:j( ddlZddlZddlZddlmZej eZdZedk(rVeZ e jr:e jr.e jr"ejdeje js.e jr"ejdejej!de j"ej!d e j$ee j"e j$e je je je j&e j(e j*e j,e je j.e j0e j2 yy) N)quant_pre_processctjd}|jddd|jddd|jd td d |jd td d |jdtd d |jdddd |jddtd|jdddd |jddtd|jdddd |jd d!dd |jd"d#d$|jd%d&td'|j S)(NaModel optimizer and shape inferencer, in preparation for quantization, Consists of three optional steps: 1. Symbolic shape inference (best for transformer models). 2. Model optimization. 3. ONNX shape inference. Model quantization with QDQ format, i.e. inserting QuantizeLinear/DeQuantizeLinear on the tensor, requires tensor shape information to perform its best. Currently, shape inferencing works best with optimized model. As a result, it is highly recommended to run quantization on optimized model with shape information. This is the tool for optimization and shape inferencing. Essentially this tool performs the following three (skippable) steps: 1. Symbolic shape inference. 2. Model optimization 3. ONNX shape inference) descriptionz--inputTzPath to the input model file)requiredhelpz--outputzPath to the output model filez--skip_optimizationFzSkip model optimization step if true. It's a known issue that ORT optimization has difficulty with model size greater than 2GB, rerun with this option to get around this issue.)typedefaultrz--skip_onnx_shapezSkip 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.z--skip_symbolic_shapezSkip 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.z --auto_mergez:Automatically merge symbolic dims when confliction happens store_true)ractionr z --int_maxzGmaximum value for integer to be treated as boundless for ops like slicei)rr r z--guess_output_rankz;guess output rank to be the same as input 0 for unknown opsz --verbosezHPrints detailed logs of inference, 0: turn off, 1: warnings, 3: detailedrz--save_as_external_dataz%Saving an ONNX model to external dataz--all_tensors_to_one_filez(Saving all the external data to one filez--external_data_locationz+The file location to save the external file)rr z--external_data_size_thresholdz$The size threshold for external datai)argparseArgumentParser add_argumentboolint parse_args)parsers x/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/onnxruntime/quantization/preprocess.pyparse_argumentsrs  $ $F(  D7UV  T8WX  1   '   4   I    V     J    W    ! 4   # 7   " :  ( 3      __main__z9Skipping all three steps, nothing to be done. Quitting...z:ORT model optimization does not support external data yet!zinput model: %szoutput model: %s)r loggingsysshape_inferencer getLogger__name__loggerrargsskip_optimizationskip_onnx_shapeskip_symbolic_shapeerrorexitsave_as_external_datainfoinputoutput auto_mergeint_maxguess_output_rankverboseall_tensors_to_one_fileexternal_data_locationexternal_data_size_thresholdrrr0s6 .   8 $`F z  D $"6"64;S;S PQ  " "(B(B QR  KK!4::. KK"DKK0            "" $$ ## ))r