g/ddlZddlZddlZddlZ ddlmZmZddl m Z ddl m Z m Z mZmZddlmZddlmZddlmZddlmZdd lmZdd lmZmZej<eej<d kr ed ed gdZde fdZ! dde dee de"fdZ#dee e e fde fdZ$dee e e fde de fdZ%dZ&ddZ'e(dk(re&yy#e$r ddlmZmZYwxYw)N)PackageNotFoundErrorversion)Path)AnyDictListOptional) load_dataset) evaluator)ORTModelForQuestionAnswering) __version__)r) AutoTokenizerpipelinez1.13.1z1Please install optimum>=1.13.1. Current version: .)z5bert-large-uncased-whole-word-masking-finetuned-squadzdeepset/roberta-base-squad2z%distilbert-base-cased-distilled-squad package_namec8 t|S#t$rYywxYwN)rr)rs d/var/www/openai/venv/lib/python3.12/site-packages/onnxruntime/transformers/models/bert/eval_squad.pyget_package_versionr7s$|$$ s model_id onnx_pathuse_io_bindingc|ztj|d||}tjj d|}|j |tjj |d}t d|||fStjtjj|t|j||}||fS)aLoad onnx model given pretrained model name and optional ONNX model path. If onnx_path is None, the default onnx model from optimum will be used. Args: model_id (str): pretrained model name or checkpoint path onnx_path (Optional[str], optional): path of onnx model to evaluate. Defaults to None. Returns: model: ORTModel for the onnx model onnx_path: the path of onnx model T)exportproviderrrz model.onnxzModel is exported to onnx file:) file_namerr) r from_pretrainedospathjoinsave_pretrainedprintdirnamername)rrrrmodel save_onnx_dirs rload_onnx_modelr'>s,<< TH^  S(3  m,GGLL =  /; ) -<< GGOOI &9o**)   ) results csv_filenamect|ddd5}gd}tj||}|j|D]}|j ||j dddt d|y#1swYxYw) zOutput a CSV file with detail of each test results. Args: results (List[Dict[str, Any]]): list of JSON results. csv_filename (str): path of output CSV file aasciimodenewlineencoding)pretrained_model_namerrdisable_fused_attention batch_sizesequence_lengthrexactf1total HasAns_exact HasAns_f1 HasAns_total best_exactbest_exact_threshbest_f1best_f1_threshtotal_time_in_secondssamples_per_secondlatency_in_seconds fieldnamesNz&Detail results are saved to csv file: )opencsv DictWriter writeheaderwriterowflushr")r)r*csv_file column_names csv_writerresults routput_detailsrPbs lb7 Cx .^^HF  F    ' ; D> 2<. AB? D Cs AA;;B metric_namec  t|ddd5}gd}t|Dchc]}|d c}}|jt|Dchc]}|d c}}|jt|Dchc]}|d c}}|jg} |D] } |D]} | jd | d | "t j ||| z } | j |D]} i}i}|j| Dcic]}|dc}|D]k}|d| k(s ||s|jDcic] \}}||vs ||}}}|s|j||d} |d} d | d | }|| vsd||||<m|s| D]}|j|d||<| j||jd d d td |d|y cc}wcc}wcc}wcc}wcc}}w#1swY5xYw)aOutput a CSV file with summary of a metric on combinations of batch_size and sequence_length. Args: results (List[Dict[str, Any]]): list of JSON results. csv_filename (str): path of output CSV file metric_name (str): the metric to summarize r,r-r.r/)r3rrr4rrr5r6b_srDNzSummary results for z are saved to csv file: ) rFlistsortappendrGrHrIupdateitemsgetrJrKr")r)r*rQrL header_namesrO model_list batch_sizessequence_lengths key_namesr6r5rNr%rowvalueskvheaderskeys routput_summaryrfsC lb7 Cx WEW66+.WEF wGwVF<0wGH  Qv(9!: QR /O)   1ZL?2C!DE* 0^^H 9QR  ECF MM)4)Q1b5)4 5!+&%/F;4G06 T1!|BSq!tGT 7+!' !5J&,->&?Oj\O+<=Ci'&,[&9s "$C%zz#r2CH%##C(1 4 i Dl  -El^ TU[FH!R"5UI D CsjG9 G G9 G$  G96 G) BG9 G. G9!G9'G9: G3 G3 ,G99 G9?G9G99HcPt}t|dD]}t|}|st|d|!|j}|jrAt j j|jstd|jt jjdddk(}g}tj|}|jD]'}||_t|dzd|_|j td t#j"}t%||j|j&|j(\} } t#j"|z } td | d d t| j*|| j*j,kDr td t/d| |d|j0} t3d} tdt#j"}t5d|j6dkDrd|j6dnd}t#j"|z } td| d d tdt#j"}| j9| |dd}t#j"|z } td| d d |j&|d<||d<||d<| |d <|j0|d!<||d"<|j(|d#<t||j;|*t=|d$d%D]}t?||d&|y)'N)zonnxruntime-gpu onnxruntimeonnxtorch transformersoptimumdatasetsevaluatez versionz Onnx model path does not exist: ORT_DISABLE_FUSED_ATTENTION01z4Exporting onnx model. It might take a few minutes...z!Onnx model exported or loaded in z.1fz secondszTsequence length should not be larger than {ort_model.config.max_position_embeddings}zquestion-answeringT)r% tokenizerquestion_firstr5zLoading dataset...squadrz validation[:] validation)splitzDataset loaded in zrs=2 A@,,!<:.0='*=-*=*=h*GG I/IZZ[\ ]]cns!!&sm!fj!H&CDc3h0&C&CR>VDc3h0>V>VRU>VBCGL3l zFQ A@@AsC C#"C#