o 3Ih(@sddlZddlZddlZddlmZeeZddZedkrpeZ e j r4e j r4e j r4e dee j sCe jrCe deed e jed e jee je je j e j e j e je je je je je je je j dSdS) N)quant_pre_processcCstjdd}|jdddd|jdddd|jd td d d |jd td dd |jdtd dd |jdddd d|jddtdd|jdddd d|jddtdd|jdddd d|jd d!dd d|jd"d#dd$|jd%d&td'd|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)ractionrz --int_maxzGmaximum value for integer to be treated as boundless for ops like slicei)rrrz--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)rrz--external_data_size_thresholdz$The size threshold for external datai)argparseArgumentParser add_argumentboolint parse_args)parserrc/home/air/sanwanet/gpt-api/venv/lib/python3.10/site-packages/onnxruntime/quantization/preprocess.pyparse_argumentss  r__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_thresholdrrrrs@  c