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vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)type with contained types did not overload createWithContained: ../aten/src/ATen/core/jit_type_base.hArrayRef: invalid index Index = Cannot create dict for key type '', only int, float, complex, Tensor, device and string keys are supported../aten/src/ATen/core/jit_type.hRun NNC AOT compiler for pytorch model. Example usage:
build/bin/aot_model_compiler --model=<model file> --model_name=<model name> --model_version=<model version> --input_dims=<input dimensions like '1,3,224,224;2,2'> --input_types=<input dtypes like 'float;float'> --input_memory_formats=<input memory formats like 'channels_last;contiguous'> [--method_name=<method name>] [--output_llvm=<llvm assembly output file path>] [--output_model=<output model file path>]Failed to parse command line flags!../binaries/aot_model_compiler.ccNumber of input_dims and input_types should be the samedims_size == split(';', FLAGS_input_types).size()Number of input_memory_formats should be 0 (default contiguous) or the same as number of input_dimsmem_formats_size == 0 || mem_formats_size == dims_sizeThe compiled model was saved to (std::string, default "") The torch script model to optimize.(std::string, default "") The name of the model.(std::string, default "") The version of the model.(std::string, default "") The dimensions of input TensorCPUs using comma separated numbers.If multiple inputs needed, use semicolon to separate the dimension of different tensors.(std::string, default "float") The dtype of input TensorCPUs.If multiple inputs needed, use semicolon to separate the dtype of different tensors.Supported dtypes: float, int64, uint8(std::string, default "") Input memory format.If multiple inputs needed, use semicolon to separate.Supported values: contiguous, channels_last(std::string, default "") Comma separated dimensions of input tensors that can be dynamic(std::string, default "forward") The name of the method.(std::string, default "") Name of the output llvm assembly to be saved.(std::string, default "") Name of the output model to be saved.Key already registered with the same priority: Higher priority item already registered, skipping registration of NoneTensorStorageDoubleComplexDoubleIntSymIntSymFloatBoolTupleStringBlobGenericListGenericDictFutureAwaitDeviceStreamObjectPyObjectUninitializedCapsuleRRefQuantizerGeneratorEnum%dInvalidTag()Can't hash IValues with tag ''createWithContained; Length = ../c10/util/ArrayRef.hatDict[, ]Dict(vector::_M_realloc_insertcreateExpected 2 contained types!FLAGS_model.empty()!FLAGS_model_name.empty()!FLAGS_model_version.empty()!FLAGS_input_dims.empty().compiled.ll.compiled.ptsizestypesmemory_formatsdynamic_sizesasmfilemodel_namemodel_versionnncmodelinput_dimsfloatinput_typesinput_memory_formatsdynamic_dimsforwardmethod_nameoutput_llvmoutput_model%s
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