U h@sddlZddlZddlZddlmZddlmZddlZddlZddl m Z ddl m Z e eZd eeeeeeeeeeeeeedd d d ZdS)N)Path)Optional)SymbolicShapeInference)add_pre_process_metadataF)input_model_pathoutput_model_pathskip_optimizationskip_onnx_shapeskip_symbolic_shape auto_mergeint_maxguess_output_rankverbosesave_as_external_dataall_tensors_to_one_fileexternal_data_locationexternal_data_size_thresholdreturnc  CstjddT} t| }d}|sBtdtt|||||}|s|st |d}| rrtj ||d| | ddn t ||d}t |d }z0t }||_t jj|_t j||d gd }~Wn,tk rtd ttYnX|}|s^|dk r:t |d}| r*tj ||d| | ddn t ||d}t |d }tj||t|}W5QRX|dkr|t|}t|| rtj ||d| | | ddn t ||dS)aShape inference and model optimization, in preparation for quantization. Args: input_model_path: Path to the input model file") output_model_path: Path to the output model file skip_optimization: Skip model optimization step if true. This may result in ONNX shape inference failure for some models. skip_onnx_shape: Skip ONNX shape inference. Symbolic shape inference is most effective with transformer based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with unknown shape can not be quantized. skip_symbolic_shape: Skip symbolic shape inference. Symbolic shape inference is most effective with transformer based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with unknown shape can not be quantized. auto_merge: For symbolic shape inference, automatically merge symbolic dims when conflict happens. int_max: For symbolic shape inference, specify the maximum value for integer to be treated as boundless for ops like slice guess_output_rank: Guess output rank to be the same as input 0 for unknown ops verbose: Logs detailed info of inference, 0: turn off, 1: warnings, 3: detailed save_as_external_data: Saving an ONNX model to external data all_tensors_to_one_file: Saving all the external data to one file external_data_location: The file location to save the external file external_data_size_threshold: The size threshold for external data z pre.quant.)prefixNz&Performing symbolic shape inference...zsymbolic_shape_inferred.onnxTF)rrsize_thresholdconvert_attributezoptimized.onnxZCPUExecutionProvider)Z providerszYONNX Runtime Model Optimization Failed! Consider rerun with option `--skip_optimization'.zonnx_shape_inferred.onnx)rrlocationrr)tempfileTemporaryDirectoryrloggerinforZ infer_shapesonnxloadstrZ save_modelsave onnxruntimeZSessionOptionsZoptimized_model_filepathZGraphOptimizationLevelZORT_ENABLE_BASICZgraph_optimization_levelZInferenceSession Exceptionerror traceback format_excZshape_inferenceZinfer_shapes_pathr)r r r r r rrrrrrrrZ quant_tmp_dirZ temp_pathmodelZopt_model_pathZ sess_optionsessZinferred_model_pathr*L/tmp/pip-unpacked-wheel-socb9apf/onnxruntime/quantization/shape_inference.pyquant_pre_processs)            r,) FFFFrFrFFNr)loggingrr&pathlibrtypingrrr#Z&onnxruntime.tools.symbolic_shape_inferrZ quant_utilsr getLogger__name__rr!boolintr,r*r*r*r+sH