMaxPoolAveragePoolConcatReluSigmoidTanhBatchNormalizationResizeGlobalMaxPoolGlobalAveragePoolN11onnxruntime16NchwcTransformerE     /onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_layout_transformation::api::GraphRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_layout_transformation::api::GraphRef&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_layout_transformation::api::NodeRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_layout_transformation::api::NodeRef&]NhwcTransformerQLinearConvNhwcFusedConvFN26onnx_layout_transformation15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setISsSt4hashISsESt8equal_toISsESaISsEEEN11onnxruntime15NhwcTransformerEPFN26onnx_layout_transformation15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setISsSt4hashISsESt8equal_toISsESaISsEEEN11onnxruntime15NoopEliminationEWhereNotstd::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&]N11onnxruntime14NotWhereFusionEQuantizeLinearbool onnxruntime::GetQConstantLowerUpper(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/clip_quantizelinear.ccUnexpected data type for QuantizeLinear input y_zero_point of N11onnxruntime15ClipQuantFusionE  EnsureUniqueDQForNodeUnit/duplicatedDequantizeLinearAdded by /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/ensure_unique_dq_for_node_unit.ccEnsureUniqueDQForEachExplicitOutputEdgeN11onnxruntime25EnsureUniqueDQForNodeUnitE/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_final_cleanup.ccCleanUpNodeSequenceCleaning up back-to-back nodes: with name "" and N11onnxruntime26QDQFinalCleanupTransformerE*ZN11onnxruntime12_GLOBAL__N_119CleanUpNodeSequenceENS0_12NodeSequenceERNS_5GraphEmRKNS_7logging6LoggerEEUlRKSsE_static 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()const onnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const onnxruntime::Graph&, onnxruntime::graph_utils::ExtendedGraphEdge::End) constthis->_M_is_engaged()constexpr 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-10/root/usr/include/c++/10/optionalInvalid node index node != nullptrSqueezeUnsqueezestatic 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_q_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.ccAt least one graph node must be specified in the propagation edge.src_node || dst_nodeInsertQDQPairInserting Q/DQ pair between node ("", index: and at NodeArg "_q_to_dq_post_dq_qInserted by QDQPropagationTransformerFailed to set op schema for added Q node.Failed to set op schema for added DQ node.constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge; _Dp = std::_Optional_base]PropagateQBackwardPropagateDQForwardN11onnxruntime25QDQPropagationTransformerE *N11onnxruntime12_GLOBAL__N_130GraphConstantInitializerGetterE/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quantN11onnxruntime20QDQS8ToU8TransformerEbool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const onnxruntime::Node&, const GetConstantInitializerFn&, bool&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_util.ccq_or_dq_input_defs.size() >= 2N11onnxruntime15ReluQuantFusionEweight_zp_s8_2_u8_s8_2_u8GatherdropDQArgMax1DQLeakyRelu2DQQDQSelectorActionTransformerdropSplitQDQ*DQN11onnxruntime28QDQSelectorActionTransformerEN11onnxruntime3QDQ20DropQDQNodesSelectorEN11onnxruntime3QDQ19DropDQNodesSelectorEN11onnxruntime3QDQ13UnarySelectorEN11onnxruntime3QDQ14BinarySelectorEN11onnxruntime3QDQ13WhereSelectorEN11onnxruntime3QDQ14MatMulSelectorEstd::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*ZNK11onnxruntime3QDQ23DropDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKSsE_*ZNK11onnxruntime3QDQ24DropQDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKSsE_/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/shared/utils.ccValidateNodeGroupDQNodesQDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: /onnxruntime_src/onnxruntime/core/optimizer/quick_gelu_fusion.ccQuickGeluN11onnxruntime15QuickGeluFusionEminvirtual 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.ccUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&]N11onnxruntime12FuseReluClipE Shapestd::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]startendSlicestartsstd::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&]ConcatTraining/onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccFuse_SubgraphCannot replace concat node with initializer:allowzeroFused reshape node: Total fused reshape node count: N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rocm_blas_alt_impl.cc__backwardpassN11onnxruntime15RocmBlasAltImplEApplyRulesOnNode/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.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&]Failed to remove node.N11onnxruntime6ActionEN11onnxruntime11RemoveNodesEN11onnxruntime15MergeIntoTargetEN11onnxruntime14ReplaceWithNewEonnxruntime::NodesToOptimize::ToIndices() const::/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.ccNode index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() constA target node must be set.target_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::GetNodesToOptimizeIndices(gsl::span, onnxruntime::NodeIndex, gsl::span, int, int)::Index out of rangeMoveInputOutputImplMoveInputOutput/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.ccSetOpSinceVersionForProducedNodesNot enough produced nodes in the runtime optimization record.New node op () does not match produced node op in runtime optimization record (Too many produced nodes in the runtime optimization record.void onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const string&, const OpVersionsMap&, std::unique_ptr, std::unique_ptr)Existing registration with name inserted_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&]ApplySelectorsAndActionsMatchAndProcessMatched 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 for transformer Missing action Nodes to optimize are not valid, skipping action.N11onnxruntime25SelectorActionTransformerE_Float_CastCast Input to float/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccSkipLayerNormalizationfused SkipLayerNorm subgraphs N11onnxruntime19SkipLayerNormFusionEN11onnxruntime14EliminateSliceE onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()TensorrtExecutionProviderMIGraphXExecutionProviderForEachWithIndexvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)VitisAIExecutionProviderExecution type '' doesn't support memcpy MemcpyCopy from/to host memorybool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)ProcessInitializersN11onnxruntime17MemcpyTransformerE*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ai.onnxvirtual size_t onnxruntime::ApiTensor::NumElements() const/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimizer/optimizer_api_impl.ccFailed to get size of TensorProtosize >= 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&]Datavirtual std::vector onnxruntime::ApiTensor::Data() constvirtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) constNo NodeArg found for name node_arg_ != nullptrint onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map, int>&)Transpose optimizer is expected to add only onnx domain ops. Domain: provided for op: domain == kOnnxDomain domain not found in opset imports.opset_import_iter != domain_to_version_map.end()Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.since_version.has_value()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>::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]QLinearAveragePoolQLinearGlobalAveragePoolstd::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*&]Optimization after layout transformation failed: virtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector&)Permutation length does not match rank perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation entry out of bounds for shape 0 <= p && p_int < shape_proto->dim_size()Added in transpose optimizer_outvirtual void onnxruntime::ApiGraph::CopyValueInfo(std::string_view, std::string_view)Existing destination type is not compatible with source type.dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector&)Failed to find initializer for name: successTransposeInitializervirtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector&)Failed to find initializer to reshape with name Cannot reshape initializer to have different number of elementsnew_num_elts == old_num_eltsconst_transpose_optimizerN26onnx_layout_transformation3api9TensorRefEN26onnx_layout_transformation3api12ValueInfoRefEN26onnx_layout_transformation3api7NodeRefEN26onnx_layout_transformation3api8GraphRefEN11onnxruntime12ApiValueInfoEN11onnxruntime9ApiTensorEN11onnxruntime7ApiNodeEN11onnxruntime8ApiGraphEIdentityNhwcMaxPoolbad optional access/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimizer/ort_transpose_optimizer.ccTranspose optimizer failed: St19bad_optional_accessN11onnxruntime18TransposeOptimizerEpermtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_layout_transformation::api::TensorRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_layout_transformation::api::TensorRef&]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]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]storage_order!(__hi < __lo)constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = long int]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_algo.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]ExpLogReciprocalAbsCeilFloorNegErfHardSigmoidRoundIsInfIsNaNSeluShrinkSignSoftplusSoftsignThresholdedReluCeluHardSwishSinCosTanSinhCoshAsinAcosAtanAsinhAcoshAtanhMaxMinAndOrXorModPReluBitShiftEqualGreaterLessGreaterOrEqualLessOrEqualMeanCastLikePadReduceLogSumReduceLogSumExpReduceMaxReduceMinReduceProdReduceSumSquareReduceL1ReduceL2ArgMinTileHardmaxLogSoftmaxcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoidcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearConcatcom.microsoft.QLinearAddcom.microsoft.QLinearMulcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearGlobalAveragePoolUnsupported 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]InstanceNormalizationConvTransposeLpPoolMaxUnpoolGlobalLpPoolLRNGridSampleDepthToSpaceSpaceToDepthUnsqueezeElimination_/onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node N11onnxruntime20UnsqueezeEliminationEonnxruntime::optimizer_utils::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::/onnxruntime_src/onnxruntime/core/optimizer/utils.ccUnexpected data type for Clip input of RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomial>yE>ValidateGemmInitializer/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.hStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerValidateUnidirMaskunidir 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 floatbool 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() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:CheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose has not perm attributek_reshape const not matchedPass CheckNodesInPathKCheckDistilBertReshapeShapeFailed to find shape pathCheckNodesInPathVStart 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 expectedPass CheckNodesInPathVreshape initializer value is not expectedMatchGemmSubgraphStart MatchGemmSubgraphGemmFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters 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 expectedPass MatchGemmSubgraphMatchUnidirMaskSubgraphStart 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 slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertExpandFailed to find mask pathOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3where const not matched.equal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_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 indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphMask_Int32Cast mask from int64 to int32FuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs num_headsunidirectionalmask_filter_valueFused an attention node for GPT.qkv_weightsqkv_biasFuseSubGraphQKImpl/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_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.ConvertMaskToInt32Mask 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 vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFuseSubGraphQKFused an attention node.FuseSubGraphQKDistilBertFailed in match input mask subgraphshape of layer norm bias tensor not expectedTotal fused Attention node count: N11onnxruntime15AttentionFusionE    BitmaskBiasDropoutBiasDropout/onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.ccDropoutBitmaskDropoutfused Add-Dropout-(Add) for N11onnxruntime17BiasDropoutFusionE /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccFastGelufused Add and GeluN11onnxruntime14BiasGeluFusionE/onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.ccint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)/onnxruntime_src/onnxruntime/core/providers/common.haxis is not in valid range [-axis >= -tensor_rank && axis <= tensor_rank - 1fused into softmax(input + bias)BiasSoftmaxis_inner_broadcastN11onnxruntime17BiasSoftmaxFusionEN11onnxruntime15CastEliminationE/onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrepresentative.output_index != kInvalidOutputIndexNot eliminating output of node ] because it's the graph's output.N11onnxruntime30CommonSubexpressionEliminationECould not find OrtValue with name 'ConstantFolding/onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccCould not find a CPU kernel and hence can't constant fold node 'virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold N11onnxruntime15ConstantFoldingE*ZNK11onnxruntime15ConstantFolding9ApplyImplERNS_5GraphERbiRKNS_7logging6LoggerEEUlRKSsE_typename 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&]/onnxruntime_src/onnxruntime/core/optimizer/constant_sharing.ccTotal 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.ccExpected Conv then Add.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"ConvActivationFusionConvActConvAddReluvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)activation_paramsN11onnxruntime20ConvActivationFusionE *N11onnxruntime12_GLOBAL__N_17actions15FuseConvAddReluE*N11onnxruntime12_GLOBAL__N_17actions24FuseConvActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors11ConvAddReluE*N11onnxruntime12_GLOBAL__N_19selectors22ConvActivationSelectorEvirtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_act_fusion.ccConvAddActivationFusionConvAddActNhwcFusedConvActvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constN11onnxruntime23ConvAddActivationFusionE*N11onnxruntime12_GLOBAL__N_17actions27FuseConvAddActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors25ConvAddActivationSelectorEvirtual 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.ccconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_N11onnxruntime13ConvAddFusionEvirtual 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.ccbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_N11onnxruntime12ConvBNFusionE 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.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_N11onnxruntime13ConvMulFusionEN11onnxruntime12DivMulFusionEDoubleQDQRemoved_N11onnxruntime21DoubleQDQPairsRemoverE*ZN11onnxruntime21DoubleQDQPairsRemover15IsNodeRemovableERNS_5GraphERKmRmS5_S5_EUlRKSsE_N11onnxruntime16EliminateDropoutE /onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.ccDynamicQuantizeLinearDynamicQuantizeMatMulN11onnxruntime27DynamicQuantizeMatMulFusionE/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64MatchInputToConcatSubgraphFailed 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 shapeNonZeroConstantOfShapeRangeMatchPositionEmbeddingSubgraphsFromGatherOutput 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. Failed to match Shape node. 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 int32mask_indexEmbedLayerNormalizationfused EmbedLayerNorm subgraphs 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.position_embeddingsFailed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). FuseSubGraphDistilBertN11onnxruntime20EmbedLayerNormFusionEN11onnxruntime17ExpandEliminationE/onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs N11onnxruntime14FastGeluFusionEFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)/onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccInvalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of N11onnxruntime32FreeDimensionOverrideTransformerE/onnxruntime_src/onnxruntime/core/optimizer/gather_fusion.ccSplit for Fused Gather nodesSqueeze for Fused Gather nodesnum_outputsSqueezeAxesInitializervirtual onnxruntime::common::Status onnxruntime::GatherToSliceFusion::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrange_input_defs.size() == 3unsqueeze_output_Unsqueeze_Unsqueeze for Fused Gather nodesUnsqueezeAxesInitializerSliceAxesInitializerSlice for Fused Gather nodesN11onnxruntime19GatherToSplitFusionEN11onnxruntime19GatherToSliceFusionE/onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccBiasGeluGelu approximationTotal Gelu Approximation (FastGelu) node count: N11onnxruntime17GeluApproximationE/onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.ccfused Gelu subgraphs N11onnxruntime10GeluFusionE/onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.ccEluScaledTanhParametricSoftplusFusedGemmfused Gemm with activation N11onnxruntime20GemmActivationFusionE  virtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/gemm_sum_fusion.ccother_sum_input != nullptrvirtual 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() == 1_sum_transformedFused Gemm with Sumgemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0!sum_input_movedgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())N11onnxruntime13GemmSumFusionE  _sum_transformedstd::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&]Fused Gemm with TransposeN11onnxruntime19GemmTransposeFusionE onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&)/onnxruntime_src/onnxruntime/core/optimizer/initializer.ccInitializer must have a datatypeutils::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()Initializervoid onnxruntime::Initializer::scale_by_axis(const onnxruntime::Initializer&, int)Axis must be non-negativeaxis >= 0void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1Invalid other(scalers) sizescalers.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]onnxruntime::Initializer& onnxruntime::Initializer::add(const onnxruntime::Initializer&)Expecting the same data typedata_type() == other.data_type()Expecting the same sizesize() == other.size()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::mul(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::sub(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::div(const onnxruntime::Initializer&)St23_Sp_counted_ptr_inplaceIN11onnxruntime12CPUAllocatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEtypename 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=onnxruntime::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.ccFailed to get allocator for optimizerallocator_ptr_Infoonnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)std::__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]TryCreateKernelN11onnxruntime23OptimizerExecutionFrameE*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSK_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&]init_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aopsetgenerated at runtimeQGemmN11onnxruntime19ReplaceWithNewFixedEN11onnxruntime3QDQ17QDQReplaceWithNewEN11onnxruntime3QDQ18ReplaceWithQLinearEN11onnxruntime3QDQ23UnaryReplaceWithQLinearEN11onnxruntime3QDQ24BinaryReplaceWithQLinearEN11onnxruntime3QDQ26VariadicReplaceWithQLinearEN11onnxruntime3QDQ22ConvReplaceWithQLinearEN11onnxruntime3QDQ23WhereReplaceWithQLinearEN11onnxruntime3QDQ21SplitReplaceWithQuantEN11onnxruntime3QDQ24MatMulReplaceWithQLinearEN11onnxruntime3QDQ20GemmReplaceWithQuantEonnxruntime::GetAvailableExecutionProviderNames()::/onnxruntime_src/onnxruntime/core/providers/get_execution_providers.ccMake the EP: name shorterprovider.name.size() <= kMaxExecutionProviderNameLenOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderQNNExecutionProviderNnapiExecutionProviderJsExecutionProviderCoreMLExecutionProviderRknpuExecutionProviderXnnpackExecutionProviderCANNExecutionProviderAzureExecutionProviderRegisterOnnxOperatorKernels/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxMLOperatorKernelsRegisterFp16KernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constN11onnxruntime20CPUExecutionProviderE*ZN11onnxruntime20CPUExecutionProvider17RegisterAllocatorERNS_16AllocatorManagerEEUliE_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_policyE2EEN11onnxruntime15ProviderHostCPUEN11onnxruntime19ProviderHostCPUImplEX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)Unknown AutoPadType Stringonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.hA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSETstd::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]virtual onnxruntime::common::Status onnxruntime::FusedConvFp16::UseSharedPrePackedBuffers(std::vector >&, int, bool&)/onnxruntime_src/onnxruntime/core/providers/cpu/fp16/fp16_conv.ccprepacked_buffers[0].get() == nullptronnxruntime::FusedConvFp16::FusedConvFp16(const onnxruntime::OpKernelInfo&)GetFusedActivationAttr(info, activation_).IsOK()vector::_M_fill_insertkernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape. channels_last: 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&]Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.ComputePadAndOutputShapeInferPadsAndOutputShapeInvalid input shape: ComputeZ shape does not match output shape. Z: Output: FN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEEN11onnxruntime13FusedConvFp16EPFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEE*ZNK11onnxruntime13FusedConvFp167ComputeEPNS_15OpKernelContextEEUllE_onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const string&, int)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.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()(NHWC):[(NCHW):[/onnxruntime_src/onnxruntime/core/providers/cpu/fp16/fp16_pool.ccInput dimension cannot be less than 3.virtual onnxruntime::common::Status onnxruntime::PoolFp16::Compute(onnxruntime::OpKernelContext*) constInvalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || N == 0Invalid kernel shape. Input shape ] Kernel shape:[`InlinedVector::at(size_type)` failed bounds checkvoid onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constUnsupported AutoPad Type.N11onnxruntime8PoolFp16E *ZNK11onnxruntime8PoolFp167ComputeEPNS_15OpKernelContextEEUllE_T1T2void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList]/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.hutils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)SetValueFromTensorProtoUnsupported value attribute datatype: PrepareComputeMust have a valid input shape./onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList]Must have a single dimensiont_proto_p->dims_size() == 1Must have a single dimension of 1t_proto_p->dims()[0] == 1N11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJlNS_9MLFloat16EfdasihtjmbEEEEE*N11onnxruntime12_GLOBAL__N_115ConstantOfShapeEonnxruntime::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()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)sample_sizeonnxruntime::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: _M_a <= _M_bstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/random.hstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]input count mismatchCould not infer data type from input tensor with data type _M_stddev > _RealType(0)std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1MultinomialCompute/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccInvalid data type of N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialEdelta in Range operator can not be zero!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:N11onnxruntime5RangeEonnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hmin_ <= max_void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.ccmin should be a scalar.min->Shape().IsScalar()max should be a scalar.max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, 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 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()exclusivereverseAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = float]dims.size()=starts.size()=dims.size() == starts.size()extents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.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]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEEDetonnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccX != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedN11onnxruntime3DetIfEE@basic_string::basic_stringEinsumtypename 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&]DeviceCompute/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 is not supported yetonnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.hMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()Einsum op: There must be atleast one inputstd::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*&]There was a problem acquiring temporary memory allocator in Einsum opFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_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_11concurrency10ThreadPoolEPvEonnxruntime::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.ccEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span&)The rank of the input must match permutation size for Transposeinput_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr)The innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]Einsum op: Unsupported data type for Diagonal std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, onnxruntime::AllocatorPtr, void*, const Transpose&)Length of permutation must match the rank of the input to be permutatedtypename 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: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*)Cannot parse the diagonal elements along dims for input shape rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]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]Data types of the inputs must match for MatMulinput_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 = float; 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]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]...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisonnxruntime::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 == rankFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Output subscript contains repeated lettersOutput subscript contains letters not seen in the inputsNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all 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 . The shape is: Another operand has a dim value of in the same dimensionEinsum 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&]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]Rank of the input must match number of subscript labels corresponding to the inputstd::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&]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_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: 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]The override dims are not compatible with given tensor's shape. Left shape: Left shape override: left.Shape().Size() == left_shape_override.Size()Right shape: Right shape override: right.Shape().Size() == right_shape_override.Size()Ranks of pair-wise operands must be equal. Right shape: left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignstd::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]std::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]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]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 = 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 = int]void onnxruntime::InputBroadcaster::AdvanceBy(size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.hInputBroadcaster can only start at span boundary!offset % span_size_ == 0fmodonnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Expand_8::Compute >::Invalid usage. Input 1 is a shape with no data.void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper]BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()BitwiseNotBitwiseXorBitwiseAndBitwiseOrvoid onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestvoid onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)onnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)Can broadcast 0 by 0 or 1. is invalid.largest <= 1onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t)Invalid start/ending offset [) for tensor of length:start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenBroadcast Output range [) are not at boundary of span with size:start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::BitShift::Compute::cur1 == end1cur_out == end_outonnxruntime::BitShift::Compute::onnxruntime::BitShift::Compute::onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]LEFTRIGHTInvalid direction value of ''. 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]void onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if::value, void>::type = void]fmod attribute must be true for floating point typesvoid 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: void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&)Tensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&)ExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]Must have 1 or more inputsinput_count >= 1All inputs must have the same shapedata_1.Shape() == shapedata_n.Shape() == 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_6IfEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIdEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime5EqualIdEEN11onnxruntime11LessOrEqualIfEEN11onnxruntime11LessOrEqualIdEEN11onnxruntime11LessOrEqualIiEEN11onnxruntime11LessOrEqualIlEEN11onnxruntime14GreaterOrEqualIfEEN11onnxruntime14GreaterOrEqualIdEEN11onnxruntime14GreaterOrEqualIiEEN11onnxruntime14GreaterOrEqualIlEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime10BitwiseAndIaEEN11onnxruntime10BitwiseAndIsEEN11onnxruntime10BitwiseAndIiEEN11onnxruntime10BitwiseAndIlEEN11onnxruntime10BitwiseAndIhEEN11onnxruntime10BitwiseAndItEEN11onnxruntime10BitwiseAndIjEEN11onnxruntime10BitwiseAndImEEN11onnxruntime10BitwiseNotIaEEN11onnxruntime10BitwiseNotIsEEN11onnxruntime10BitwiseNotIiEEN11onnxruntime10BitwiseNotIlEEN11onnxruntime10BitwiseNotIhEEN11onnxruntime10BitwiseNotItEEN11onnxruntime10BitwiseNotIjEEN11onnxruntime10BitwiseNotImEEN11onnxruntime9BitwiseOrIaEEN11onnxruntime9BitwiseOrIsEEN11onnxruntime9BitwiseOrIiEEN11onnxruntime9BitwiseOrIlEEN11onnxruntime9BitwiseOrIhEEN11onnxruntime9BitwiseOrItEEN11onnxruntime9BitwiseOrIjEEN11onnxruntime9BitwiseOrImEEN11onnxruntime10BitwiseXorIaEEN11onnxruntime10BitwiseXorIsEEN11onnxruntime10BitwiseXorIiEEN11onnxruntime10BitwiseXorIlEEN11onnxruntime10BitwiseXorIhEEN11onnxruntime10BitwiseXorItEEN11onnxruntime10BitwiseXorIjEEN11onnxruntime10BitwiseXorImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8ISsEEN11onnxruntime3ModE((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO5?5?5?5?????!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^1?1?1?1?"?"?"?"?@K@K@K@K@@@@ɿɿɿɿZZZZ4§4§4§4§GGGG s7 s7 s7 s7nnnn*=*=*=*=pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<****]G]G]G]G8       -DT!<*ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = float; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hc_shape is required if c_data is providedc_shape != nullptronnxruntime::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() == 1right.NumDimensions() == 2 K: N:GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = double; int64_t = long int]N11onnxruntime8GemmBaseEZNK11onnxruntime4GemmIdE17ComputeActivationEPdmPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime4GemmIfEEN11onnxruntime4GemmIdEEZNK11onnxruntime4GemmIfE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_ /onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=N11onnxruntime7HardmaxIfEE/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchonnxruntime::common::Status onnxruntime::MatMulComputeHelper::Compute(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&, bool, bool, bool, bool, bool)Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dims/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]N11onnxruntime6MatMulIfEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIlEE?N11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundIdEEN11onnxruntime4SignEComputeImplOpset13/onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.ccN11onnxruntime7SoftmaxIfEEN11onnxruntime7SoftmaxIdEESoftmaxCPU 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]ITopKvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, unsigned int&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo&, int&)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeN11onnxruntime4TopKILi9EfEEN11onnxruntime4TopKILi9EdEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi10EdEEN11onnxruntime4TopKILi11EfEEN11onnxruntime4TopKILi11EdEEN11onnxruntime4TopKILi11EiEEN11onnxruntime4TopKILi11ElEE*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ArrayFeatureExtractorai.onnx.mlInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=N11onnxruntime2ml23ArrayFeatureExtractorOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpISsEEBinarizerthresholdInput data with index: is NaNN11onnxruntime2ml11BinarizerOpIfEE%fCastMapcast_toonnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK()TO_STRINGTO_INT64onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64info.GetAttr("map_form", &attr).IsOK()DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&)Invalid PACK_MAP value of Expected DENSE or SPARSEmax_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATconst T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]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.ccNegative index values are not permitted. First entry in map has index value of cur_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]Invalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of N11onnxruntime2ml7CastMapECategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_stringsonnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.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]N11onnxruntime2ml14CategoryMapperEDictVectorizerstring_vocabularyonnxruntime::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]int64_vocabularyonnxruntime::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>]N11onnxruntime2ml16DictVectorizerOpISslEEN11onnxruntime2ml16DictVectorizerOpISsfEEN11onnxruntime2ml16DictVectorizerOpISsdEEN11onnxruntime2ml16DictVectorizerOpIlSsEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIldEEFeatureVectorizerinputdimensionsonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccNumber of inputs () does not match number of inputdimensions values (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()input_tensor_ptr != nullptrInvalid input type:N11onnxruntime2ml17FeatureVectorizerEImputerimputed_value_floatsimputed_value_int64sreplaced_value_floatonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected '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.Empty input dimensions.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]virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constInvalid typeN11onnxruntime2ml9ImputerOpELabelEncoderkeys_int64svalues_strings_Unusedkeys_floatskeys_stringsvalues_int64svalues_floatsdefault_floatInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.hinfo.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()) must have the same length. (name: attribtues in LabelEncoder The values is and the number of However, the number of key is num_keys == num_valuesonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string]classes_stringsonnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string]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]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = 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 = 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 multi_classpost_transformSOFTMAXSOFTMAX_ZEROinterceptsclasslabels_stringsclasslabels_intscoefficientsonnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc!coefficients_.empty()LOGISTICNONEvoid onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span, int64_t, int64_t, int64_t, const std::vector&, const std::vector&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) constScores output is incorrect size. Expected:scores_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]shape_size == out.size()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 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_LinearRegressortargetsonnxruntime::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 N11onnxruntime2ml15LinearRegressorENormalizeronnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()MAXL2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&)Invalid normalize value of L1Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of N11onnxruntime2ml10NormalizerEOneHotEncoderUnknown Category and zeros = 0.zerosonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.ccOne and only one of the 'cats_*' attributes must be definedtmp_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]N11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpISsEEScaleroffsetonnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccEmpty scale in attributes!scale_.empty()Scale size: () != (scale_.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_N11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIiEESVMClassifierkernel_typeLINEARRBFkernel_paramsonnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.hinfo.GetAttrs("kernel_params", kernel_params).IsOK()POLYvectors_per_classprob_aprob_bsupport_vectorsrhoonnxruntime::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() > 0coefficients_.size() > 0N11onnxruntime2ml9SVMCommonEN11onnxruntime2ml13SVMClassifierE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfLm18446744073709551615EEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_SVMRegressorn_supportsonnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.ccone_classonnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext*) const [with T = float]num_features == feature_count_Unexpected mode:ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPfE_N11onnxruntime2ml12SVMRegressorIfEEstd::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 = 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]X dims is empty.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 > >]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hpredictions.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::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::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::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 > >]base_valuesnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuesclass_idsclass_treeidsclass_nodeidsclass_weightsclasslabels_int64sbase_values_as_tensornodes_hitrates_as_tensornodes_values_as_tensorclass_weights_as_tensorvoid 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]this->base_values_.size() == predictions.size()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]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.hTreeEnsemble only works on 1D, 2D tensors.One path in the graph requests feature but input tensor has features.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::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]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::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::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::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::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]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::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::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::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::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::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::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]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::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::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::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::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::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]predictions.size() == 2void 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::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::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::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]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::TreeAggregatorSum; 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]Unknown aggregation function in TreeEnsemble.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]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::TreeAggregatorSum; 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]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::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::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::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]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]void 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::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::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::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::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::TreeAggregatorSum; 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 = double; ThresholdType = double; OutputType = float; std::string = std::basic_string; int64_t = long int]n_targets_or_classes > 0nodes_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()AVERAGESUMMINtarget_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty()nodes_modes.size() < std::numeric_limits::max()BRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQNode in tree is already there.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_)Unable to find node (truenode).A node cannot point to itself: (falsenode). (weights).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*&]Target is missing for leaf truenode_ids[i] != ifalsenode_ids[i] != ibase_values_as_tensoronnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]target_weights_as_tensoraggregate_functionn_targetstarget_idstarget_nodeidstarget_treeidstarget_weightsonnxruntime::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]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 = 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 = 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 = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; 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_2ml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is specified but is empty.onnxruntime::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.ccAttribute '' must be a vector.n_dims == 1Unexpected type ((for attribute 'proto.data_type() == proto_type' has one dimension but is empty.n_elements > 0onnxruntime::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_tensorN11onnxruntime2ml21TreeEnsembleRegressorIfEEN11onnxruntime2ml21TreeEnsembleRegressorIdEEZipMaponnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T* 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]N11onnxruntime2ml8ZipMapOpEonnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.hinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccUnsupported pooling size.Index tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:N11onnxruntime9MaxUnpoolEInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: training_modeonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.hTraining mode only supports spatial BNis_spatial_onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = float]Invalid number of outputs for BN training!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = double]N11onnxruntime9BatchNormIfEEN11onnxruntime9BatchNormIdEE/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.ccoutput and sum shape must matchN11onnxruntime4ConvIfEEvoid 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.h*out_size >= 0void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const TensorShapeVector&, const TensorShapeVector&, const TensorShapeVector&, const TensorShapeVector&, int64_t, onnxruntime::ConvAttributes::ConvPadVector*, onnxruntime::TensorShapeVector*) constdim_size > 0output_paddingoutput_shapegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group.PrepareForComputeDoConvTranspose/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccN11onnxruntime13ConvTransposeIfEEfloat onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.hratio input should have a single value.ratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0ffloat onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float]X and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; 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 = float; T2 = double]N11onnxruntime7DropoutIffEEN11onnxruntime7DropoutIfdEEN11onnxruntime7DropoutIdfEEN11onnxruntime7DropoutIddEE?Flattenonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hinfo.GetAttr("axis", &axis_).IsOK()virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) constThe rank of input tensor must be >= axisgsl::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 vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count 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 X.shape()[axis:] == . Size of scale and bias (if provided) must match this. Got scale size of and bias size of ComputeImplN11onnxruntime13LayerNormImplE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIddEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIdfEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIffEENS_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()size_ > 0size_ % 2 == 1alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 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 void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()auto_padonnxruntime::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*) constinput_dims.size() >= 2onnxruntime::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_6LpPoolEEEN11onnxruntime9MaxPoolV8EN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime4PoolIfNS_6LpPoolEEEN11onnxruntime9LpPoolV18IfEEN11onnxruntime13MaxPool1DTaskIfEEN11onnxruntime13MaxPool2DTaskIfEEN11onnxruntime13MaxPool3DTaskIfEEN11onnxruntime13MaxPool1DTaskIdEEN11onnxruntime13MaxPool2DTaskIdEEN11onnxruntime13MaxPool3DTaskIdEEN11onnxruntime13MaxPool1DTaskIaEEN11onnxruntime13MaxPool2DTaskIaEEN11onnxruntime13MaxPool3DTaskIaEEN11onnxruntime13MaxPool1DTaskIhEEN11onnxruntime13MaxPool2DTaskIhEEN11onnxruntime13MaxPool3DTaskIhEE MaxRoiPoolpooled_shapeonnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.hinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_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()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()N11onnxruntime6ShrinkEStringNormalizeronnxruntime::string_normalizer::Locale::Locale(const string&)::/onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.ccFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesWCHAR_TInput contains invalid utf8 charsSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedis_case_sensitiveonnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)attribute is_case_sensitive is not setcase_change_actionattribute case_change_action is not setLOWERUPPERattribute case_change_action has invalid valuelocalestopwordsEmpty stopwords not allowed!sw.empty()Duplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_errorConversion Erroren_US.UTF-8Conversion ErrorN11onnxruntime16StringNormalizerETfIdfVectorizertypename 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]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.ccDuplicate ngram detected, size: id: p.first->second->id_ == 0size_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]onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&)mode is requiredIDFTFIDFmode: is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNonemin_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0max_gram_lengthmin_gram_length >= max_gram_length required: >= impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0ngram_countsNon-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()ngram_indexesNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })weightsGot weights of size: but ngram_indexes size: must be of equal sizeimpl_->weights_.size() == impl_->ngram_indexes_.size()pool_stringspool_strings must not be empty if specified!pool_strings.empty()pool_int64snon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for -gramsend_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)TFN11onnxruntime15TfIdfVectorizerEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_std::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_boxonnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.hcenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccGetThresholdsFromInputsiou_threshold must be in range [0, 1].static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SelectedIndex; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::SelectedIndex&]void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Sequence = std::vector; _Compare = std::less]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_queue.hvirtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constoutput != nullptrN11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionERoiAlignstd::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]avgonnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hInvalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0RoiAlignBaseThe existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.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 matchN11onnxruntime12RoiAlignBaseEN11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEE *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_OBOptionalHasElementOptionalOptionalGetElementonnxruntime::Optional::Optional(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.hOptional op must have a TypeProto in the 'type' attribute if the attribute is presentattr->has_tp()PropagateInputOrtValueToFirstOutput/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableTrying to use OptionalGetElement on an optional type OrtValue which contains no dataThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))N11onnxruntime8OptionalEN11onnxruntime18OptionalHasElementEN11onnxruntime18OptionalGetElementET3ConvIntegervirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/conv_integer.ccMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(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_ZN11onnxruntime17ParQuantizeLinearIhEEvPKfPT_mfS3_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.virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/matmul_integer.ccMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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.cconnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = signed char]typename 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&]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]QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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_valuestatic void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = signed char; int64_t = long int; uint8_t = unsigned char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = signed char; int64_t = long int]QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = unsigned char; int64_t = long int]ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE0_N11onnxruntime11QLinearConvIhEEN11onnxruntime11QLinearConvIaEEvoid 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.ccx_zero_point must be null or a scalar or 1D tensor or size 1.zero_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)onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = int]DequantizeLinear with type int32 should have no zero point or all zero points should be 0zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })ZN11onnxruntime17ParQuantizeLinearIaEEvPKfPT_mfS3_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime16DequantizeLinearIaEEN11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIiEEN11onnxruntime14QuantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEE@`@p@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&]QLinearMatMulvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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_PROCESSORN11onnxruntime13QLinearMatMulEvoid onnxruntime::ValidateMustBeOverloaded()/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccmust be overloaded.void onnxruntime::ValidateNoTransposeReduce(int64_t)Reduction on all axes, output size should be 1.count == 1void onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor*)Axes input is nullaxes_tensor != nullptrAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1void onnxruntime::ValidateFastReduceKR(const gsl::span&, const onnxruntime::Tensor&)Only works on matrices with two dimensions.fast_shape.size() == 2Output size mismatch.fast_shape[0] == output.Shape().Size()void onnxruntime::ValidateFastReduceRK(const gsl::span&, const onnxruntime::Tensor&)fast_shape[1] == output.Shape().Size()void onnxruntime::ValidateFastReduceKRK(const gsl::span&, const onnxruntime::Tensor&)Only works on matrices with three dimensions.fast_shape.size() == 3fast_shape[0] * fast_shape[2] == output.Shape().Size()void onnxruntime::ValidateFastReduceRKR(const gsl::span&, const onnxruntime::Tensor&)void onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape&, int64_t)Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = true]/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()select_last_indexonnxruntime::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_N3gsl4spanIKlLm1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uceKRKERKNS_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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GRUonnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclipclip_ > 0.fforwardbidirectionalonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.sigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2layoutBatchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.layout_ == 0gsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [with T = float]Can not get unpacked span from prepacked weights!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 yetvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constInvalid data type for GRU operator of N11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPiE_LSTMonnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.hinput_forgetactivation_func_names.size() == static_cast(num_directions_) * 3LSTM operator does not support double yetvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccInvalid data type for LSTM operator of PrePackN11onnxruntime8LSTMBaseEN11onnxruntime13DeepCpuLstmOpEInput X must have 3 dimensions only. Actual:Input B must have shape {*}. 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 P must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.ccRNNAffine/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cconnxruntime::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()FffffEN11onnxruntime3RNNIfEEPFffffEstd::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.ccExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)weights.quant_para_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)reluaffineleakyreluthresholdedreluscaledtanhhardsigmoidsoftplusvoid (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)Invalid activation function of softsignvoid (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)Invalid LSTM merge activation function of void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)Invalid GRU hidden gate activation function: 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;%%~3a*~3a*77A\3A\3JCy7JCy7 ': ':Y;Y;Ӡ5Ӡ5֕8֕8;;Y;Y; #<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]ZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPhE_*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_ConcatFromSequenceonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.hMust have valid 'axis' attributevirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccGot nullptr for sequence input.N11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceESplitToSequenceSequenceEmptySequenceLengthSequenceConstructSequenceEraseSequenceInsertSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccUnsupported 'dtype' value: CloneTensoronnxruntime::Tensor onnxruntime::CloneTensor(const onnxruntime::Tensor&, onnxruntime::OpKernelContext*, const onnxruntime::DataTransferManager&)split_size_sum () != split_dim_size (virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.int64_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 (int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector&)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int]Split should be > 0split_scalar > 0Invalid 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 = float]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 = std::basic_string]SplitToSequence operator does not support yetN11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEinverseDFTSTFTstatic void SafeIntExceptionHandler::SafeIntOnDivZero()Divide by zeroT onnxruntime::bit_reverse(T, unsigned int) [with T = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/dft.ccUnsupported bit size.dft_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.htensor->Shape().Size() == 1Unsupported input data type of dft_length must be a scalar value.dft_length must be greater than zero.onnxruntime::common::Status onnxruntime::discrete_fourier_transform(onnxruntime::OpKernelContext*, int64_t, bool, bool)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::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = float; U = float]signal shape must end in 1 (real) or 2 (real, imaginary).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.frame_length == window_lengthEnsure that the dft size is smaller than the signal.window_size < signal_sizeshort_time_fourier_transformonnxruntime::common::Status onnxruntime::short_time_fourier_transform(onnxruntime::OpKernelContext*, bool, bool) [with T = float; U = std::complex]virtual onnxruntime::common::Status onnxruntime::STFT::Compute(onnxruntime::OpKernelContext*) constUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.onnxruntime::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]N11onnxruntime3DFTEN11onnxruntime4STFTES!@S!S! @@ `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_?output_datatypeHannWindowBlackmanWindowHammingWindowMelWeightMatrixT onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = float]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.cclower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_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 = unsigned 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 = 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@onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccAttribute to is not set.NaN-INFINF%.8gstoullstod*N11onnxruntime12_GLOBAL__N_14CastECol2Imonnxruntime::Col2Im::Col2Im(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.hstrides_.empty()dilations_.empty()pads_.empty()onnxruntime::common::Status onnxruntime::Col2Im::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.ccsize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.dilations_.size() == image_dim_numbersize of '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_src/onnxruntime/core/framework/copy.hlast >= firstonnxruntime::StridedCopy::onnxruntime::StridedCopy::onnxruntime::StridedCopy::onnxruntime::StridedCopy::counter.current_offset == last/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cconnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) constinput != nullptrCannot concatenate scalarsRanks of input data are different, cannot concatenate them. expected rank: input_rank == reference_rankNon concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatchvoid 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]src and dst must have same shape and not be rank 0.dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()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]onnxruntime::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]src and dst types must matchdst.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]invalid expand shapeZNK11onnxruntime6ExpandINS_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_9MLFloat16EEEEyeLikeEyeLike : Input tensor dimension is not 2N11onnxruntime7EyeLikeETindonnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.hMissing/Invalid 'axis' attribute valueindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccGather Tind type not supported in this build.N11onnxruntime10GatherBaseEN11onnxruntime6GatherEZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_GatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hint64_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]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: Unsupported tensor type, size: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]GatherElements op: Data type of input 'data' should match the data type of the outputN11onnxruntime14GatherElementsE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clISsKSsEEDaSA_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_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_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_clIhKhEEDaSA_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_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_batch_dimsGatherNDindices tensor must has rank larger than 0invalid index found, index = virtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorindices tensor data type not supportedN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDEZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_padding_modebicubiconnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.h" not supported, expect bilinear, nearest or bicubicmode_str == "bilinear" || mode_str == "nearest" || mode_str == "bicubic"borderreflectionpadding_mode "" not supported, expect zeros, border or reflectionpadding_mode_str == "zeros" || padding_mode_str == "border" || padding_mode_str == "reflection"bilinearconstexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = float]Only 4-D tensor is supportedonnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.ccGrid batch size does not match input batch size grid_dims[0] == NLast dimension of grid: , expect 2grid_dims[3] == 2ZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime10GridSampleIfEE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]Unable to get an allocatoronnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEdetect_positiveonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_positivedetect_negativeFailed to obtain detect_negativeN11onnxruntime5IsInfENull input ptrN11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNIdEEN11onnxruntime5IsNaNINS_9MLFloat16EEEMeanVarianceNormalizationonnxruntime::MeanVarianceNormalization_1::MeanVarianceNormalization_1(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.hMeanVarianceNormalization CPU EP only supports NHW and NCHW reduction for axes attribute.Input is expected to have four dimensions corresponding to [N,C,H,W]N11onnxruntime27MeanVarianceNormalization_0IfEEN11onnxruntime27MeanVarianceNormalization_1IfEEonnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = long int]/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 = unsigned char]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsOneHot/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.std::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]N11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlfiEECannot 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 reflectonnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.hInvalid 'mode' attribute valueInvalid 'pads' attribute valueedgeconstantonnxruntime::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.ccInput tensor has no dimensionsdata_rank > 0'pads' has wrong number of valuesdata_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 >]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) constPads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Axes tensor should be a 1D tensor axes_tensor_dims.size() == 1Pads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 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 >]N11onnxruntime7PadBaseEN11onnxruntime3PadE   onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hAttribute shape is not set.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 requested_shape[i] >= -1At most one dimension can be -1.unknown_dim == -1The 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:, requested shape:size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizevirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1N11onnxruntime7ReshapeEN11onnxruntime9Reshape_1E Resize operatorUpsample operator/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsamplebase.hScalesValidationScale 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 dataonnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&)info.GetAttr("mode", &mode).IsOK()cubiconnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)mode attribute is . It can only be (default) or or antialiaswhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)info.GetAttrs("scales", scales_).IsOK()UpsampleBasekeep_aspect_ratio_policystretchnot_largernot_smalleronnxruntime::AspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const string&)keep_aspect_ratio of [] is not supported!extrapolation_valuepytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetriconnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&)coordinate_transform_mode:[] is not supportted!`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attributeround_prefer_floorround_prefer_ceilceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&)nearest_mode:[cubic_coeff_aexclude_outsideexclude_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 and anti-aliasing is set to N11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIaEEN11onnxruntime6ResizeIaEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEReverseSequencebatch_axisonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()Invalid batch_axis of . Must be 0 or 1batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisInvalid sequence length: . Value must be in range [0,gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::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]sequence_lens shape must be {batch_size}. Got:. batch_size=gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccUnknown tensor type of N11onnxruntime17ReverseSequenceOpEaddCPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.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 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.ScatterElementsScatteronnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccreductionnoneCPU 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'.mulCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.data type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=Indices type is not supported.N11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbSsEEEEE CPU 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: 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. , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: 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'.ScatterND/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_N11onnxruntime5ShapeESizeN11onnxruntime4SizeEonnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hMissing or invalid starts and ends attributehas_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()void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*)Unexpected element size of onnxruntime::SliceImpl >:: >&)>/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccoutput == output_endonnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>onnxruntime::SliceImpl::&)>FillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts 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 an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0Cannot slice scalarsN11onnxruntime9SliceBaseEN11onnxruntime6Slice1EN11onnxruntime7Slice10Eblocksizeonnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&)DepthToSpace op: only 'DCR' and 'CRD' modes are supportedSpaceDepth ops require a 4-D input. Provided rank: DepthToSpace requires input depth to be a multiple of (block_size * blok_size)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in DepthToSpace op: SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeUnsupported input type in SpaceToDepth op: N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthEN11onnxruntime12DepthToSpaceEonnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&, uint32_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0std::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 . Size of dimension being split is Input cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was virtual onnxruntime::common::Status onnxruntime::SplitImpl::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccThe split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1N11onnxruntime9SplitBaseEN11onnxruntime9SplitImplEN11onnxruntime10Split_1_13EN11onnxruntime8Split_18Estatic onnxruntime::TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const TensorShapeVector&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hDimension of input must be 1 instead of . shape=input_shape[i] == 1virtual 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' tensorvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.ccTile doesn't have an implementation yet for the type: N11onnxruntime4TileE[ perm: does not align with rank of input data: void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, gsl::span, const gsl::span&, size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.ccMethod IncrementIndexAndComputeOffset assumes this value is strictly positive.naxes > 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.void onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const string*, std::string*)Transpose not implemented for empty tensors.num_axes > 0(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. N11onnxruntime13TransposeBaseEN11onnxruntime9TransposeETriluupperonnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.hinfo.GetAttr("upper", &temp).IsOK()virtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.cck should be a 1-D or 0-D tensor.IsScalarOr1ElementVector(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 N11onnxruntime6UniqueEonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueinfo.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_tensorN11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEParseSizesDataResize: input tensor's rank does not match the output tensor's rank.axes should be less than output_dims.size()ParseScalesDataAndAdjustOutputSizeInput 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: 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]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.Resize: unexpected modeUpsample: unexpected modeBaseCompute/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.UpsampleNearest4-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.Invalid 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_555555N11onnxruntime5WhereIhEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereIdEEN11onnxruntime5WhereISsEERegisterQuantizationKernels/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 input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEECropAndResizeonnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKSsPKiPS1_EUllE_EEvSG_lOS8_lEUllE_N11onnxruntime7contrib13CropAndResizeIfEEDynamicSliceScaleonnxruntime::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()N11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEExpandDimsvirtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.hAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()Axis must be within range [ Axis is axis <= X_NumDims && axis >= -X_NumDimsN11onnxruntime7contrib10ExpandDimsEunimplemented activation: activation_params count mismatchonnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccN11onnxruntime7contrib14FusedConvFloatE/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()Bias size () does not match the number of channels (N11onnxruntime7contrib11ImageScalerIfEEInverseN11onnxruntime7contrib7InverseE<<*ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib9LayerNormILb0EEEN11onnxruntime7contrib9LayerNormILb1EEE/onnxruntime_src/onnxruntime/contrib_ops/cpu/math/sparse_dense_matmul.ccSparseToDenseMatMulCOO indices must be 2-D, got: COO k index: is out of bounds of lhs_right: COO m index: is out of bounds of out_left: Currently supporting only 2-D matricesCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: Expecting COO 2-D indices shapeExpecting 2xValues == indicesExpecting the same number NNZ == size of Inner indicesOuter size must be M + 1Currently support only COO and CSR(x64) formatsN11onnxruntime7contrib19SparseToDenseMatMulEMaxpoolWithMask/onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hN11onnxruntime7contrib15MaxpoolWithMaskEN11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEEacross_channelsonnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cckeysInvalid assumption of output element sizesizeof(uint32_t) == output_element_bytesinput_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()onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&)info.GetAttr("channels", &channels_).IsOK()invalid channel countchannels_ > 0onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode '' for NCHWc Upsampletransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'virtual 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*) constX_shape.size() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constX_shape.NumDimensions() == 4virtual 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)Unsupported convolution size.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 > 0do_rotaryqkv_hidden_sizesstd::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_N11onnxruntime7contrib10QAttentionIfEEDynamicQuantizeLSTMInput W_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight 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.cconnxruntime::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*) constMatmulInteger : b zero point is not validIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) constMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)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 != nullptrN11onnxruntime7contrib15MatMulInteger16IssiEE/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.cconnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]N11onnxruntime7contrib11NhwcMaxPoolIaEEN11onnxruntime7contrib11NhwcMaxPoolIhEEQEmbedLayerNormalizationWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1input index out of range/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qembed_layer_norm.ccN11onnxruntime7contrib18EmbedLayerNormBaseEN11onnxruntime7contrib15QEmbedLayerNormIfEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfaEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfhEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_QLinearSigmoidQLinearLeakyReluZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_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_???QLinearMulQLinearAddvoid onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper]void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_binary_op.ccMatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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]N11onnxruntime7contrib10QLinearAddIaEEN11onnxruntime7contrib10QLinearAddIhEEN11onnxruntime7contrib10QLinearMulIaEEN11onnxruntime7contrib10QLinearMulIhEE*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_QLinearConcatonnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.ccEach input must be (tensor, scale, zero_point) tuple!input_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @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_virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_global_average_pool.ccInput x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(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_void 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_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : 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 = signed 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 = unsigned char]N11onnxruntime7contrib18QLinearAveragePoolEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEEZN11onnxruntime7contrib16dequantize_arrayIaEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib16dequantize_arrayIhEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_QLinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_softmax.ccopset must be existed in attributes of QlinearSoftmaxvoid onnxruntime::contrib::{anonymous}::BuildLookupTableIfFixed(const onnxruntime::OpKernelInfo&, std::vector&, size_t, bool)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)N11onnxruntime7contrib14QLinearSoftmaxE*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIaEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIhEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_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]onnxruntime::contrib::QLinearWhere::QLinearWhere(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_where.ccThere must be inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)input_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()virtual onnxruntime::common::Status onnxruntime::contrib::QLinearWhere::Compute(onnxruntime::OpKernelContext*) constInput scale is not float for quantized input x @ 2Input 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_TATBTCTYZTYstatic void onnxruntime::contrib::QGemm::CheckInputs(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::GemmHelper&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccQGemm : 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 1IsScalarOr1ElementVector(a_zp)QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or Nb_zp_shape.NumDimensions() == 0 || (b_zp_shape.NumDimensions() == 1 && (b_zp_shape[0] == 1 || b_zp_shape[0] == helper.N()))QGemm : scale of input b must be a scalar or 1D tensor of size 1 or Nb_scale_shape.NumDimensions() == 0 || (b_scale_shape.NumDimensions() == 1 && (b_scale_shape[0] == 1 || b_scale_shape[0] == helper.N()))QGemm : zero point and scale of input b should have same shape sizeb_scale_shape.NumDimensions() == b_zp_shape.NumDimensions() && (b_scale_shape.NumDimensions() == 0 || (b_scale_shape[0] == b_zp_shape[0]))QGemm : zero point of y must be null or a scalar or 1D tensor of size 1y_zp == nullptr || IsScalarOr1ElementVector(y_zp)QGemm : scale of y must be null or a scalar or 1D tensor of size 1y_scale == nullptr || IsScalarOr1ElementVector(y_scale)void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = int; int64_t = long int]N11onnxruntime7contrib5QGemmESampleOpN11onnxruntime7contrib8SampleOpIfEEonnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double]input is expected to have 3 or 2 dimensions, got skip is expected to have same shape as inputgamma 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_N11onnxruntime7contrib13SkipLayerNormIfEEN11onnxruntime7contrib13SkipLayerNormIdEEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::StringPiece; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::StringPiece&]Tokenizeronnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute mark is not setpad_valueattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valuemincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: 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&]Can not digest tokenexp: Input string contains invalid utf8 charsconst value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int]Input string contains invalid utf8 chars: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedN11onnxruntime7contrib9TokenizerEBeamSearchdecodervirtual 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.ccSetupSubgraphExecutionInfo should only be called once for each subgraph.gpt_subgraph_ == nullptrinit_decoderinit_run_gpt_subgraph_ == nullptrencodert5_encoder_subgraph_ == nullptrconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]SetupSubgraphExecutionInfoEncoder 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_ == nullptrGenerateNextToken/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_base.hInput 'input_features' is expected to have 3 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 onnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState&, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::T5EncoderSubgraph&, onnxruntime::contrib::transformers::T5DecoderSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, onnxruntime::contrib::transformers::BeamSearchParameters&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const InitCacheIndirFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, const CreateEncoderInputsFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateDecoderFeedsFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, const void*, int) [with T = onnxruntime::MLFloat16; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::InitCacheIndirFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::IBeamSearchCpuState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc = std::function*, gsl::span&, int, int, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::CreateEncoderInputsFunc = std::function, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateDecoderFeedsFunc = std::function, 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*)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_t5.hDecoder masked multihead attention can only be used on GPU cards of compute capability 5.3 or higher. This card has compute capability cuda_device_arch_ >= 530onnxruntime::contrib::transformers::BeamSearchT5::BeamSearchT5(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState&, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::T5EncoderSubgraph&, onnxruntime::contrib::transformers::T5DecoderSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, onnxruntime::contrib::transformers::BeamSearchParameters&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const InitCacheIndirFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, const CreateEncoderInputsFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateDecoderFeedsFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, const void*, int) [with T = float; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::InitCacheIndirFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::IBeamSearchCpuState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc = std::function*, gsl::span&, int, int, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::CreateEncoderInputsFunc = std::function, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateDecoderFeedsFunc = std::function, 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*)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>; onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc = std::function, OrtValue&, bool, int)>]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.honnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]parameters->sequence_length == 1onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::Compute(onnxruntime::OpKernelContext*) constSubgraph SessionState was not found for 'decoder' attribute.decoder_session_stateCreateFeedsFetchesManager 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_onnxruntime::contrib::transformers::BeamSearchGpt::BeamSearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, onnxruntime::contrib::transformers::BeamSearchParameters&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = float; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::IBeamSearchCpuState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc = std::function*, gsl::span&, int, int, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]Decoder masked self attention can only be used on GPU cards of compute capability 5.3 or higher. This card has compute capability onnxruntime::contrib::transformers::BeamSearchGpt::BeamSearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, onnxruntime::contrib::transformers::BeamSearchParameters&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = onnxruntime::MLFloat16; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::ProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::IBeamSearchCpuState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitBeamStateFunc = std::function*, gsl::span&, int, int, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]Subgraph SessionState was not found for 'encoder' attribute.encoder_session_stateencoder_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&]Model type is not supported.FN11onnxruntime6common6StatusEPKNS_18IExecutionProviderEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS8_SaIS8_EERSt10unique_ptrIcSt8functionIFvPcEEEEFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_19IBeamSearchCpuStateEPNS6_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_biEN11onnxruntime11controlflow18IControlFlowKernelEN11onnxruntime7contrib12transformers10BeamSearchEN11onnxruntime7contrib12transformers12GenerateBaseEN11onnxruntime7contrib12transformers14BeamSearchBaseIfEEN11onnxruntime7contrib12transformers13BeamSearchGptIfEEN11onnxruntime7contrib12transformers14BeamSearchBaseINS_9MLFloat16EEEN11onnxruntime7contrib12transformers13BeamSearchGptINS_9MLFloat16EEEN11onnxruntime7contrib12transformers12BeamSearchT5IfEEN11onnxruntime7contrib12transformers12BeamSearchT5INS_9MLFloat16EEEPFN11onnxruntime6common6StatusEPKNS_18IExecutionProviderEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS8_SaIS8_EERSt10unique_ptrIcSt8functionIFvPcEEEEPFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_19IBeamSearchCpuStateEPNS6_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_biEmodel_typeearly_stoppingeos_token_idpad_token_iddecoder_start_token_idno_repeat_ngram_sizevocab_size/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_parameters.cceos_token_id is invalidpad_token_id is invalidmin_length shall be smaller than max_lengthvoid onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)context != nullptrinput_features shall have 3 dimensions. Got dims.size() == 3input_ids shall have 2 dimensions. Got dims.size() == 2max_length () shall be greater than input sequence length (max_length > sequence_length) shall be no more than 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 num_return_sequences >= 1num_return_sequences () shall be be no more than num_beams (num_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got repetition_penalty > 0.0fvirtual void onnxruntime::contrib::transformers::BeamSearchScorer::Initialize(onnxruntime::AllocatorPtr&, int)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_scorer.ccnext_beam_scores_.empty()void onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span&, gsl::span&)top_k <= Size()void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::contrib::transformers::HypothesisScore; _Sequence = std::vector >; _Compare = onnxruntime::contrib::transformers::HypothesisScoreCompare]std::priority_queue<_Tp, _Sequence, _Compare>::const_reference std::priority_queue<_Tp, _Sequence, _Compare>::top() const [with _Tp = onnxruntime::contrib::transformers::HypothesisScore; _Sequence = std::vector >; _Compare = onnxruntime::contrib::transformers::HypothesisScoreCompare; std::priority_queue<_Tp, _Sequence, _Compare>::const_reference = const onnxruntime::contrib::transformers::HypothesisScore&]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()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::contrib::transformers::BeamHypotheses; _Alloc = onnxruntime::OrtStlAllocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::contrib::transformers::BeamHypotheses&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]Batch can only be done if all beams have been generatedbeam_hyp.Size() >= gsl::narrow_cast(num_beams_)beam_idx == num_beams_hypothesis_buffer_offset_ <= hypothesis_buffer_length_virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(onnxruntime::contrib::transformers::ISequences*, gsl::span&, onnxruntime::Tensor*, onnxruntime::Tensor*)sequences != nullptroutput_sequences != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::contrib::transformers::BeamHypotheses; _Alloc = onnxruntime::OrtStlAllocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::contrib::transformers::BeamHypotheses&]N11onnxruntime7contrib12transformers11IBeamScorerEN11onnxruntime7contrib12transformers16BeamSearchScorerEZN11onnxruntime10IAllocator13MakeUniquePtrIbEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPbE_N11onnxruntime7contrib12transformers14IConsoleDumperEN11onnxruntime7contrib12transformers22CpuTensorConsoleDumperEBeamSearch op: An implementation for the input type 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]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/generation_device_helper.ccelement_type == DataTypeImpl::GetType()is_kv_cacheonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IBeamSearchState*, onnxruntime::contrib::transformers::IBeamSearchCpuState*, 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() == 3ProcessLogitsonnxruntime::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::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]input_features_shape.NumDimensions() == 3onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&) [with T = float; 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 = float; onnxruntime::AllocatorPtr = std::shared_ptr]last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)input_ids_shape.NumDimensions() == 2void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]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::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]Sample/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_cpu_helper.hGreedySearchProcessLogitsSt15binary_functionIffbESt7greaterIfESt4lessIfEvirtual 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.ccNot ImplementedGreedySearch/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*) constonnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, ParametersT&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = float; ParametersT = onnxruntime::contrib::transformers::GreedySearchParameters; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc = std::function*, gsl::span&, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]onnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, ParametersT&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = onnxruntime::MLFloat16; ParametersT = onnxruntime::contrib::transformers::GreedySearchParameters; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc = std::function*, gsl::span&, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]FN11onnxruntime6common6StatusERK8OrtValuePNS_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@N11onnxruntime7contrib12transformers20ILogitsProcessorListEN11onnxruntime7contrib12transformers19LogitsProcessorListEN11onnxruntime7contrib12transformers16ILogitsProcessorIfEEN11onnxruntime7contrib12transformers32RepetitionPenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers28NoRepeatNGramLogitsProcessorIfEEN11onnxruntime7contrib12transformers24VocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PrefixVocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers24MinLengthLogitsProcessorIfEEN11onnxruntime7contrib12transformers26TemperatureLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PresencePenaltyLogitsProcessorIfEEvirtual 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*) constonnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, ParametersT&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = float; ParametersT = onnxruntime::contrib::transformers::SamplingParameters; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc = std::function*, gsl::span&, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]onnxruntime::contrib::transformers::GreedySearchGpt::GreedySearchGpt(onnxruntime::OpKernelContextInternal&, const onnxruntime::SessionState*, onnxruntime::contrib::transformers::GptSubgraph*, const onnxruntime::SessionState&, onnxruntime::contrib::transformers::GptSubgraph&, onnxruntime::concurrency::ThreadPool*, onnxruntime::Stream*, onnxruntime::contrib::transformers::IConsoleDumper*, ParametersT&, const CreateGptInputsFunc&, const AddToFeedsFunc&, const ReorderPastStateFunc&, const TopkFunc&, onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc&, onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc&, const void*, int) [with T = onnxruntime::MLFloat16; ParametersT = onnxruntime::contrib::transformers::SamplingParameters; onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc = std::function&, std::shared_ptr, OrtValue&, OrtValue&, OrtValue&)>; onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc = std::function, std::vector&, std::unique_ptr >&)>; onnxruntime::contrib::GenerationDeviceHelper::ReorderPastStateFunc = std::function; onnxruntime::contrib::GenerationDeviceHelper::TopkFunc = std::function, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, onnxruntime::Tensor&, onnxruntime::Tensor&)>; onnxruntime::contrib::GenerationDeviceHelper::GreedySearchProcessLogitsFunc = std::function*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, std::shared_ptr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*)>; onnxruntime::contrib::GenerationDeviceHelper::InitGreedyStateFunc = std::function*, gsl::span&, onnxruntime::Stream*)>; onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc = std::function, gsl::span, onnxruntime::Stream*, int)>; onnxruntime::contrib::GenerationDeviceHelper::UpdateGptFeedsFunc = std::function, onnxruntime::Stream*, const std::vector&, std::vector&, int, OrtValue&, bool, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool)>]N11onnxruntime7contrib12transformers8SamplingEN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_18SamplingParametersEEEtemperaturetop_pfilter_valuemin_tokens_to_keeppresence_penaltycustomvoid onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_parameters.ccSeed must be >= 0seed >= 0N11onnxruntime7contrib12transformers10ISequencesEN11onnxruntime7contrib12transformers9SequencesESetup/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]beam_widthpast_sequence_lengthGetParameterssubgraph 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]DecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentionN11onnxruntime7contrib12transformers8SubgraphEonnxruntime::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.ccSetup must be called before CreateInitialFeedssession_state_ != nullptrCreateInitialFeedsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).ValidateInvalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)position_idssubgraph input 1 shall be named as position_ids, got: subgraph input 2 shall be named as attention_mask, got: past_0subgraph input 3 shall be named as past_0, got: logitspresent_0subgraph 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 outputN11onnxruntime7contrib12transformers11GptSubgraphEencoder_hidden_states/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.ccdecoder_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: encoder_attention_maskdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: 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_ == falseN11onnxruntime7contrib12transformers17T5DecoderSubgraphEonnxruntime::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:number of outputs expected to be 2 + 4 * layers, got:encoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder_input_idsencoder 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: present_key_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: present_value_self_0encoder 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 typeN11onnxruntime7contrib12transformers17T5EncoderSubgraphE/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_encoder.ccencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeN11onnxruntime7contrib12transformers22WhisperEncoderSubgraphEInput tensor to Unique op should be 1DN11onnxruntime7contrib6UniqueIfEEembedding_sizeconv_window_sizechar_embedding_sizeWordConvEmbeddingConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccN11onnxruntime7contrib17WordConvEmbeddingEhӥhӥhӥhUnsupported typeNo attribute with name:''is defined.Attribute name and type don't match for '/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.hgamma/onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.hCreate/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.ccunknown kernel typeonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::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::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu]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::Sigmoid]N11onnxruntime8functors7SigmoidIfEEN11onnxruntime8functors4TanhIfEEN11onnxruntime8functors26ElementWiseRangedTransformINS_9MLFloat16EEEN11onnxruntime8functors4ReluINS_9MLFloat16EEEN11onnxruntime8functors9LeakyReluINS_9MLFloat16EEEN11onnxruntime8functors3EluIfEEN11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEEN11onnxruntime8functors11HardSigmoidIfEEN11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEEN11onnxruntime8functors9LeakyReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEEN11onnxruntime8functors4ReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEEN11onnxruntime8functors4ReluIdEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEEN11onnxruntime8functors4ReluIaEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEEN11onnxruntime8functors4ReluIiEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluINS_9MLFloat16EEEEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluINS_9MLFloat16EEEEEN11onnxruntime8functors4SeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEEN11onnxruntime8functors7SigmoidIdEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEEN11onnxruntime8functors8SoftplusIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEEN11onnxruntime8functors8SoftsignIfEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEEN11onnxruntime8functors4TanhIdEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEEN11onnxruntime8functors4CeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEEN11onnxruntime8functors15ThresholdedReluIfEEN11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEEN11onnxruntime8functors18ParametricSoftplusIfEEN11onnxruntime8functors10ScaledTanhIfEEBBBB¥¥¥;?;?;?;?r1r1r1r1giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>BBBBO@O@O@O@OOOO9999IfFailed to create output tensor for If output onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc'If' node has outputs which doesn't match the subgraph's outputs.num_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 Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedstd::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_branchvirtual 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&)info == nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtDevice*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const OrtDevice*&]void onnxruntime::If::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()N11onnxruntime2IfE*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceR8OrtValueRbE_Loopstd::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]void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector&, std::vector&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccAll scan outputs MUST be tensorslast_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*&]onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)Graph in 'body' attribute of Loop should have inputs. Found:static_cast(num_subgraph_inputs) == subgraph_inputs.size()'Loop' node has outputs so the subgraph requires but has num_subgraph_outputs - 1 == num_outputsvirtual 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}.virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) constSubgraph SessionState was not found for 'body' attribute.typename 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&]FN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEN11onnxruntime4LoopEPFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEScan<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]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()directionsSubgraph in 'body' produces outputs but Scan expects 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&]AllocateOutputTensorsonnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)Output OrtValue has not been created for loop state variable output p_mlvaluestd::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&]CreateLoopStateVariablesInvalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was has batch size of Scan inputs have inconsistent sequence lengths. Previous value was has length of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was ValidateInputonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]feeds_fetches_manager_ && info_FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEN11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEN11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorE*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_onnxruntime::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&]const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hAttempt to retrieve final output before it was set.final_output_mlvalue_Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()Outputs from Scan are not optional and should never be null.TransposeOutputvoid onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]scan_input_directionsscan_output_directionsscan_input_axesNumber of entries in 'scan_input_axes' was but expected gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&)output_mlvalue but input '' dimension Invalid value in scan_input_axes for input . Input tensor rank was SetupInputsonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]N11onnxruntime4ScanILi9EEE *ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_void onnxruntime::scan::detail::LoopStateVariable::Next()/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.ccMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_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&]onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)The subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsvoid onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, onnxruntime::TensorShapeVector&, size_t)Number of entries in '' was directions.size() == num_entriesInvalid values in ''. 0 == forward. 1 == reverse.Mismatch between expected shape and shape from first output is not compatible with CreateFeedsFetchesManagerstd::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 #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)If shape was concrete we shouldn't be using a custom allocator!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_onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::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_8functors10ScaledTanhIfEEEEN11onnxruntime7contrib4GeluIfEEN11onnxruntime7contrib9QuickGeluIfEE֕8AttnLSTMonnxruntime::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: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib17DeepCpuAttnLstmOpEN11onnxruntime7contrib16AttentionWrapperIfEE row[ rows[ [seqno=/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib9AttentionIfEEInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got onnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.ccExpect to have present state output when past state input is givenAttention cannot have both past and relative_position_biasInput '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 Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes: q_hidden_size= k_hidden_size= v_hidden_size=bias_dims[0]=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 set/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]use_approximationN11onnxruntime7contrib8BiasGeluIfLb0EEEN11onnxruntime7contrib8BiasGeluIfLb1EEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_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 Input 1 dimension 0 should have same length as the last dimension of input 0BifurcationDetectormin_ngram_sizeonnxruntime::contrib::BifurcationDetector::BifurcationDetector(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bifurcation_detector.hinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_virtual 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 and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of MultiHeadAttentiononnxruntime::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 AddBiasReshapeAddBiasTransposePacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUonnxruntime::common::Status onnxruntime::contrib::MultiHeadAttention::Compute(onnxruntime::OpKernelContext*) const [with T = float](key != nullptr && value != nullptr) || (past_key != nullptr && past_value != nullptr)ZN11onnxruntime7contrib14AddBiasReshapeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_ZN11onnxruntime7contrib16AddBiasTransposeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_N11onnxruntime7contrib18MultiHeadAttentionIfEETidNGramRepeatBlockngram_sizeonnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/ngram_repeat_block.hinfo.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_size_ > 0onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) const::token_id < vocab_sizevirtual onnxruntime::common::Status onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeN11onnxruntime7contrib16NGramRepeatBlockEZNK11onnxruntime7contrib16NGramRepeatBlock7ComputeEPNS_15OpKernelContextEEUlllE0_CDistmetriconnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanonnxruntime::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:N11onnxruntime7contrib5CDistIfEEN11onnxruntime7contrib5CDistIdEEConvTransposeWithDynamicPadsN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEonnxruntime::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.ccnot support normalize yet.!normalize_void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]Real memory steps is not in (0, mem_steps <= max_memory_steps_ && mem_steps > 0N11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEECudaCudaPinnedOpenVINO_GPUDMLSpecified device is not supported./onnxruntime_src/onnxruntime/core/framework/allocator.ccN11onnxruntime12CPUAllocatorE/onnxruntime_src/onnxruntime/core/framework/allocatormgr.ccCreateAllocatorReceived invalid value of arena_extend_strategy void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr)Duplicate allocator for OrtMemType: device: Existing allocator: New allocator: 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_policyE2EE/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cccudaMallochipMalloc/onnxruntime_src/onnxruntime/core/framework/bfc_arena.hRegionForCould not find Region for onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)h < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) constp_int >= base_intp_int < base_int + memory_size_std::_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)Could not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0void 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_numstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = void*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = void*&] BFC Arena shrunk by bytes. The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*)Could not find Region for: entry != regions_.end()BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: memory limit: arena_extend_strategy: Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b) Chunk Free DumpMemoryLogAllocator: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_use: Chunks . Bytes Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: Available memory of is smaller than requested bytes of onnxruntime::BFCArena::Extend(size_t)::Incorrect arena extend strategy.ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t)0 == memory_size % kMinAllocationSizeFailed to allocate memory for requested buffer of size AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)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()N11onnxruntime8BFCArenaEN11onnxruntime16StreamAwareArenaEConfig key is empty or longer than maximum length 128Config value is longer than maximum length: /onnxruntime_src/onnxruntime/core/framework/config_options.ccAddConfigEntryConfig with key [] already exists with value []. It will be overwrittenCopyTensors/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccCopySparseTensorsCopyTensorCopyTensorAsyncvirtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&) constsrc.SizeInBytes() == dst.SizeInBytes()N11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEdata_transfer registered is nullptr.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]Tensor size mismatchThere's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::utils::data_types_internal::TypeNode&]int8bfloat16doublefloat16(null)uint8uint16int16booluint32uint64GetDeleteFuncGetElementTypevirtual 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())virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) conststatic void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto*, onnx::TypeProto&)/onnxruntime_src/include/onnxruntime/core/framework/data_types.hexpected a registered ONNX typevalue_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&)onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)Invalid DataTypeImpl TypeProto definitionbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto&, const onnx::TypeProto&)virtual bool onnxruntime::OptionalTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->optional_type())bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constvoid onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)Only ONNX MLDataType can be registeredproto != nullptrWe do not expect duplicate registration of types for: tensor type is not supportedsparse tensor type MLDataType for: is not currently registered or supportedN11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime16OptionalTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeISsEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12OptionalTypeINS_6TensorEiEEN11onnxruntime12OptionalTypeINS_6TensorEfEEN11onnxruntime12OptionalTypeINS_6TensorEbEEN11onnxruntime12OptionalTypeINS_6TensorESsEEN11onnxruntime12OptionalTypeINS_6TensorEaEEN11onnxruntime12OptionalTypeINS_6TensorEhEEN11onnxruntime12OptionalTypeINS_6TensorEtEEN11onnxruntime12OptionalTypeINS_6TensorEsEEN11onnxruntime12OptionalTypeINS_6TensorElEEN11onnxruntime12OptionalTypeINS_6TensorEdEEN11onnxruntime12OptionalTypeINS_6TensorEjEEN11onnxruntime12OptionalTypeINS_6TensorEmEEN11onnxruntime12OptionalTypeINS_6TensorENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqEiEEN11onnxruntime12OptionalTypeINS_9TensorSeqEfEEN11onnxruntime12OptionalTypeINS_9TensorSeqEbEEN11onnxruntime12OptionalTypeINS_9TensorSeqESsEEN11onnxruntime12OptionalTypeINS_9TensorSeqEaEEN11onnxruntime12OptionalTypeINS_9TensorSeqEhEEN11onnxruntime12OptionalTypeINS_9TensorSeqEtEEN11onnxruntime12OptionalTypeINS_9TensorSeqEsEEN11onnxruntime12OptionalTypeINS_9TensorSeqElEEN11onnxruntime12OptionalTypeINS_9TensorSeqEdEEN11onnxruntime12OptionalTypeINS_9TensorSeqEjEEN11onnxruntime12OptionalTypeINS_9TensorSeqEmEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_8BFloat16EEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeIbEEN11onnxruntime17PrimitiveDataTypeISsEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_CleanUp/onnxruntime_src/onnxruntime/core/framework/device_stream_collection.ccvoid onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, onnxruntime::Stream*)idx < num_streams_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) conststream_idx < num_streams_std::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]void onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)N11onnxruntime6StreamEN11onnxruntime11synchronize12NotificationEN11onnxruntime17DummyNotificationEN11onnxruntime11DummyStreamE/onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: Caught exception while destructing CustomOpsLoader with message: A dso with name has already been loaded.LoadExternalLibCaught exception while loading custom ops with message: N11onnxruntime11ExLibLoaderEMemory pattern planner is not enabled on this execution framework.constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::OrtValuePatternPlanner; _Dp = std::_Optional_base]/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccTraceAllocateTraceAllocation for ort_value_idx= size=VerifyOutputSizesExpected shape from model of does not match actual shape of for output 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_onnxruntime::StreamAwareArena* onnxruntime::AsStreamBasedAllocator(onnxruntime::AllocatorPtr)allocator is nullptrallocator.get() != nullptrvoid 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)!is_strided_tensorShape 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.AllocateMLValueTensorPreAllocateBuffervoid 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]AllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actual size is: , fall back to default allocation behavioronnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtDevice&, const onnxruntime::TensorShape&)Failed to get allocator for 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&]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: AllocateReusedOrtValueIfNotAllocatedHelperonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&)OrtValue shape verification failed. Current shape: Requested shape:shape && tensor.Shape() == *shapeshape && sp_tensor.DenseShape() == *shapeonnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, gsl::span)node_index_info and ort_value_idx_map are out of sync and cannot be usednode_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()ExecutionFrameAllocation of memory pattern buffer for returned nullptr failed. Error:N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameE*ZN11onnxruntime14ExecutionFrameC4EN3gsl4spanIKiLm18446744073709551615EEENS2_IK8OrtValueLm18446744073709551615EEES4_S7_RKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS5_RbEESt4hashImESt8equal_toImESaISt4pairIKmSL_EEEPKNS_22DeviceStreamCollectionERKNS_12SessionStateEEUlRKSsE_IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, onnxruntime::HashValue&) const/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccIExecutionProvider constructor must be called with true for use_metadef_id_creatormetadef_id_generator_void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)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&]N11onnxruntime18IExecutionProviderE/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.ccCandidate for fallback CPU execution: std::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer&, const onnxruntime::IExecutionProvider::IKernelLookup&, gsl::span)kernel_info != nullptrGetCpuPreferredNodesvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = long unsigned int; _Alloc = std::allocator]ORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this node*ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKNS_18IExecutionProvider13IKernelLookupEN3gsl4spanIKmLm18446744073709551615EEEEUlRKNS_7NodeArgEmE0_void 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&)API VERSION /onnxruntime_src/onnxruntime/core/framework/func_kernel.hCreate state function failed. Return value: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*&]GetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.std::visit: variant is valuelessonnxruntime::KernelLookup::KernelLookup(onnxruntime::ProviderType, gsl::span >, const onnxruntime::IKernelTypeStrResolver&)/onnxruntime_src/onnxruntime/core/framework/kernel_lookup.hprovider_type must be specified.!provider_type_.empty()typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IndexedSubGraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IndexedSubGraph&]Node '' OpType: with domain: was inserted using the NHWC format as requested by , but was not selected 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.PartitionOnnxFormatModelImplThe Function Style fusion is deprecated.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Node*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ComputeCapability; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ComputeCapability&]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.No provider specified.std::__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]PartitionOnnxFormatModelPartitionN11onnxruntime18IExecutionProvider13IKernelLookupEN11onnxruntime14FunctionKernelEN11onnxruntime12KernelLookupE*ZN11onnxruntimeL27PartitionOrtFormatModelImplERKNS_12_GLOBAL__N_115PartitionParamsERNS_21KernelRegistryManagerERNS_18IExecutionProviderEEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISE_EEE_*ZN11onnxruntimeL28PartitionOnnxFormatModelImplERNS_5GraphERNS_11FuncManagerERNS_21KernelRegistryManagerERNS_14KernelRegistryERNS_18IExecutionProviderENS_16GraphPartitioner4ModeERiRKSt8functionIFNS_6common6StatusES1_RbS9_RKSD_IFvRKS0_EEEESM_EUlS3_RKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISV_EEE0_std::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: MatchKernelDefTypes/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()type_proto != nullptrThis op has been implemented only for the following types (), but the node in the model has the following type (Kernel found kernel in the supported version range (node_version: However the types are incompatible. Op with name ( domain ( and type ( kernel is not supported in Encountered following errors: (Kernel not found 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.The node is not placed on any Execution Provider. (node Failed to find kernel for found duplicated provider in KernelRegistryManager/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver.ccResolveKernelTypeStrFailed to find op_id: 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.op_kernel_type_str_args is null.op_id is null.kernel_type_str_args is null.kernel_type_str is null.Duplicate entry for kernel type str: Duplicate entry for op id: RegisterOpSchemaRegisterNodeOpSchemaOp schema must be available.RegisterGraphNodeOpSchemasN11onnxruntime22IKernelTypeStrResolverEN11onnxruntime21KernelTypeStrResolverEN11onnxruntime29OpSchemaKernelTypeStrResolverEFlatBuffers 1.12.0SaveKernelTypeStrResolverToBuffer/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver_utils.ccktsrLoadKernelTypeStrResolverFromBufferFailed to verify KernelTypeStrResolver flatbuffers data.AddLayoutTransformationRequiredOpsToKernelTypeStrResolverktsrH,(X8@PH0 :Squeeze:1T||x$com.microsoft:NhwcMaxPool:1$ :Unsqueeze:11&  :Transpose:1Drlh8 :Squeeze:13@ :Identity:14&  :Squeeze:11@nhdD :Unsqueeze:13axes :Unsqueeze:12,( :Identity:13P~xt(\($com.microsoft:QLinearConv:1y_scale T3 T1HD T2<Dt T4hw_scalex_scale4 :Gather:1,ZTP :Transpose:13xX :Identity:1 :Identity:16  VB<84 :Gather:11`L|TT :Gather:13 T  Tind const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccnullptr != type_protoonnxruntime::NodeIndexInfo::Init::/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init > >::onnxruntime::NodeIndexInfo::Init::ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_static std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_map_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypestatic std::unique_ptr OrtOptionalTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_optional_type_info.cctype_proto is not of optional typevalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypestatic std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_sequence_type_info.cctype_proto is not of type sequence!value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeThe type is not tensor, sparse tensor, sequence, map or optional typestatic std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_typeinfo.ccOrtValue is TensorSequence type but has no element Tensor DataType.tensor_data_type != nullptrTensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typestatic std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto&)This TypeProto does not have ValueCase setTempSpace allocator not foundCPU allocator not foundonnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptrint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_virtual OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&)virtual OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)virtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constInvalid arg_num of . Num args is arg_num < arg_counts.size()N11onnxruntime15OpKernelContextENo attribute with this name is defined.No attribute with name: is defined.Attribute: expected to be of type: but is of type: Attribute name and type don't matchRequested attribute: is expected to have type: 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.h!using_counters_std::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&]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]static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()Insufficient dimensions to slice on . Shape:gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInvalid dim0_offset of . Dimension 0 is dim0_offset < dim0_sizestatic onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]Received nullptr for nameReceived nullptr for OrtValueReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user.AddInitializer/onnxruntime_src/onnxruntime/core/framework/session_options.ccAn OrtValue for this name has already been added: onnxruntime::common::Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span >, gsl::span)Expecting same size spansinit_num == values.size()AddExternalInitializersSt23_Sp_counted_ptr_inplaceIN11onnxruntime14LibraryHandlesESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE/onnxruntime_src/onnxruntime/core/framework/session_state.ccconstexpr 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]const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_.has_value()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]CreateKernelsPruneRemovableAttributesfailed at retrieving the removable attributesfor node '' ('').removed removable attributes '), among attributes: 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&]SetupAllocatorsAllocator already registered for . Ignoring allocator from Failed to find input name in the mapping: Could not find an implementation for ) node with name 'VerifyEachNodeIsAssignedToAnEpImplVerifyEachNodeIsAssignedToAnEpNode placements All nodes placed on []. Number of nodes: Node(s) placed on [Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.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()void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr)Entry exists in node for attribute existing_entries.find(attribute_name) == existing_entries.cend()CreateSubgraphSessionStateonnxruntime::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 empty!op_type.empty()+Using cached version of pre-packed weight for constant initializer: used in the node: which is of op type: 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 void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.session.node_partition_config_filesession.disable_prepackingFinalizeSessionStateImplconstexpr _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&]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)Missing session state for subgraph. Node:' Index: Attribute:entry != node_to_subgraph_ss.second.cend()OuterScopeNodeArgLocationAccumulatorp_op_kernelFinalizeSessionStateduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextEN11onnxruntime28IStreamCommandHandleRegistryEN11onnxruntime31StreamCommandHandleRegistryImplE*ZN11onnxruntime12SessionState15SetupAllocatorsEvEUl10OrtMemTypeE_*ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRKNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRNS_14InlinedHashMapISsmSaISt4pairIS1_mEEEERKNSC_ISs9OrtDeviceSaISD_IS1_SI_EEEEbEUlS2_iRK8OrtValueRKNS_11OrtCallbackEbbE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapISs9OrtDeviceSaISt4pairIKSsSD_EEEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapISs9OrtDeviceSaISt4pairIKSsSD_EEEEEUlRKNS_7NodeArgEmE_shape.Size() must >=0Failed memory size calculation/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 DeserializeTensorProtoExtDataTensorProtoToTensorstring tensor is not supported for copying between allocatorsFailed to copy tensor to SubgraphGraphSaveInputOutputNamesToNodeMapping input with name is not used by any node.SaveInitializedTensorsSaving initialized tensors.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&)OrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates bytes for Skipping entry for missing optional value at idx Using user supplied initializer with name (session.use_device_allocator_for_initializersDeserialize tensor Done saving initialized tensorsSt8functionIFvPvEEN11onnxruntime19session_state_utils19ExtDataValueDeleterESt19_Sp_counted_deleterIPvSt8functionIFvS0_EESaIvELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE0_*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE_/onnxruntime_src/onnxruntime/core/framework/sparse_tensor.ccstatic const onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue&)the ort_value must contain a constructed sparse tensorstatic onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue&)this tensor already has populated sparse_indicesValues size must be less than total buffer size: SparseTensor Allocation failed for size: onnxruntime::SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constMust contain Coo format. Got: Format() == SparseFormat::kCooExpecting to contain one index, got: format_data_.size() == 1Ustd::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]onnxruntime::SparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constMust contain Csr format. Contains: Format() == SparseFormat::kCsrcExpecting two indices. Got: format_data_.size() == 2Uonnxruntime::SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constMust contain BlockSparse format. Got: Format() == 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: Inner and Outer indices must either be both zero or non-zeroExpecting inner index size: the same as values size: Outer index count must be rows + 1 or zero. Got: rows: This method does not expect allocator to be setSparse format must not be set. Already contains format: onnxruntime::SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)This method should follow a call to constructor that supplies the allocatorallocator_ != nullptrFormat() == SparseFormat::kUndefinedMakeCsrDataValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2Expecting index blocks: to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}Not expecting an allocator setonnxruntime::SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)MakeBlockSparseDataCopyDataMust have the same size. Got src_size: dst_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 MakeBlockSparseStringsCopyThis instance should not be emptyDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeMust have the same shapeUnable to find a data transfer for copying from device type: to device type: MakeCsrStringsExpecting data type to be set as stringMakeBlockSparseStringsstd::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constIndex size: must be equal to or twice the values size: values_count == index_sizeonnxruntime::SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)MakeCooDataUse MakeCooStringsMakeCooStringsZN11onnxruntime10IAllocator13MakeUniquePtrIvEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPvE_Currently do not support dims higher than 2 dimensions: Input must be of COO formatUnable to convert strings tensor to a sparse tensor that is not on CPU/onnxruntime_src/onnxruntime/core/framework/sparse_utils.ccSparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyUnsupported element size: Invalid index: > dense_size: Support 2-D matrices onlyInput must be of CSR formatonnxruntime::common::Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const onnxruntime::DataTransferManager&, const onnxruntime::SparseTensor&, const AllocatorPtr&, const AllocatorPtr&, onnxruntime::Tensor&)Expecting inner indices to be same as nnz. Got: inner_num == src.Values().Shape().Size()Outer indices must be M + 1. Got: outer_num == (rows + 1)SparseCsrToDenseTensorDenseTensorToSparseCoostd::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&]Unable to convert strings tensor to a sparse tensor that not on CPUDenseTensorToSparseCsrsize_t onnxruntime::Tensor::SizeInBytes() const/onnxruntime_src/onnxruntime/core/framework/tensor.cctensor size overflowstatic size_t onnxruntime::Tensor::CalculateTensorStorageSize(onnxruntime::MLDataType, const onnxruntime::TensorShape&, gsl::span)Strided tensor is supported for training only for now.strides.empty()tensor failed memory size calculationvoid 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)Internal error.Mem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedTrace/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hFailed 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 locationFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEint64_t onnxruntime::TensorShape::SizeToDimension(size_t) const/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccInvalid dimension of for SizeFromDimension. Tensor has dimension <= num_dimsint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constonnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constInvalid tensor shape slice argument.dimstart <= dimend && dimend <= values_.size()Tensor type is undefinedconst onnxruntime::Tensor& {anonymous}::GetIndicesTensor(const OrtValue&, OrtSparseIndicesFormat)/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.ccUnsupported indices_format passedthe 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 TensorProtoUnpackTensor: the pre-allocated size does not match the raw data size, expected corrupted protobuf data: tensor shape size() does not match the data size() in protodata overflow/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.ccGetExternalDataInfoTensor does not have external data to read from.External data type cannot be UNDEFINED or STRING.*/_ORT_MEM_ADDR_/*TensorProto: external data size mismatch. Computed size: , 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)GetExtDataFromTensorProtoGetFileLength for GetFileContentExternal initializer: offset: size to read: given file_length: are out of bounds or can not be read in full.TensorProtoToTensor() tensor shape mismatch!TensorProto type can not be writen into Tensor type TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated bufferTensorProtoToMLValueReadExternalDataForTensorUnpackTensorWithExternalDataImplnullptr == p_dataUnpackInitializerDataUnsupported type: 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 tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64onnxruntime::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:SparseTensorProtoToDenseTensorProtoElement_size of: Unsupported sparse tensor data type of ConstantNodeProtoToTensorProtoonnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&, const string&)Unsupported attribute value type of in 'Constant' node 'onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&)Must have a valid data typeHasDataType(dense_proto)DenseTensorToSparseTensorProto data_type: *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_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()onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, onnxruntime::Stream*, std::vector*, std::vector*)Failed to find allocator for device allocator != nullptrUnsupported OrtValue type.BatchOrCopyMLValuestd::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&]Unsupported OrtValue type to copy between device.CopyOneInputAcrossDevicesvoid onnxruntime::utils::UpdateWithParentStream(onnxruntime::DeviceStreamCollection&, onnxruntime::Stream*)Subgraph has nodes running on device: while parent graph node running on device: , this is not supported yet.onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, gsl::span, std::vector&, gsl::span, gsl::span)copy_info.size() == num_feedsfeed_streams.size() == num_feedsCopyInputsAcrossDevicesExecuteGraphImplstd::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]CopyOutputsAcrossDevicesconst OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::SessionState&, std::string_view)exec_plan_ptrFindDeviceForValueconst OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, std::string_view)CalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfoExecuteGraphstd::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]int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccinvalid value index: against size n >= 0 && static_cast(n) < ort_value_info_.size()std::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]void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)id >= 0 && static_cast(id) < 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)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forstatic size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&)nullptr != tensor_type_baseconst onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap&, onnxruntime::NodeIndex)SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&, const KernelCreateInfoMap&)p_providerstd::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: (ort_value_idx) output_name : Execution Plan: Index out-of-range!Start logic stream: 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]\u%04x\u%04x\u%04xinvalid UTF-8 byte at index : 0x!empty()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&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char]incomplete UTF-8 string; last byte: 0x\ufffd�cannot use insert() with onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&)Can not find the node ComputeReuseCountstd::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 = 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()onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)Only tensors are supported for external outputs for now.!IsNonTensor(*node_output)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]cannot use operator[] with a string argument with cannot create object from initializer list{}{ , ":[][ "bytes": [], "subtype": {"bytes":[],"subtype":null}streamsdevicesSaveConfigCaught exception during saving DeviceBasedPartitioner config: std::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)Failed to find node "" in node-stream mapiter != node_stream_map.end()n_}iDxIQ""""""""""""""""""""""""""""""N--------------s888dPartitioner 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&]void onnxruntime::DeviceBasedPartitioner::Initialize()Number of streams does not equal to number of device types!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&)onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeValueLocation()Should not have entry in kernel create info with nullptr for kernel_defp_kernel_defCan not find the execution provider ComputeValueLocationonnxruntime::common::Status onnxruntime::PlannerImpl::BuildExecutionPlan(const onnxruntime::ExecutionProviders&, const onnxruntime::IStreamCommandHandleRegistry&)BuildExecutionPlanexecution_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&] reused by as inputOptimizeReusePlanForMultiStreamvoid std::deque<_Tp, _Alloc>::pop_front() [with _Tp = long unsigned int; _Alloc = std::allocator]CreatePlanComputeReusePlanonnxruntime::PlannerImpl::ComputeValueLocation()::There is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_mapN11onnxruntime17IGraphPartitionerEN8nlohmann6detail23output_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_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_*ZN11onnxruntime11FuncManager8GetFuncsERKSsRPKNS_15NodeComputeInfoEEUlPvE1_*ZN11onnxruntime11FuncManager8GetFuncsERKSsRPKNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_/onnxruntime_src/onnxruntime/core/framework/library_handles.ccUnloadLibrariesonnxruntime::HashValue onnxruntime::PrePackedWeights::GetHash() const/onnxruntime_src/onnxruntime/core/framework/prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const string&)/onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.ccUnsupported device allocator in the context of pre-packed weights caching: *ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKSsEUliE_SequentialExecutor::Execute/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccExecuteThePlanNumber of streams: SessionScopeBegin execution_fence_beforeop_name{"":[]}_kernel_time_fence_afternode_indexactivation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapethread_scheduling_statsonnxruntime::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.hAll implicit inputs should have OrtValue instances by now. does not.entry != nullptronnxruntime::common::Status onnxruntime::ExecuteKernel(onnxruntime::StreamExecutionContext&, onnxruntime::NodeIndex, size_t, const bool&, onnxruntime::SessionScope&)Async Kernel Support is not implemented yet.Non-zero status code returned while running node. Name:'' Status Message: ExecuteKernel launch kernel with idx N11onnxruntime23OpKernelContextInternalE*ZN11onnxruntime14ExecuteThePlanERKNS_12SessionStateEN3gsl4spanIKiLm18446744073709551615EEENS4_IK8OrtValueLm18446744073709551615EEES6_RSt6vectorIS7_SaIS7_EERKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS7_RbEESt4hashImESt8equal_toImESaISt4pairIKmSR_EEERKNS_7logging6LoggerEPKNS_22DeviceStreamCollectionERKbbbEUlvE_N11onnxruntime21SimpleTensorAllocatorEstd::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()RecycleNodeInputsort value releasedExiting due to terminate flag being set to true.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::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_model format error! Need a key for the external data infomodel format error! Need a value for the external data infoparsing checksummodel format error!model format error! Missing 'location'locationError parsing node:Attribute expected to have tensor or sparse tensor type[TypeInferenceError] expected to have tensor or sparse typeOutput 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 dimensionsInvalid bias shapeqkv_hidden_sizes should have 3 elementsInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past input shall be 5 dimensions = Constant()Error unexpected extra input in node:Number of attention headsWhether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Whether to use rotary position embedding. Default value is 0.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 projectionbiasMAttention 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)pastadditional add to QxK' with shape (batch_size, num_heads, sequence_length, total_sequence_length)relative_position_biasWhen 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).token_offsetA tensor with shape (batch_size + 1). It specifies the cumulative sequence length.cumulative_sequence_lengthA 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)PackedAttentionInput 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)queryKey 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_keypast 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). past_valueWhen 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).present_keypast 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).present_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)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) from input projectionKey padding mask with shape (batch_size) or (3 * batch_size + 2) or (batch_size, kv_sequence_length)key_padding_maskrelative 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 input and output to float tensors.Constrain mask to integer typesOne sided attention windows length W, or half of total window lengthwindow3D 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_weightglobal_biasGlobal attention flags with shape (batch_size, sequence_length)global3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain 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)q_weight2D input tensor with shape (hidden_size, 2 * hidden_size)kv_weight2D 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)key_cachevalue_cacheIf static_kv = true, cross-attention; else self-attentionstatic_kvIf use_past = true, use cache; else no cacheuse_pastIf has_layer_state = true, layer_state = {} or [a,b]; else layer_state = Nonehas_layer_statehas_key_padding_mask or nothas_key_padding_mask3D output tensor with shape (sequence_length, batch_size, hidden_size)output tensor with shape (batch_size, num_heads, new sequence_length, head_size)new_key_cachenew_value_cacheConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.DecoderAttentionThe epsilon value to use to avoid division by zero.The mask index tensor type for shape inference (0: None, 1: 1D mask_index)mask_index_type2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)segment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embedding1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)beta2D 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 normalizationembedding_sumConstrain input and output integer tensors typesConstrain input and output float tensors types.bias tensoroutput tensorConstrain input and output types to float or half tensors.Max distancemax_distanceis_bidirectional2D input tensor with shape (num_buckets, num_heads), COL-major(See UT for example)bias_tableThe length of query. Self Attention requires query_length = key_lengthquery_lengthThe length of key.key_length4D output tensor with shape (1, num_heads, sequence_length, sequence_length)Constrain sequence_length to int tensors.RelativePositionBias3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size)skip1D 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.inv_std_varSum of the input and skip inputs (and bias if it exists) with shape (batch_size, sequence_length, hidden_size).input_skip_bias_sumConstrain mean and inv_std_var to float tensors.3D 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.2D input tensor with shape (batch_size, sequence_length)2D input tensor with shape (batch_size, vocab_size)scores2D output tensor with shape (batch_size, vocab_size)scores_outConstrain 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.Encoder input ids.src_tokensDecoder input ids.cur_tokensPrevious suffix match indexprev_suffix_match_idxPredicted token ids from aggressive decodingpred_tokensDecoder input ids after merging predicted tokenstokensnew suffix match indexsuffix_match_idxConstrain to integer types.WGemmFastGeluInput tensor with shape (batch_size, sequence_length, hidden_size)Number of non-padding tokens in each sequence with shape (batch_size).sequence_token_countoutput 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)cumulated_seq_lenMax sequence length without padding. Its shape is (1)max_seq_lenConstrain sequence_token_count and token_offset to integer typesRemovePaddingInput 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)query_layer1-d tensor with shape (num_heads x head_size)query_biastensor with shape (1, num_head, seq_len, seq_len)rel_posgemm 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)eco_aoutput tensor with shape (batch_size, num_heads, seq_len, seq_len)GatedRelativePositionBiasX_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) F?&TpxAD?̼+̼+*ZN11onnxruntime7contrib11GetOpSchemaINS0_23FastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Attention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GemmFastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28RemovePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29RestorePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30PackedAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DecoderAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31NGramRepeatBlock_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33MultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34BifurcationDetector_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35RelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_40GatedRelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41DecoderMaskedSelfAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_46DecoderMaskedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_sparse_tensor(float)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)inputs are expected to have tensor type and output type should not be null.first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionboth data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensorInput 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. but has rank expected to have rank >Dimension mismatch in unification between 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Inputs 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: 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesIncompatible dimensionspooled_sizeAttribute 'pooled_size' must be >= 1.max_output_boxesAttribute 'max_output_boxes' must be >= 1.First 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).Incompatible dimensions for matrix multiplicationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorShapeProto*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnx::TensorShapeProto*&]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.CAlpha 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.padding_idxA 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``scale_grad_by_freqOutput 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.TorchEmbeddingBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.apply softmax to elements for dimensions 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 - 1The bias (or mask) as Tensor.(Optional) Seed to the random generator, if not specified we will auto generate one.seedThe 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 dataresidualThe 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.ratioIf 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.The output mask of dropout.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.If true, check only for Inf, -Inf.If true, check only for NaN.Constrain the output to a boolean tensor.Input tensors to check.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.IsAllFiniteThree 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.Grid4-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 padding tokenThe id of the token that indicates decoding starts.no repeat ngrams sizeearly stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like 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 shapeFThe sequence used as a prompt for the generation. 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)repetition_penaltyMask of vocabulary. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (vacab_size)vocab_maskMask 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)prefix_vocab_maskCustom attention mask. Shape is (batch_size, sequence_length)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)sequences_scoresProcessed 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 to float tensors.Constrain input type to float or int 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.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 samplingIf 1 custom sampling logicModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartPresence penalty mask. Shape is (batch_size, vocab_size)presence_maskSeed for random number generator. Shape is (1)Filtered logits as input to the mutinomial function for debug purpose. Shape is (batch_size, vocab_size)filtered_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 tensorssignal_ndimnormalizedonesidedRfftIrfftinput_0input_1ComplexMulComplexMulConjConstrain input and output types to float tensorsZInput 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 stringsInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).markThe 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.tokenexpan 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).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_axis2-dimensional sparse matrix A. Either COO or CSR formatN-dimensional dense matrix BAn 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 embeddingSequenceSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).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 arrayInput tensor.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] 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).Tensor after padding.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'countsInput can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".2D matrix with shape (M,N)2D matrix with shape (K,N)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.rois1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.batch_indices1-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.crop_sizeRoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.Saved inverse standard deviation used during training to speed up gradient computation.InvStdDevConstrain 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.EfficientNMS_TRTThe boxes input tensor.boxesThe scores input tensor.The anchors input tensor.anchorsThe num_detections output tensor.num_detectionsThe detection_boxes output tensor.detection_boxesThe detection_scores output tensor.detection_scoresThe detection_classes output tensor.detection_classesBackground class ID.background_classEncoding type for the boxes or anchors inputs.box_codingBox IOU threshold value.iou_thresholdMax detections to output.Version number of the TRT plugin.plugin_versionActivation function to apply to the scores input.score_activationScore threshold value.score_thresholdMultilevelCropAndResize_TRTThe first feature map input tensor.feature_map_0The second feature map input tensor.feature_map_1The third feature map input tensor.feature_map_2The fourth feature map input tensor.feature_map_3The cropped patches output tensor.patchesImage size.image_sizePooled size.PyramidROIAlign_TRTDisentangledAttention_TRTcontent-to-content attention tensor, QcKc^T.c2c_attentioncontent-to-position attention tensor, QcKr^T.c2p_attentionposition-to-content attention tensor, KcQr^T.p2c_attentionThe disentangled attention output tensor.disentangled_attentionMaximum relative distance, k.spanScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.factorSnpepayload of the SNPE DLC file.DLC(Optional) SNPE version used to convert the model.snpe_version(Optional) Target device like CPU, DSP, etc.target_device(Optional) Some notes for the modelnotesList of tensors for SNPE DLC inputinputsOne or more outputs, list of tensors for DLC outputConstrain input and output types to uint8, uint16, float tensors.The bit-packed output mask.Constrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.NumReducedAxes = Sub (Rank, Axis1D)NumReducedAxes = Neg (Axis1D)EpsilonXShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)XU = Cast (X2D)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedV, Scale2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Alpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) HalfOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) ;f?*ZN11onnxruntime7contrib11GetOpSchemaINS0_18Pad_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_19Gelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20Trilu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_21Unique_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_22Inverse_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23GatherND_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23Sampling_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24QuickGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Tokenizer_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25BeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25ExpandDims_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GridSample_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26BiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26FusedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26IsAllFinite_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26MurmurHash3_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GreedySearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28CropAndResize_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29TorchEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MatMulInteger16_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MaxpoolWithMask_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30TransposeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33BitmaskBiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34SparseToDenseMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36FusedMatMulActivation_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_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 CgroupsActivation after group normalization: 0 for None, 1 for SwishInput data tensor. Dimensions are (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 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.GroupNorm/onnxruntime_src/onnxruntime/core/graph/contrib_ops/diffusion_defs.ccInput 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 value/onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccFvRN4onnx8OpSchemaEEPFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. 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.ccInput 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.x_scaleZero point tensor for input 'X'. It must be a scalar.x_zero_pointScale of quantized output 'Y'. It must be a scalar.y_scaleZero point tensor for output 'Y'. It must be a scalar.y_zero_pointOutput 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_26QLinearConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28NhwcFusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33QLinearAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39QLinearGlobalAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvEUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvENKUlRN4onnx8OpSchemaEE_clES3_EUlRNS1_16InferenceContextEE_Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (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.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.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.Sliced data tensor.Constrain input and output types to all tensor types.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.drop_statesThe previous GRU hidden state.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesArray of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsThe timestep for this operation.The new GRU hidden state calculated by this op.hiddenGRUUnitIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling 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_15Crop_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GivenTensorFill_Onnx_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26GivenTensorFill_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_ QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, `f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}First operand.Input A's scale. It's a scalar, which means a per-tensor/layer quantization.A_scaleInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.A_zero_pointSecond operand.Input B's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointC_scaleC_zero_pointResult, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors.Required attribute axis is missingaxis must be in [-rank, rank)All inputs to Concat must have same rank Dimension=Can't merge shape info. Both source and target dimension have values but they differ. Source=Shape of quantized tensor must be 1D.]. Its actual value is: , 'axis' must be in [ not specifiedValue of attribute should be of integer type and specify a type. does not specify a valid type. or UNDEFINED. Got: expected to have: 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 enumbfp_typeEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.block_dimN-D full precision input tensor to be quantized.1-D, contiguous BFP dataShape of xStrides of xConstrain the input to float and bfloat.Constrain y to uint8.Constrain shape and strides to uint64.QuantizeBFPThe datatype to dequantize to.1-D, contiguous, raw, BFP data to be de-quantized.shape of the original tensor.strides of the original tensor.de-quantized tensor.Constrain the input to uint8.Constrain y to float and bfloat16.DequantizeBFPAn input tensor.Reduced output tensor.reducedConstrain 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.ReduceSumIntegerConstrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.MulIntegerScale 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'.b_scaleZero 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'.b_zero_point1D 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'.a_scaleZero 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'.a_zero_pointConstrain 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.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointapply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.The input tensorZero 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. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.RThe 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]`.sequence_lensOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_cThe 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.PW'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]`.Y_hThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_cConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Which axis to concat onY's scale.T8Y's zero point.TVList of tensors/scale/zero_point for concatenationConcatenated tensorConstrain 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 yconditionX's scale.X's zero point.Z's scale.z_scaleZ's zero point.z_zero_pointTensor of shape equal to the broadcasted shape of condition, X, and YScale 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.Corresponding 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.input_scalescale 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 quantizationweight_scaleAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.input_zero_pointzero 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 quantizationweight_zero_pointpast 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.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantMaskScale for word embeddingsword_embedding_scaleScale for position embeddingsposition_embedding_scaleScale for segment embeddingssegment_embedding_scaleScale for 1D gamma tensorgamma_scaleScale for 1D beta tensorbeta_scaleZero point for word embeddingsword_embedding_zero_pointZero point for position embeddingsposition_embedding_zero_pointZero Point for segment embeddingssegment_embedding_zero_pointZero Point for 1D gamma tensorgamma_zero_pointZero Point for 1D beta tensorbeta_zero_pointLayerNorm Outputlayernorm_outMask Index Outputmask_index_outConstrain input and output types to float32 tensors.cublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_inputcublasLt order of output matrix.order_outputTODO: input tensor of (ROWS, COLS). if less than 2d, will broadcast to (1, X). If 3d, it is treated as (B, ROWS, COS)scale of the inputscale_inputConstrain to float typesConstrain Scale to float32 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.order_AcublasLt order of matrix Border_BcublasLt order of matrix Y and optional matrix Corder_Y3-dimensional matrix Ascale of the input A.scale_A2-dimensional matrix B. Transposed if order_B is ORDER_COL.scale of the input B. Scalar or 1-D float32.scale_Bscale of the output Y.scale_Y1d 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] scale_CConstrain bias and scales to float32QOrderedMatMulHidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixorder_weightcublasLt order of global biasscale of the input, scalar value (per tensor) currently.scale of the gemm - scalar (per-tensor quantization)scale_Q_gemmscale_K_gemmscale_V_gemm2D input tensor with shape (input_hidden_size, hidden_size), where hidden_size = num_heads * head_sizeQ_weightK_weightV_weightscale of the weight (scalar for per-tensor quantization or 1-D of dims [hidden_size] for per-channel quantization)scale_Q_weightscale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale of the softmax result - scalar (per-tensor quantization)scale_QKT_softmaxscale of the gemm - scalar (per-tensor quantization). Also this is the output scale for the operator.scale_values_gemmAttention 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.QOrderedAttentioncublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.order_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale of the quantized Xscale_XScale 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.QOrderedLayerNormalizationcublasLt 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 float32QOrderedGeluorder_global_weightscale of the weightscale_weight1D 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_biasscale of the output for fused kqv gemmscale_qkv_gemmscale of the global_weightscale_global_weightscale of the global_qkv_gemmscale_global_gemmscale of the outputscale_outputBe compatible with float version.QOrderedLongformerAttentionInput 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 axisQLinearConvTranspose̼+*ZN11onnxruntime7contrib11GetOpSchemaINS0_20QGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MulInteger_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25QAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QuantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27QLinearWhere_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28DequantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28QLinearConcat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QLinearSoftmax_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QOrderedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QuantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DequantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QLinearReduceMean_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QuantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34DequantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35MatMulIntegerToFloat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35QLinearConvTranspose_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36DynamicQuantizeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41QOrderedLayerNormalization_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_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.limitTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.delta1-D Tensor of the range.Can not get shape initializer data!Unsupported non-raw-data data type!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 dimensionsConstant/onnxruntime_src/onnxruntime/core/graph/graph.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&]ForThisAndAllSubgraphs(") : ("") -> () const onnx::TensorShapeProto& onnxruntime::utils::GetShape(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.hTypeProto must have shape for this to runshape != nullptrGraph attribute inferencing failed: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr&] in node No Graph instance was found for attribute This is an invalid model. The sum of input arg count is not equal to size of input defs in node (std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&]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, Node [] op_type [Removable attributes were removed before the node is saved.does not have the graph for key fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg 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.onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()void onnxruntime::Graph::SetInputs(gsl::span)Input to set must exist.input->Exists()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&]LoadEdgesFromOrtFormatinput index: is not the same as this node's index:input edgesoutput edgesvoid onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid node indexes specified when adding edge.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]Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid node indexes specified when removing edge.Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Graph*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::Graph*&]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator]void onnxruntime::Graph::KahnsTopologicalSort(const std::function&, const std::function&) constSome nodes are not included in the topological sort, graph have a cycle._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&]void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph ctor should have created NodeArg for initializer. Missing:node_argThis is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Subgraph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This 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 tensors , or optional typed entities , or sparse tensorsMergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.Output:Type mismatch. Current= Input=UpdateTypeAndShapeOptional Type mismatch. Expected: . Got: This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)AddInitializedTensor already has tensor with name but different TensorProto.existing->second == &tensorThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types ( in node (InferAndVerifyTypeMatchstd::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]Node () output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (Node:) Op (onnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&)UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.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&]bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)Can't remove node as it still has output edges.node->GetOutputEdgesCount() == 0BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&)nullptr != func_meta_defdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()gsl::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 instanceNode::LoadFromOrtFormat, input_arg_counts is missingDuplicate initializer (dense or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.Duplicate initializer (dense, sparse or ConstantNode): 'Node index is out of rangeonnxruntime::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)graph_proto cannot be nullgraph_proto != nullptrDuplicate constant node sparse initializer name: '' Model is invalid.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: 'This is an invalid model. Tensor does not have type information.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.NoOponnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&)Schema was not found for fused node. Domain: OpType: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<>::atvoid onnxruntime::Graph::RemoveInitializedTensor(const string&)sparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0graph_proto_ is not in sync with name_to_initial_tensor_.!foundvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constOuter scope node arg name ''was added but does not exist. void onnxruntime::Node::ToProto(onnx::NodeProto&, bool) constRemovable attributes were removed before the conversion is started.std::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: ) is not a registered function/op) attribute () is required but not specified.This is an invalid model. In Node, , Error PerformTypeAndShapeInferencingSize mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Subgraph input missing type.InferAndVerifySubgraphTypesResolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&)Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&)Node has no function body and cannot be inlined.callnode.TryGetFunctionProto(inlined_fp)InlineFunctiononnx::GraphProto onnxruntime::Graph::ToGraphProto() constFailed to convert dense initializer to sparsevoid onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set >*)Cannot find NodeArgs for [initializer_node_arg != nullptrCleanUnusedInitializersAndNodeArgsRemoving initializer ''. 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 != nullptrRemoving NodeArg 'FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEN4onnx15GraphInferencerEN4onnx24FunctionBodyBuildContextEN4onnx28FunctionBodyBuildContextImplEN11onnxruntime5GraphEN11onnxruntime19GraphInferencerImplEN11onnxruntime20InferenceContextImplEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE*ZN11onnxruntime5Graph49PopulateNodeArgToProducerConsumerLookupsFromNodesEvEUlRKNS_7NodeArgEbE_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_std::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: LoadInitializerOrtFormatMissing 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.LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing 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.Empty graph proto from deserialization of ORT format modelNull 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.FLOATINTSTRINGTENSORFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORSvoid 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]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = int&]const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccAttempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)Attempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.int onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const string&, bool)Attempting to get index by a name which does not exist:for node: itr != node_args.end()void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs: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&]void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast(target_input_idx)onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)Initializer with same name exists. Name:!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; }) == 0bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)Node must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.const std::vector& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const/onnxruntime_src/onnxruntime/core/graph/graph_viewer.ccInvalid ExecutionOrderonnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*)IndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:nodeargMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: N11onnxruntime11NodeCompareEN11onnxruntime19PriorityNodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_typename 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_ONLYbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()/onnxruntime_src/onnxruntime/core/graph/model_load_utils.h are '0' and '1'. The environment variable contained the value: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset&]/onnxruntime_src/onnxruntime/core/graph/model.ccvoid onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map, int>&, const onnxruntime::logging::Logger&, bool, const string&, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Graph is null. Invalid ORT format model.onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&)ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Missing model IR version.Unsupported model IR version: , max supported IR version: ModelONNX 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]No graph was found in the protobuf.LoadFailed to load model with error: less than 0.Load model failed. File doesn't existsystem error number is less than 0.SaveProtobuf serialization failed.SaveModelSt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEstd::pair, onnx::AttributeProto>, false, true>, bool> onnxruntime::utils::SetNodeAttribute(onnx::AttributeProto, onnxruntime::NodeAttributes&)/onnxruntime_src/onnxruntime/core/graph/node_attr_utils.ccAttributeProto must have a name.utils::HasName(attribute)basic_string_view::substr%s: __pos (which is %zu) > __size (which is %zu)/onnxruntime_src/onnxruntime/core/graph/op_identifier.hLoadFromStringInvalid OpIdentifier string: Failed to parse since_version from LoadOpIdentifierOrtFormat/onnxruntime_src/onnxruntime/core/graph/op_identifier_utils.ccSaveRuntimeOptimizationRecordToOrtFormat/onnxruntime_src/onnxruntime/core/graph/runtime_optimization_record_container.ccLoadRuntimeOptimizationRecordFromOrtFormatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::RuntimeOptimizationRecord; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = onnxruntime::RuntimeOptimizationRecord&]Attempting 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_Domain 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/onnxruntime_src/onnxruntime/core/graph/schema_registry.cc, but it its domain is notknown by the checker., but it its version is higherthan the operator set version RegisterOpSetN4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime27OnnxRuntimeOpSchemaRegistryEN11onnxruntime21SchemaRegistryManagerECouple 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]` QWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` MWThe 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]` memory_seq_lensThe 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.` AWA 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]`. onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&)/onnxruntime_src/onnxruntime/core/graph/function.cc is not found or is not constant initializer.initializer != nullptrN11onnxruntime8FunctionEN11onnxruntime12FunctionImplEvoid onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = false]/onnxruntime_src/onnxruntime/core/graph/function_utils.ccNumber of actual parameters cannot exceed number of formal parametersactuals.size() <= formals.size()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 >&]void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = true]TAggregatedTypesall_tensor_types_with_bfloatstd::unique_ptr onnxruntime::function_utils::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, bool)input_arg->Type() != nullptrstd::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)The given function name: , domain: is not found in model local functionsonnx_func_protoTinonnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto&, std::unique_ptr&, const onnxruntime::InlinedHashMap, int>&, const onnxruntime::InlinedHashMap, int>&)::No opset registered for domain in function opset imports.it != opset_imports.end()domain_version != -1Too many inputs for op 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]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 > >]N4onnx11SymbolTableEN4onnx15shape_inference15SymbolTableImplE*ZN11onnxruntime14function_utils12CreateSchemaERKSsS2_RKNS_14InlinedHashMapISsPKN4onnx13FunctionProtoESaISt4pairIS1_S7_EEEERKSt13unordered_mapISsiSt4hashISsESt8equal_toISsESaIS8_IS1_iEEERKNS_21SchemaRegistryManagerERKNS_7logging6LoggerEbEUlRNS4_16InferenceContextEE_*ZN11onnxruntime14function_utilsL22IOTypeConstraintHelperERKN4onnx13FunctionProtoERSt10unique_ptrINS1_8OpSchemaESt14default_deleteIS6_EERKNS_14InlinedHashMapISsiSaISt4pairIKSsiEEEESI_EUlRKNS1_9NodeProtoEE_bool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*)/onnxruntime_src/onnxruntime/core/util/math_cpu.ccdims[d_i] < d_maxvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil]Gemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, ptrdiff_t, ptrdiff_t, ptrdiff_t, T, const T*, const T*, T, T*, Provider*) [with T = double; Provider = onnxruntime::concurrency::ThreadPool; ptrdiff_t = long int]CblasNoTrans Unexpected CBLAS_TRANSPOSE for TransB of CblasTrans Unexpected CBLAS_TRANSPOSE for TransB of Unexpected CBLAS_TRANSPOSE for TransA of Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringSize of affinity string must be between 1 and std::vector > onnxruntime::concurrency::ReadThreadAffinityConfig(const string&)/onnxruntime_src/onnxruntime/core/util/thread_utils.ccProcessor id must consist of only digits: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must start from 1: processor_from > 0 && processor_to > 0Invalid processor interval: processor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; std::vector<_Tp, _Alloc>::reference = std::vector&]Failed to read affinities from affinity stringReadThreadAffinityConfigFound invalid processor id in affinity string: , skip affinity settingFound out-of-range processor id in affinity string: std::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(onnxruntime::Env*, OrtThreadPoolParams)Number of affinities does not equal to thread_pool_size minus one, affinities: , thread_pool_size: actual_num_affinities == static_cast(options.thread_pool_size) - 1custom join thread function not set*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_28MLAS_HALF_GEMM_POSTPROCESSOR*Z17MlasHalfGemmBatchmmmmPK26MLAS_HALF_GEMM_DATA_PARAMSPN11onnxruntime11concurrency10ThreadPoolEEUllE_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_bad dimensions?@@@*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_bad mlas activation kindQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} 2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?33{@33{@33{@33{@A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>l?l?l?l?fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>r1r1r1r1*>*>*>*>>>>>7777e9 (*>(*>(*>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!35MLAS_HALF_GEMM_ACTIVATION_PROCESSORDDDDDDDDŽŽŽŽŽŽŽŽbbbbbbbb99999999[[[[[[[[@@@@@@@@ffffffffVVVVVVVV88888888ȂDDDDŽŽŽŽbbbb9999[[[[@@@@ffffVVVV8888*Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_vector::_M_fill_insert/onnxruntime_src/onnxruntime/core/common/cpuid_info.ccArmLinuxInitFailed to init pytorch cpuinfo library, may cause CPU EP performance degradation due to undetected CPU features.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&)/onnxruntime_src/onnxruntime/core/common/logging/logging.ccDefault logger already set. onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int)ISink must be provided.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.Systemonnxruntime/onnxruntime_src/onnxruntime/core/common/path.ccParsePathRootFailed to parse path root: Parsestatic onnxruntime::Path onnxruntime::Path::Parse(const PathString&)/\onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()/onnxruntime_src/onnxruntime/core/common/profiler.ccenabled_void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)session_logger != nullptrWriting profiler data to file ] {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : " : "} }, std::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&]EndTimeAndRecordEventMaximum number of events reached, could not record profile event.SessionNodeKernelApionnxruntime::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_ARGUMENTINVALID_GRAPHMODEL_LOADEDNO_MODELINVALID_PROTOBUFNOT_IMPLEMENTEDGENERAL ERRORENGINE_ERRORFAILNO_SUCHFILERUNTIME_EXCEPTIONEP_FAILtypename 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&]UnknownEventDistributionEnqueueDistributionWaitWaitRevokestd::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 > >&]thread::id of a non-executing thread": {"num_run": "core": void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)/onnxruntime_src/onnxruntime/core/common/threadpool.ccLogStart must pair with LogEnd!points_.empty()void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >]void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()points_.empty()"thread_id": "", "block_size": [], "core": std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()Profiler not started yet{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function&)n >= 0onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*)Nested parallelism not supported!current_parallel_section.has_value()!ps_void 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.hMore work items than threadsn <= num_threads_ + 1void 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_3EnvEEEZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_?{Gz?*ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE0_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_N11onnxruntime7logging8CLogSinkE\/Failed to get symbol lib.soFailed to load library Got null library handleFailed to unload library with error: file "" failed: closeopenInvalid fd was supplied: fstatReceived negative size from stat callonnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&)/onnxruntime_src/onnxruntime/core/platform/posix/env.ccNegative thread index is not allowedindex >= 0custom_create_thread_fn returned invalid handle.pthread_attr_init failed, error code: error msg: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: cpuinfo_initialize failednftw_removeremove() failed. Error code: , path: UnmapFilemunmap failed. error code: Failed to close file descriptor - error code: ThreadMaincpu does not exist, skipping it for affinity settingpthread_setaffinity_np succeed for thread: , index: , mask: 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.DeleteFolderDeleteFolder(): nftw() failed with error: MapFileIntoMemoryfile_path == nullptroffset < 0mmapReadFileIntoBufferlength > buffer.size()lseekReadFileIntoBuffer - unexpected end of file. File: , offset: , length: realpathN11onnxruntime9EnvThreadEN11onnxruntime3EnvE*N11onnxruntime12_GLOBAL__N_111PosixThreadE*N11onnxruntime12_GLOBAL__N_18PosixEnvEN11onnxruntime9TelemetryE 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 nowSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadTensorShapeOrtFormatLoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.LoadTensorTypeAndShapeOrtFormatNull sequence type info. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.Null map type info. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Type: is not supported currentlyLoadValueInfoOrtFormatNull type info for Sparse tensor indices () first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.Sparse tensor () index value at position [] out of range.] not in lexicographic sorted order.) has values, but NNZ is ] out of range [0, ] not in sorted order.Field 'name' of 'value_info' is required to be non-empty.' is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.Location of external TensorProto ( tensor name: ) should not be empty.) should be a relative path, but it is an absolute path: ) should be file inside the , but the '' points outside the directory) should be stored in , but it doesn't exist or is not accessible., but it is not regular file.) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data field' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.void std::basic_string<_CharT, _Traits, _Alloc>::pop_back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator]Warning: Model contains experimental ops:ATenConstantFillop_typeNodeProto (name: , type: ) has zero input and zero output.No opset import for domain '' appeared multiple times.ai.onnx.trainingNo Op registered for with domain_version of Op registered for is deprecated in domain_version of Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.initTensor 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 '' of node: OpType: is not output of any previous nodes.' has been used as output names multiple times.Bad node spec for node. Name: ==> Context: attrtype field and data field mismatch in attribute Attribute (name: ) 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&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char]tensor(float8e4m3fn)tensor(float8e4m3fnuz)tensor(float8e5m2)tensor(float8e5m2fnuz)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(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))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(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))Condition for the ifcondValues 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.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.Only bool/build/Release/_deps/onnx-src/onnx/defs/controlflow/defs.ccA 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)v_initialFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.bodytensor 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_inputsinitial_state_and_scan_inputsFinal values of the loop's N state variables followed by K scan_outputsfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An 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].All Tensor types up to IRv9.then_branch and else_branch produce different number of outputs. but subgraphs produce If node has 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] else= then=Mismatched type for output Mismatched tensor element type for output is invalid for a tensor of rank axis value 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 Graph attribute inferencing returned type information for was notScan 'body' subgraph outputs should all be tensors but output Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (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 complex64complex128float8e4m3fnfloat8e4m3fnuzfloat8e5m2float8e5m2fnuzInvalid tensor data type tensor(seq(optional(map(sparse_tensor(Unsuported type proto value case.DataTypeUtils::FromDataTypeString - Received invalid data type string 'optionalmapopaqueInput 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-dimensionalX_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.sparse_valueThe value for the sole element for the scalar, int64, output tensor.value_intThe values for the elements for the 1D, int64, output tensor.value_intsThe value for the sole element for the scalar, float32, output tensor.value_floatThe values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.value_stringThe values for the elements for the 1D, UTF-8 string, output tensor.value_stringsOutput tensor containing the same value of the provided tensor./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 input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.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.highThe 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 distributionThe 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.(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.Scalar. Value to step by.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) }> } 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.BernoulliAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorOne 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.Output type must be int32 or int64Input tensor must have rank 2*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_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_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_First input operand for the bitwise operator.Second input operand for the bitwise operator.Result tensor.First input operand for the logical operator.Second input operand for the logical operator.andConstrain input to boolean tensor.Constrain output to boolean tensor./build/Release/_deps/onnx-src/onnx/defs/logical/defs.ccxorgreaterConstrain input types to all numeric tensors.lessConstrain 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.*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx25BinaryBitwiseDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx25BinaryBitwiseDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_Enable broadcastingbroadcastIf 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 to float tensors.Constrain input to integral tensors.*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_*ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_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.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. periodicA scalar value indicating the length of the window.A {name} window with length: size. The output has the shape: [size].output = 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)Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)equationdata_0List of tensors for Output tensor.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1./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 treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.divisionConstrain 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 XslopeOutput tensor (same size as X) { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } Value 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 tensorValuesTensor 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.IndicesConstrain 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.The 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 ascale of quantized input azero point of quantized input aN-dimensional quantized matrix bbscale of quantized input bzero point of quantized input bscale of quantized output yzero 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.Zero 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 onlyConstrain 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.targetOptional 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 losslossType 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.ignore_indexConstrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossEinsum 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.labelsA 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).log_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. dft_lengthThe 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.HannConstrain 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.num_mel_binsThe 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.sample_rateLower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.lower_edge_hertzThe desired top edge of the highest frequency band.upper_edge_hertzThe Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].Constrain to integer tensors.Constrain to float tensorsConstrain to any numerical types.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.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.signalThe number of samples to step between successive DFTs.frame_stepA 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.frame_lengthThe 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.Invalid rank for broadcasting: () 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.is_onesided and inverse attributes cannot be enabled at the same timeaxis attribute value dft_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.output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels)Shape3D X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) log_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)const_zeroconst_oneexpanded_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_ignore_index const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) input_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_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_floatconst_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) 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_13FunctionProtoEEPFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15STFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26MelWeightMatrix_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx29CosineSumWindowOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx29CosineSumWindowOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ 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. Pass 1 to enable broadcastinglegacy optimization attribute.consumed_inputsIf 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.ccsoftmaxlogsoftmax1 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_targetsinput_gather_element_transformsqueeze_maskweight_gather_tempweight_gather_temp_1*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_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_The input tensor of rank >= axis.The output values with the same shape as the input tensor.*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.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]).Constrain 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_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).averageDilation 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.ccThe 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.offset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.DeformConvlp 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).momentumIf 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 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).input_meanrunning (training) or estimated (testing) variance tensor of shape (C).input_varThe running mean after the BatchNormalization operator.running_meanThe running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.running_varConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.The 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) } A 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 overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum 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 stringsstring 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.image_shapeThe 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.block_shapeOutput tensor produced by rearranging blocks into an image.Constrain input and output types to all numeric tensor types.The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r]. Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Tensor to be normalized.Normalized tensor.Constrain input types and output Y type to float tensors.The number of groups of channels. It should be a divisor of the number of channels `C`.num_groupsInput 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`.GroupNormalizationThe pads attribute cannot be used simultaneously with auto_pad attributeAttribute pads must have an even sizeinput must have rank 3.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.Invalid value() for attribute 'axis'sinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalization%s:%u: %s: Assertion `%s` failed.FloatEpsilonEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)C = Shape (X)NumGroupsGroupSize = 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)Axes2Mean = ReduceMean (X3D, Axes2)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)*ZN4onnx11GetOpSchemaINS_17Col2Im_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21DeformConv_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29GroupNormalization_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_29LayerNormalization_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_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.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.is_testThe 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_meanSaved variance used during training to speed up gradient computation. Should not be used for testing.saved_var Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. 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_squaredX_squaredE_XsquaredVarianceSTDX_varianceProcessed_STD*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_*ZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZN4onnx26LpPoolOpSchemaGenerator_11EPKcEUlRNS_8OpSchemaEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx26LpPoolOpSchemaGenerator_11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_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.ccAn 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.max_output_boxes_per_classFloat 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].selected_indicesInteger 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.*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.The input element.Type of the element in the optional outputThe optional output enclosing the input element.Constrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types./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 input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.Output element in the optional input.Constrain output type to all tensor or sequence types.Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.OptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.*ZN4onnx11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input must be an optional-type value containing an element with type information./build/Release/_deps/onnx-src/onnx/defs/optional/old.ccOptionalHasElement is expected to have 1 input.*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ column: (line: Error context: [ParseError at position Identifier expected but not found.Incomplete string literal.Value expected but not found.Integer value expected, but not found.String value 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: %dtype_protofloatsintssparse_tensorstype_protosir_versionopset_importproducer_nameproducer_versiondomainmodel_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.saturateConstrain '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_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_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 { 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) } *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ 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*ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_*ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_*ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_axes as an input and attribute cannot be specified at the same time.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.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).*ZN4onnx29ReduceDocGenerator_opset13_18EPKcbbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEEUlRS6_E_*ZZN4onnx29ReduceDocGenerator_opset13_18EPKcbbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEENKUlRS6_E_clESD_EUlRNS_16InferenceContextEE_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 3One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The 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_fowardOptional 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.output_sequenceA 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) *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_Attributes:Inputs:(unnamed)(no doc)(no type) (no explicit description available)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.trainingstd::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&] typestr: , has unsupported type: has inconsistent type has unsupported type 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]Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Unrecognized attribute: for operator Mismatched 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' has unknown expected typeRequired attribute '' is missing.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())Duplicate type constraint nameAttribute specification type mismatch.Cannot find a function builder that satisfies the requested opset version: op_type = , opset_version = Error parsing function body:Extra unparsed input unexpected.N4onnx16OpSchemaRegistryESt15_Sp_counted_ptrIPN4onnx13FunctionProtoELN9__gnu_cxx12_Lock_policyE2EE*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_Attribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input type for input at index 0 is null. Type info is expected.new_axisnew_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Invalid value of attribute 'axis'. Rank= Value=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=Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Input Sequence and Tensor are expected to have the same elem type. Sequence=(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence.Constrain output types to any tensor type./build/Release/_deps/onnx-src/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.Constrain input types to any tensor type.Input sequence.input_sequenceInput 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).positionOutput sequence that contains the inserted tensor at given position.Constrain to any tensor type.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 graphadditional_inputsOutput sequence(s)out_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMap expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_body_itercount_cond_in_cond_out_inSequenceMap__seqlen_cond_seqempty*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input type was nullElement type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got Input element type of 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 unknownMismatched type: source= target=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.shape input must be 1D tensorOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps. Expected range [Unexpected axis value: data tensor must have rank >= 1axis must be in [-r, r-1]'step' cannot be 0 for SliceBoth `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` tensorOneHot 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 >= 1'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.axis must be in [-rank, rank-1].All inputs to Concat must have same rank. Input has rank 'input' must have rank >= 2'sequence_lens' must have rank of 1Invalid attribute perm {}, input shape = {Attribute perm for Transpose has repeated value: does not match type of output: and 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]output = 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.Input tensor to be cast.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.ccThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 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.target_typeOutput tensor produced by casting the first input tensor to have the same type as the second input tensor.(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)updatesTensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.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 tensorList 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).expandedBlocks 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].DCR (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.1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.repeatsOutput 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.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorThe 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'.N-D tensor after resizingConstrain 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"roiThe 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. sizesConstrain roi type to float or double.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.grid4-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.depthRank 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.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain 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 of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(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'. inverse_indicesA 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'The 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.input_data1-D tensor representing the cropping window dimensions.Output data.output_dataIf provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.CenterCropPadindices must be in [-rank, rank-1].Input rank for starts and ends should be the same: (Target shape may not have multiple -1 dimensions.Invalid position of 0.Invalid dimension value: Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesBoth 'split' input and 'num_outputs' attribute were givenMismatch between number of splits () and outputs (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 givenSlice 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 lengthBlocksize must be positiveInput tensor must be 4-dimensionalk2axes_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)x_shape = Shape (input_data)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_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. Attribute value for pads is requiredAttribute pads has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute axes has incorrect length'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64'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.rank must be greater than axisTarget shape may not have multiple -1 dimensionsInvalid position of 0Invalid Target shape product 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.Supported 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).constant_valueConstrain 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`.paddingsThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapeNumber of repeated copies to make of the input tensor.tilesAxis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scaleTwo interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.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_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_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 values.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' (*ZN4onnx15PadDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE0_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE_*ZZN4onnx15PadDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ is undefined so it cannot be parsed.The type of tensor: Actual:. Expected: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 size expected size Data size mismatch. Tensor: map(string, int64)map(int64, string)map(int64, float)map(int64, double)map(string, float)map(string, double)Exactly 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.The 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'.map_formIf the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput 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.A 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 listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.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 ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Data to be classified.Classification 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).A collection of intercepts.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.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 model(s).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'normData 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.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe 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.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.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 classesNode 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 node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines 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_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./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:unk__type case unsupported for symbolic shape inference. inferred=tensor_typesequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SETInferred 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= is out of bounds.GraphProto 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]Cannot infer type and shape for node name . No opset import for domain optype Inference error(s): Shape inference error(s): Graph has inputs but were providedCannot use the same name as both a subgraph initializer and subgraph input: were provided.The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: Cannot find missing input: in initializers. 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]onnx.TypeProtoonnx.TensorShapeProto.Dimensiononnx.ModelProtoonnx.ValueInfoProtoonnx.GraphProtoonnx.TypeProto.Optionalonnx.AttributeProtoonnx.StringStringEntryProtoonnx.TypeProto.Opaqueonnx.TensorProtoonnx.TypeProto.Maponnx.TensorAnnotationonnx.TensorProto.Segmentonnx.TypeProto.SparseTensoronnx.FunctionProtoonnx.TypeProto.Tensoronnx.TrainingInfoProtoonnx.TensorShapeProtoonnx.TypeProto.Sequenceonnx.OperatorSetIdProtoonnx.SparseTensorProtoonnx.NodeProtog_OD6(lXG'N6google8protobuf8internal16InternalMetadata13ContainerBaseEN4onnx14AttributeProtoEN4onnx14ValueInfoProtoEN4onnx9NodeProtoEN4onnx17TrainingInfoProtoEN4onnx10ModelProtoEN4onnx22StringStringEntryProtoEN4onnx16TensorAnnotationEN4onnx10GraphProtoEN4onnx19TensorProto_SegmentEN4onnx11TensorProtoEN4onnx17SparseTensorProtoEN4onnx26TensorShapeProto_DimensionEN4onnx16TensorShapeProtoEN4onnx16TypeProto_TensorEN4onnx18TypeProto_SequenceEN4onnx13TypeProto_MapEN4onnx18TypeProto_OptionalEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_OpaqueEN4onnx9TypeProtoEN4onnx18OperatorSetIdProtoEN4onnx13FunctionProtoEN6google8protobuf8internal16InternalMetadata9ContainerISsEEIR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19IR_VERSION_2020_5_8IR_VERSION_2021_7_30_START_VERSIONEXPERIMENTALSTABLEBFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16FLOAT8E4M3FNFLOAT8E4M3FNUZFLOAT8E5M2FLOAT8E5M2FNUZINT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINEDDEFAULTEXTERNALFLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINED   onnx.MapProtoonnx.OptionalProtoonnx.SequenceProtoN4onnx13SequenceProtoEN4onnx8MapProtoEN4onnx13OptionalProtoEMAPOPTIONALSEQUENCESPARSE_TENSORTENSORUNDEFINEDMAPOPTIONALSEQUENCESPARSE_TENSORTENSORUNDEFINED/build/Release/_deps/protobuf-src/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::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(cannot determine missing fields for lite message)/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/build/Release/_deps/protobuf-src/src/google/protobuf/parse_context.hCan't happen[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 Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: Unexpected re_anchor value: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError parsing '': Error compiling 'pattern too large - compile failedError reverse compiling 'std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; std::deque<_Tp, _Alloc>::reference = re2::WalkState&]void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]/opt/rh/devtoolset-10/root/usr/include/c++/10/bits/stl_stack.hunexpected error/build/Release/_deps/re2-src/re2/regexp.ccNumCapturesWalker::ShortVisit called/build/Release/_deps/re2-src/re2/walker-inl.hStack not empty.Regexp not destroyed.Bad reference count Walk NULLstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]Unexpected op in Regexp::Equal: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Regexp*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::RuneRange; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::RuneRange&]N3re26Regexp6WalkerIiEEN3re217NumCapturesWalkerEno errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture group@B/build/Release/_deps/re2-src/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = typename 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]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: 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.ccCompiler::Copy called!void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]should never happenstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]No ranges in char classMissing case in Compiler: ' 3=nraN\aBN3re26Regexp6WalkerINS_4FragEEEN3re28CompilerEtypename 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.ccunhandled opcode: DeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnBytecontext does not contain textFailed to analyze start state.StateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/build/Release/_deps/re2-src/re2/nfa.ccunhandled in Prog::Fanout() 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]Unhandled in stepBad args: nsubmatch=Unexpected opcode in short circuit: /build/Release/_deps/re2-src/re2/onepass.ccCannot use SearchOnePass for unanchored matches.std::vector<_Tp, _Alloc>::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 calledAddFoldedRange recurses too much.RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAnyBad hex digit Concat of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Frame&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Splice; _Alloc = std::allocator; std::vector<_Tp, _Alloc>::reference = re2::Splice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]unknown round: void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = re2::Frame; _Alloc = std::allocator]LuLlLtLmLoNlMnMcNdPcN3re216RepetitionWalkerE[: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\W09 09AZ__az09AZazAZaz 09!~az ~!/:@[`{~ AZ09AZ__az09AFafstd::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&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = 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.ccMalformed repeat SimplifyWalker::ShortVisit calledCoalesceWalker::ShortVisit calledvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState&]DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is Case not handled in ComputeSimple: Simplify case not handled: N3re26Regexp6WalkerIPS0_EEN3re214CoalesceWalkerEN3re214SimplifyWalkerE[]^-\\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\basic_string::_M_replace_aux(?:?P</build/Release/_deps/re2-src/re2/tostring.cckRegexpCapture cap() == 0[^\x00-\x{10ffff}](?-m:^)(?-m:$)(?:)basic_string::eraseBad final char: ?{%d,}{%d}{%d,%d}\C^$\z\b\B(?HaveMatch:%d)|TPIP_lR0sh [truncated]N3re214ToStringWalkerEAZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&C&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}8`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,/,00,_,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~Z0ZZZZZZZZçħħŧŧZƧƧuǧʧЧѧ֧٧SS`p0h!: AZ'((O(pz'|'''  @   @n_n `nn!""CoAdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeChorasmianCoCommonCopticCsCuneiformCypriotCypro_MinoanCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLaoLatinLepchaLimbuLinear_ALinear_BLisuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoModiMongolianMroMultaniMyanmarNabataeanNandinagariNew_Tai_LueNewaNkoNoNushuNyiakeng_Puachue_HmongOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOld_UyghurOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangsaTangutTeluguThaanaThaiTibetanTifinaghTirhutaTotoUgariticVaiVithkuqiWanchoWarang_CitiYezidiYiZanabazar_SquareZlZpZs  * . ` d f o 0484sz az      !!##%%''))++--//11335578::<<>>@@BBDDFFHIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwzz||~      !!##%%''))++--//1139<<?@BBGGIIKKMMOqqssww{}0_aacceeggiikkmmooqqssuuwwyy{{}}      !!##%%''))++--//`+kwy      !!##%%''))++--//1133557799;;==??AACCEEGGIIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwyy{{}} '07@EPW`gp} ! !!!!!/!/!4!4!9!9!>@ACDF=Pptvfoq  &(:<=?MP]-@BIPuP'0c6@U`g 578<<?U`v   9            5 ` |        5 @ U ` r    H  # ''0Ep7qruu&DDGGPrvv+ (*02359==PP]a4GJ_a/DD@F+   /??AA 2::PP\ .@@r  0FF`eghj #$C%//0.4DFFh8j@j^jpjjjjk/kckwk}kkoJoPoPopՌ"PRdgpjp|  ,NN!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~ߦ8@ JAZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! ! !!!!!!!$!$!&!&!(!(!*!-!0!3!>!?!E!E!!!,/,`,`,b,d,g,g,i,i,k,k,m,p,r,r,u,u,~,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjll""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~§§ħǧɧɧЧЧ֧֧اا!:'pz|  @n_n4Mh  89;>@DFFJPl!<Up4Vn!  ; 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H&4QNABBAC@?B>=B<;B:9C87 A H0'TNABDCCBAB@?B>=B<;C:9 A 4|'|X<NA:9D87C65B4l A H'\NABDCCBAB@?B>=B<;C:9 A H((`xNABDCCBAB@?B>=B<;C:9 A HL(cTNABDCCBAB@?B>=B<;C:9 A H(pg0NABDCCBAB@?B>=B<;C:9 A (4k NAC E JD BLAAJA OA A AA BQ AOI AA0l+mA BL A D A +m$0+nA BL A D A +tn$ +(<A BI,,*lnLAp D C r A ,\*,qLAp D C r A 0*sLA>=D<;C:9| A (*hxMA@D] A 0*y MA65D43B21t A 8 +h~2MA@[ A G A B`B0\+ MAp D B j A 0+/MAp D B j A l+>MACIBBxAAIAXAAA8(.A BL A H A L A 8d.A BL A H A L A t, hpLABHGBFEHCDe AA P AA L AA A ABA?@A=>A BABB?@p=>QABB?@D=>uO=>IJ ABA?@A=>C BBA@?IFABB?@l=>sNBA@?>=OIN?@CABf=>BABB?@l=>GAGABA?@A=>B A L A KMAABA@?>=OB>=J\HBA@?>=CCBA@?>=00D CM A F A 0L0`D CM A F A `.KAp D E T_R A u A `IK A dA00 1, 1,41,H14,\1P,p1l,1,1,1,1ܟ,1,1,10,2L,$2h,82,L2,`2,t2ؠ,2,2,2,,2H,2d,2,3,3,(3ԡ,<3,P3 ,d3(,x3D,3`,3|,3,3,3Т,3,4,4$,,4@,@4\,T4x,(h4`A DI A H(4ȣ`A BM A F4 04A@BEBVAB(5TA Df A Dl45lC@CBY A Y A PB@I A 05,A Ba A _ A H5XA0CK A C[ AA JGND048_FCD CE C A O A GA FBrA BDDFFDHDFC A B 7L{'xB BX<05HEAp C H v A ,d7زlA BR A C5[EAB@?C>=I<;G9:pLA>=<;:9@?AI9:MC:9A56B78L AB IA78D56d CAB DC8765YA9:A78A56SB 9:A78A56A m:98765AO9:A78A56A86DAE E F A H6ȺXDA>=D<;B:9C87C65C43p A 07ܼbDAP b A A A B@P78CDADBAC@?E>=<;:98W A 9x7$DAP BEAd BAA C BAA ZrFO BAAA DG A C4$8tHCA87D65B43C2X A 4\8PCA87D65C43B2Z A 48PCA87D65C43B2Z A x8`CAG Z A g B CR AAAA _ AAA UA A A4H90PCA87D65C43B2Z A 09HBA0^ A ] A B`9BAG Z A g BC AAA LA AA\:`BA>=C<;q9:B78C56A34OAAAI A M9:P56A78B34|F9:P56A8743[A9:A78A56A34rH78IL 78A34C HE56AC9:P56A8743[H78IC8743MED56AC9:P56AC9:P56I78ICC65E8743JC65M78IK78ICC65EC658x;=BA>=E<;E:9876U A =H;|z ABAA OAAAADCB@?>=<;BSBAADCBAFEBACDA?@A=>A;AM?@A=>A;UJ ?@A=>C BI ?@A=>A;A;<,?,@?|A CK A N,p?|A DJ A N,?l|A DJ A N,?4A CK A |,@4A CK A |,0@4A CK A |,`@4A CK A |4>?A0Er A I A 8>?A@GZ A P A <?(?AP DBs A C A DP? ?ADHGCFEEDCBA@?>= A A0?2@A<;D:9B87o A ?8%@ABBAB@?I<;B=>U9:B78AKBA@?>=<;:987BA BAAA wA9:A78DR78A9:D[ 9:C HC78BA:987WD78D@+8 6@AEG V A BX'<A BI,AT4?A('D&%B$Y A <DA4d?A` E ] A B[JBA8A5?ABrqCpoEnmlk A 8AX8?ABtsCrqEponm A 8A:f?ABPOCNMBLKCJI A <8B=hU?ABXWCVUBTSERQP A <xB@0F?ABVUCTSBRQEPON A 8BC6?ABTSCRQBPOCNM A 8BF*?ABPOCNMBLKCJI A <0CXI?ABXWCVUBTSERQP A 8pCM ?ABPOCNMBLKCJIr A <COX>ABTSCRQBPODNML A 8CR|>ABRQCPOBNMCLK A <(DU>ABVUCTSBRQDPON A <hDY>ABXWCVUBTSDRQP A <D\(>ABTSCRQBPODNML A 8D_(>ABTSCRQBPOCNM A 8$Eb(>ABTSCRQBPOCNM A 8`Ee>ABPOCNMBLKCJIr A 8E,hDy>ABRQCPOBNMCLK A <E@km>ABRQCPOBNMELKJ A 8Fn\W>ABRQCPOBNMCLK A <TF$q4K>AB`_C^]B\[EZYX A <F$w8M>ABZYCXWBVUETSR A TF${ C>A BCCBC A <,ḠA>ABRQCPOBNMDLKJ A DlG,>ABbaC`_B^]E\[ZYX A 4G3>ABLKCJIBHG` A 4G$>ABLKCJIBHGr A 8$H =ABPOCNMBLKCJI A 8`Hh=ABPOCNMBLKCJIn A 8Hܕ=ABRQCPOBNMCLK A 8Hx=ABPOCNMBLKCJI A 8I=ABPOCNMBLKCJI A 0PI(=ABFECDCBBs A 8I=ABPOCNMBLKCJI A <Ihq=ABXWCVUBTSERQP A 0Jb=Ap C M QI A A l4JD@=ACE B ~ A A { A FC rO QD F I C0Jĩt=Ap G A A <JP<ACBAC@?E>=<;:E A  M 8,K <AC F l A DhK(<Ap C L bIAp  GA lKز<ACT B C dAAIA WA A A LhL<ADNMDLKJICDC[GHEEF_EHALKJIHGDCNMAIGHBEF BA A AA G AB NBHGFELAGHAEFB AB IGHAEFZAGHAEFBGHAEF0MO<Ap C M QI A A 04M-<Ap C M QI A A thMP@ <ABDCCBAH@?>=<;:M A Y:aB:AE:SA:D:r[:SZ:BJ:\I:,M<A` G Z A 8N<AEE b A <LN<<A>=G<;:9C8765 A PN <ABPOCNMIKLAIJ{ AAA [GHAEF AA h AA TAGHAEFRGHBEFR[GHAEFCSHGFE,QpLOl<A>=E<;S9:F56SAAA<;:9>=S78A56RC78 A A 78A C87SAI78A56DB8765eD9:C78A56QP<A>=E<;S9:F56SAAA<;:9>=S78A56RC78 A A 78A C87SAI78A56DB8765eD9:C78A56RHPHB<ABTSCRQBPOCNMELKJI A SS0SDSXSlS,S,S,S4,SP,Sl,S,S, T, T,4T,HT,\T0,pTL,Th,T,T,T,T,T,T,U,,$UH,8Ud,LU,`U,tU,U,U,U ,U(,UD,U`,V|,V,(V,=I<;G9:pLA>=<;:9@?AI9:MC:9A56B78L AB IA78D56d CAB DC8765YA9:A78A56SB 9:A78A56A m:98765AO9:A78A56ADZ0!3AEG V A ,T\'dA BR A BZ(LG3CD CI G A P A GB FBnBDDFFDHDFC A BxH[-2AP BEAd BAA C BAA ZrFO BAAA DG A CD[$0t2AD@?C>=E<;:98765W A ^6 \6 c2ADJICHGGFE@?S A }CDvAB AA W AA ACDAAB}IICDAABHA B BD CDAABA ACDAABAO ABA BABANCDCRBA_ A x\B2AG Z A g B CR AAAA _ AAA UA A Ax`]XI1AG Z A g B CR AAAA _ AAA UA A A]O1Ap G Z A gBlAAACAAHAAAANoJC`l^T1AG Z A g BC AAA LA AA4^xZP0A87D65C43B2Z A 8_\`0A87D65B43C21^ A <D_^h0AE F V A l_c40A>=C<;q9:B78C56A34OAAAI A M9:P56@78A34~F9:P56A8743[A9:A78A56A34dH78IL 78A34C HE56AC9:P56A8743`H78IH8743ND56AC9:P56AC9:P56I78ICC65E8743dH78ICC65E8743IH78ICC65EC65@`sA0AB@?C>=F<;:9876T A ,c@z<La=E<;G:987R A Q6|b6SC6C A HA6c@a90A<;D:9B87C65C43h A @8b`0AEG V A L|b/AE G Q A t  A A A dA ,dA Cl A R,dl4A CK A |, e|4A CK A |,Pe4A CK A |,e4A CK A |,e4A CK A |,e4A CK A |,f̙4A CK A |,@fܚ4A CK A |<|d..AP EBr A C A fff,fA Dk A R@(e-A:9D87B65C43C21h A Dle-A>=D<;B:9C87C65C4o A Dedv-A<;D:9B87C65C43C2n A eܧHG-ADHGCFEKCDBABmAJAFEDCBAHGn=>P?@z AABA y AAAA CB>=TAAAAFEDCBAHGBACDAABA?@A=>AA?@AM?@A=>AM?@MD?@C=>GJ?@CI?@A=>AB>=B@?i$gz,AEMBmAIAmIm AABA x AAAA CBAAAABAAAAAMAAMkDCQJ C BJACDh$4+ADHGCFEEDCBA@?>=W A Pjphd8,AE^BVAIAJB  AA [AAA G BA NAAAA B AB A AB BAAA Did+AE^BVAIAJB  AA [AAA G BA NAAAA B AB A AB BAAA 8jP+AP BDB A Tj u+ADJICHGGFE@?S A rCDMAByD A AADCBAgBBAs AA EACDAABwB CDAABA ICDAABnACDAAB l'<A BI8 kX*A21C0/G.-,+N A <\k d+A` E ] A B[JBA8k<*ABhgCfeBdcCb A 8k*ABhgCfeBdcCb A 4lDX*ABbaC`_E^]\| A LLllt{*ABRQCPOCNMLK A HJJ B HE JA XJCLltU*ABRQCPOCNMLK A HJJ B HE JA XJC<l/*ABVUCTSCRQPOCN A <,m\*ABVUCTSCRQPOCN A <lmp*ABRQCPOCNMLKCJ A Lm\)ABRQEPONMLK A HJJ B HE JA XJCLm,|)ABRQCPOCNMLK A HJJ B HE JA XJCLLn\)ABRQCPOCNMLK A HJJ B HE JA XJC<n)ABTSCRQBPOENML A Ln\r)ABRQEPONMLK A HJJ B HE JA XJC8,o |L)ABRQCPOBNMCLK A LhoP@)ABRQCPODNMLK A HJJ B HE JA XJCLo)ABRQCPODNMLK A HJJ B HE JA XJC<p(ABVUCTSBRQDPON A <HpP(ABTSCRQBPODNML A <pP(ABTSCRQBPODNML A <px(ABXWCVUBTSDRQP A <q!(ABTSERQPONMCL A LHqp%\(ABRQEPONMLK A HJJ B HE JA XJCLq(\s(ABRQEPONMLK A HJJ B HE JA XJCLq+\M(ABRQEPONMLK A HJJ B HE JA XJCL8r.'(ABTSERQPONM A HLI B HG LA WLC<r02(ABRQCPOCNMLKCJ A <r54'AB`_C^]B\[EZYX A <s;t'AB\[CZYBXWEVUT A <Hs@d'AB\[CZYBXWEVUT A LsFP'ABRQCPOCNMLK A HJJ B HE JA YJCLsIP'ABRQCPOCNMLK A HJJ B HE JA YJC4(tL'ABJICHGBFE_ A L`tMD'ABRQCPOCNMLK~ A HJJ B HE JA XJCLtPZ'ABRQEPONMLK A HJJ B HE JA XJC8uS|5'ABRQCPOBNMCLK A 8=E<;S9:F6SAAA<;:9>=S78A6RC78i A uA 78A C87WAA876DD9:C78A6GH78A6CB876H!A87E65S34F0SAAA654387S12A0AC12i A uA 12A C21QB210lH12A0ED34C12A0DAA21040H!A87E65S34F0SAAA654387S12A0AC12i A uA 12A C21QB210lH12A0ED34C12A0DAA210Pdx, ,(HȈd,(܈)'\A DN A BPTD@BBQB@Db A JKDKEEHhg ADEBB" A X'w AP BFBGVAABAP LAA'8$ AJBR C AAEAHAK l AAAA RAAA A P*$l*Ap B D FMC@BD AA IBYAC AA Y[ AAAA RAA} AAAA DBFCUCAAGAAH.ABBAC@?B>=D<;:9C87 A Hp2ABBAC@?B>=D<;:9C87 A H45zABDCCBAB@?B>=B<;C:9 A H9$VABFECDCBBAB@?B>=C<; A H̊|= 2ABFECDCBBAB@?B>=C<; A dPA(Ap C J BBJAAIAp  NA AAdC(Ap C J BBJAAIAp  NA AATDrAp E U A p BQ AA A AA yA AH@XK ABFECDCBBAB@?B>=C<; A D,OgADJIDHGFEDDCBA@?W A Ȏ] ܎'<A BI \]4|ACTSCRQW A fOPBMNCKLAIJ AAAA Q AAAA AOPAMNAKLAIJxxx(ď\#'\A DN A B,wA0BC^ A P xD@BBQB@Db A JKDKEE(zvAp B D FMC@BD AA IBYAC AA Y[ AAAA RAA} AAAA DBFCUCAAGAAHp~@ABFECDCBBAB@?B>=C<; A HxABDCCBAB@?B>=B<;C:9 A H`TABDCCBAB@?B>=B<;C:9 A HT80ABFECDCBBAB@?B>=C<; A \ AD H M  DA jB cB ~B \AD H M  DA iB cB ~B \`)ADH M  DA lB cB @B \7AD H M  DA iB cB ~B '<A BIDD!A@DDBHT HA L HA @|A@DDBT A L A @ВA@DDBT A L A PAP DBNBHYAHDAP HLh0IAP DBCLZEAP LЮ AP DBCLZEAP 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