o ZhN@sddlZddlmZddlmZmZddlmZddlm Z ddl m Z m Z ddl mZmZddlmZdd lmZmZmZe d Zgd ZGd d d eZdededefddZde fddZddZdededeeeeeeeffddZ d>dededede edef dd Z!ded!e"d"ed#e#fd$d%Z$ded!e"d"efd&d'Z%  ( )d?deded"ed!e"de ed#e#defd*d+Z&d,edefd-d.Z'd,edefd/d0Z(d1efd2d3Z)e*d4krmeZ+e+,Z-ee-j./e-_.zce0d5e&e-j1e-j2e-j.e-j3e-j4e-j5e-je-j(r"eee-j1d6kre0d7e0d8e'e-j.e-_6e(e-j6e-_7e-j8rHe0d9e)e-j.e9e-d:r:e)e-j6e9e-d;rKe)e-j7WdSWdSWdSe:ylZ;ze0d= than 2Gbz --quantizez/Quantize the neural network to be run with int8output)super__init__ add_argumentstrSUPPORTED_PIPELINESint)self __class__Q/var/www/auris/lib/python3.10/site-packages/transformers/convert_graph_to_onnx.pyr3sJ z$OnnxConverterArgumentParser.__init__)__name__ __module__ __qualname____doc__r __classcell__r%r%r#r&r.srfilename identifierreturncCs|j|j||jS)aE Append a string-identifier at the end (before the extension, if any) to the provided filepath Args: filename: pathlib.Path The actual path object we would like to add an identifier suffix identifier: The suffix to add Returns: String with concatenated identifier at the end of the filename )parentjoinpathstem with_suffixsuffix)r,r-r%r%r&generate_identified_filename\s r4minimum_versioncCsPzddl}t|j}|tkrtd|jd|dWdSty'tdw)z Check onnxruntime is installed and if the installed version match is recent enough Raises: ImportError: If onnxruntime is not installed or too old version is found rNz*We found an older version of onnxruntime (z&) but we require onnxruntime to be >= zp to enable all the conversions options. 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Attempt to infer the static vs dynamic axes for each input and output tensors for a specific model Args: nlp: The pipeline object holding the model to be exported framework: The framework identifier to dispatch to the correct inference scheme (pt/tf) Returns: - List of the inferred input variable names - List of the inferred output variable names - Dictionary with input/output variables names as key and shape tensor as value - a BatchEncoding reference which was used to infer all the above information nameis_inputseq_lencst|ttfrfdd|DSddt|jDddi}r:t|jdkr/d|d<n td t|jd fd dt|jD}|t |dt d rUd nddd||S)Ncsg|] }|qSr%r%).0t)build_shape_dictrKrJrLr%r& sz:infer_shapes..build_shape_dict..cSsg|] \}}|dkr|qS)r;r%)rMZaxisZnumelr%r%r&rPrbatchsequencer;zUnable to infer tensor axes ()csg|] \}}|kr|qSr%r%)rMdimshape)rLr%r&rPrQzFound inputr z with shape: ) isinstancerAlist enumeraterWlen ValueErrorupdatedictfromkeysr<)rJtensorrKrLZaxesZseq_axes)rO)rKrJrLr&rOs "z&infer_shapes..build_shape_dictzThis is a sample output)Zreturn_tensorsrc i|] \}}|||dqS)Tr%rMkvrOrLr%r&  z infer_shapes..cSsg|]}d|qS)Zoutput_r%)rMir%r%r&rPsz infer_shapes..crd)Fr%rerhr%r&rirjNr%)rboolr! tokenizerZ input_idsrWrBrZr Zto_tupler[rAkeysitemsextendr@ranger]zipr`) rHrIrCZoutputsZ input_varsZinput_dynamic_axesZ outputs_flatr output_namesZoutput_dynamic_axes dynamic_axesr%rhr& infer_shapess& "     ru pipeline_namerBrmcKs`|dur|}|dkrtstd|dkrtstdtd|d|dt|||||d S) a Convert the set of arguments provided through the CLI to an actual pipeline reference (tokenizer + model Args: pipeline_name: The kind of pipeline to use (ner, question-answering, etc.) framework: The actual model to convert the pipeline from ("pt" or "tf") model: The model name which will be loaded by the pipeline tokenizer: The tokenizer name which will be loaded by the pipeline, default to the model's value Returns: Pipeline object NrLCannot convert because PyTorch is not installed. 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Please install tensorflow first.zLoading pipeline (model: z , tokenizer: rU)rBrmrI model_kwargs)r Exceptionr r<r )rvrIrBrmZ models_kwargsr%r%r&load_graph_from_argssr{opsetruse_external_formatc Cststdddl}ddlm}td|j|*t|d\}}}} t |j | |\} } ||j | | | ||d|dWddS1sJwYdS) a Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR Args: nlp: The pipeline to be exported opset: The actual version of the ONNX operator set to use output: Path where will be stored the generated ONNX model use_external_format: Split the model definition from its parameters to allow model bigger than 2GB Returns: rwrN)exportzUsing framework PyTorch: rT)frDrsrtZdo_constant_foldingZ opset_version) rrztorchZ torch.onnxr~r<r7Zno_gradrurGrBas_posix) rHr|rr}rr~rDrsrtrCrFrEr%r%r&convert_pytorchs&   "rc ststdtdzGddlddl}ddlm}tdjjd|t|d\}}}}|j |j fd d | D} |j j|j | ||d \} } WdStym} ztd | jd | jd| d} ~ ww)av Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR) Args: nlp: The pipeline to be exported opset: The actual version of the ONNX operator set to use output: Path where will be stored the generated ONNX model Notes: TensorFlow cannot export model bigger than 2GB due to internal constraint from TensorFlow rxzD/!\ Please note TensorFlow doesn't support exporting model > 2Gb /!\rN)r7zUsing framework TensorFlow: z , tf2onnx: rcs g|] \}}jj||dqS))rJ)Z TensorSpecZ from_tensor)rMkeyrbrr%r&rPCrjz&convert_tensorflow..)r|Z output_pathzCannot import z6 required to convert TF model to ONNX. 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Please upgrade to onnxruntime >= 1.5.0.z1.13.1FT) rB per_channel reduce_rangemodestatic weight_qTypeZ input_qType tensors_rangenodes_to_quantizenodes_to_excludeop_types_to_quantize) rBrrrrrZactivation_qTyperrrrz -quantizedz$Quantized model has been written at r)onnxr6Z onnx.onnx_pbrZonnxruntime.quantizationrZ'onnxruntime.quantization.onnx_quantizerrZ!onnxruntime.quantization.registryrloadrrr7r<ZCopyFromZ IntegerOpsr[Zquantize_modelr4Z save_modelrB) rrr6rrrrZ onnx_modelZ copy_modelZ quantizerZquantized_model_pathr%r%r&quantizesZ       rpathc Csddlm}m}ddlm}td|dz|}|||dgd}td|d WdS|yI}ztd |d WYd}~dSd}~ww) Nrr)RuntimeExceptionz"Checking ONNX model loading from: z ...ZCPUExecutionProvider) providerszModel u correctly loaded: ✔zError while loading the model u: ✘)r6rrZ+onnxruntime.capi.onnxruntime_pybind11_staterr<r)rrrrZ onnx_optionsrrer%r%r&verifys r__main__z' ====== Converting model to ONNX ======raV Using TensorFlow might not provide the same optimization level compared to PyTorch. 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