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It is a model path. :param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors. :param augmented_model_path: save augmented model to this path. :param use_external_data_format: use external data format to store model which size is >= 2Gb :param method: A string. One of ['entropy', 'percentile']. :param symmetric: make range of tensor symmetric (central point is 0). :param num_bins: number of bins to create a new histogram for collecting tensor values. :param num_quantized_bins: number of quantized bins. Default 128. :param percentile: A float number between [0, 100]. Default 99.99. :param scenario: see :class:`DistributionCalibrater` )rrrrNcSrrrrrrrr.rz/HistogramCalibrater.__init__..)rrCrrrerrrrr collectormethodnum_binsnum_quantized_binsr>rscenario) r?rrrrrDrrErFr>rGrrrrC s$ zHistogramCalibrater.__init__cCsV||j\|_}|jD]}||jvr|jjj||q tj|j|j |j ddS)z make all quantization_candidates op type nodes as part of the graph output. :return: augmented ONNX model rN) rrrrrrrrr rr)r?rr rrrr7s   z!HistogramCalibrater.augment_graphcCrrfrrGrrrrGrlz(HistogramCalibrater.clear_collected_datarc sBddjD}ddjD |}|sn-jd|}g}t|D]\}}||vr<|t|q)||q)j|qt jdkrTt dfd djD}i|D]} | D] \} } | g| qhqbfd d D} j stjjjjjjd _ j | dS) zy Entropy Calibrator collects operators' tensors as well as generates tensor histogram for each operator. cSrrrrLnode_argrrrrNrz3HistogramCalibrater.collect_data..cSsg|]}|jqSrrrHrrrr Orz4HistogramCalibrater.collect_data..TNrrcr!r"r%r'r(rrr er*cs i|] }|jvr||qSr)rr) merged_dictr?rrrMos z4HistogramCalibrater.collect_data..)rDrrErFr>rG)r get_inputsrrxr enumeratercopyrrer9r7r.rCHistogramCollectorrDrrErFr>rGcollectr) r?rinput_names_setrr fixed_outputs output_indexrr3r4rArBclean_merged_dictr)rJr)r?rrJsH      z HistogramCalibrater.collect_datarcCsh|jstdt|trtj}nt|trtj}nt|tr"tj }n t dt |dt ||j S)z Compute the min-max range of tensor :return: dictionary mapping: {tensor name: (min value, max value)} z9No collector created and can't generate calibration data.zUnknown calibrater z". This method must be overwritten.)rCr9r_EntropyCalibraterrcrtPercentileCalibraterruDistributionCalibraterrvrar<rXcompute_collection_result)r?calrrrr~s   z HistogramCalibrater.compute_data) NrFr>Fr?r@rArB)rPrTrUr`rrrCrrrwrrXrr<rrrrr= s& ,4r=csBeZdZ       d deeBdeedBffdd ZZS) rTNrFr)r?rrc  tj||||||||ddS)a :param model_path: ONNX model to calibrate. It is a model path :param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors. :param augmented_model_path: save augmented model to this path. :param use_external_data_format: use external data format to store model which size is >= 2Gb :param method: A string. One of ['entropy', 'percentile', 'distribution']. :param symmetric: make range of tensor symmetric (central point is 0). :param num_bins: number of bins to create a new histogram for collecting tensor values. :param num_quantized_bins: number of quantized bins. Default 128. )rDrrErFNrrC) r?rrrrrDrrErFrrrrC zEntropyCalibrater.__init__)NrFr)Fr?r?rPrTrUr`rrrCr<rrrrrT rTcsBeZdZ       d deeBdeedBffd d ZZS) rUNrFr>r@rArrc rY)a :param model_path: ONNX model to calibrate. It is a model path :param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors. :param augmented_model_path: save augmented model to this path. :param use_external_data_format: use external data format to store model which size is >= 2Gb :param method: A string. One of ['entropy', 'percentile', 'distribution']. :param symmetric: make range of tensor symmetric (central point is 0). :param num_quantized_bins: number of quantized bins. Default 128. :param percentile: A float number between [0, 100]. Default 99.99. )rDrrEr>NrZ) r?rrrrrDrrEr>rrrrCr[zPercentileCalibrater.__init__)NrFr>Fr@rAr\rrrrrUr]rUcs@eZdZ      d deeBdeedBffd d ZZS) rVNrF distributionr?rBrrc stj|||||||ddS)a :param model_path: ONNX model to calibrate. It is a model path :param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors. :param augmented_model_path: save augmented model to this path. :param use_external_data_format: use external data format to store model which size is >= 2Gb :param method: A string. One of ['entropy', 'percentile', 'distribution']. :param symmetric: make range of tensor symmetric (central point is 0). :param num_bins: number of bins to create a new histogram for collecting tensor values. :param scenario: for float 8 only, if `scenario="same"`, the algorithm weights and float 8 follow the same distribution, if `scenario="p3"`, it assumes the weights follow a gaussian law and float 8 ~ X^3 where X is a gaussian law )rDrErGNrZ)r?rrrrrDrErGrrrrCs zDistributionCalibrater.__init__)NrFr^r?rBr\rrrrrVs rVc@s,eZdZdZejddZejddZdS)CalibrationDataCollectorzL Base class for collecting data for calibration-based quantization. cCr~)z Generate informative data based on given data. name_to_arr : dict tensor name to NDArray data rr? name_to_arrrrrrOsz CalibrationDataCollector.collectcCr~)z? Get the optimal result among collection data. rrGrrrrWsz2CalibrationDataCollector.compute_collection_resultN)rPrTrU__doc__rrrOrWrrrrr_s  r_c@sveZdZdZddZddZddZdd Zd d Zd d Z ddZ ddZ ddZ e dddZddZddZdS)rNa` Collecting histogram for each tensor. 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Please run collect() first.z0Finding optimal threshold for each tensor using z algorithm ...r)r>r^re)rcrer9rfrDcompute_entropycompute_percentilecompute_distributionrGrrrrWs   z,HistogramCollector.compute_collection_resultcCs|jdks |jdkrtd|j}|j}i}tdt|td|jtdd|d|d |D]\}}|d}|d }|}t ||} |j rrt | |d} tj || |j d  tj || |j d f||<n*d|d } t | d | } t | | } tj || |j d tj || |j d f||<|d} |d}||d| kr| ||d f||<||d |kr||d|f||<g|||ddR||<tjdddvrt||q8|S)Nrdzr9rcrfrerEr7r%rcumsumr searchsortedrrosenvirongetr )r?rcr>thresholds_dictr rnr/r0totalcdf idx_rightpercent_to_cut_one_sideidx_leftrrrrrrsF   z%HistogramCollector.compute_percentilecCs|j}|j}i}tdt|td|jdtd|j|D]-\}}|||}|||<g||ddR||<tj dddvrSt |d|d q&|S) Nrrz: (The number may increase depends on the data it collects)zNumber of quantized bins : rYrrrr ) rcrFrfrerEr7get_entropy_thresholdrrrr )r?rcrFrr rnoptimal_thresholdrrrrs z"HistogramCollector.compute_entropyr cCs|dkr td|d|dd|ddd}|dkrJ|||}||d||dd}tj||jdtj||jdfSt||krt|ddkr||||}||||d|d}tj||jdtj||jdfSt||}d|t|<d|t|<t|||}|||}||d||dd}tj||jdtj||jdfS) Nrzpower=z <= 0 is invalid.rr g?rYr) r9r%rrrrr2isnanisinf)r/r0powerrrr+r,factrrr_avg_stds$$ $ $ zHistogramCollector._avg_stdc Cs:|jdkr td|j}i}tdt|td|jtd|jd|D]n\}}|d}|d}|jtj ks@J|jd krP|j ||dd \}}n|jd kr`|j ||d d \}}ntd |jtj kslJ|jtj kstJ|jtj ks|Jt ||||| | d||<tjdddvrt||q,|S)Niz3Invalid num_bins. Must be in range 512 <= num_bins.rrz Scenario : rrr rB)rp3gUUUUUU?z,Invalid scenario. Must be in {'same', 'p3'}.)r+r,r/r0r-r.rr)rEr9rcrfrerGr7rrrorr*rrrrrr ) r?rcrr rnr/r0avg_coefstd_coefrrrrs>     z'HistogramCollector.compute_distributioncs|d}|d}|j}|d}|d}|djt||d}fddt|jD} t||ddD]} || } t|| d|} || || f| | |<t|| | } | }t|d| }t|| d}|d|7<|d|7<|dk tj }tj|tj d}| j|}t|D]}||}||}t| ||||<q|dt| ||d7<tj|jtj d}t|D] }||}||}t|||}|dkr||||||<qt |}t |}|dus|durtj tj d}n tj t||d}||| |<q5t|}| |}|d}|d }|d|kr2||df}|d|kr?|d|f}t|dd sIJt|dd sSJ|S) aFGiven a dataset, find the optimal threshold for quantizing it. The reference distribution is `q`, and the candidate distribution is `p`. `q` is a truncated version of the original distribution. Ref: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf rr rYcs(g|]}tjddtjddfqS)rr)rrrrrrr Is(z.Nrrr\r)rlrrzerosrrrMdeepcopyr%r#rr rrr)argminr;)r?rnrFr/r0rEzero_bin_indexnum_half_quantized_bin kl_divergence thresholdsrrrsliced_distributionpleft_outliers_countright_outliers_countnonzerosquantized_binsnum_merged_binsindexstartendqnormdivmin_kl_divergence_idxrrrrrrr;sf        z(HistogramCollector.get_entropy_thresholdN)r )rPrTrUrbrCrdrOrhrgr}rWrr staticmethodrrrrrrrrNs :  .  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