o W۷iX@sddlmZddlZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddl m Z e Zedd d d Zed d ddZddZddZddZd)ddZd*ddZd+ddZeddd d!Zed"dd#d$Zd,d%d&Zd-d'd(ZdS).) annotationsN)_core) _accelerator)cub)common)runtimezS x, raw T bins, int32 n_binszraw U yaD if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], U(1)); cupy_histogram_kernelz,S x, raw T bins, int32 n_bins, raw W weightszraw Y yaM if (x < bins[0] or bins[n_bins - 1] < x) { return; } int high = n_bins - 1; int low = 0; while (high - low > 1) { int mid = (high + low) / 2; if (bins[mid] <= x) { low = mid; } else { high = mid; } } atomicAdd(&y[low], (Y)weights[i]); cupy_weighted_histogram_kernelcCs||jtjkrtjd|jtjtdd|tj}|dur6t |tj s(t d|j |j kr2t d| }| }||fS)z: Check a and weights have matching shapes, and ravel both z1Converting input from {} to {} for compatibility.) stacklevelNzweights must be a cupy.ndarrayz(weights should have the same shape as a.)dtypecupybool_warningswarnformatuint8RuntimeWarningastype isinstancendarray ValueErrorshaperavel)aweightsrP/home/ubuntu/vllm_env/lib/python3.10/site-packages/cupy/_statistics/histogram.py_ravel_and_check_weightsCs     rcCs|dur#|\}}||krtdt|rt|s"td||n$|jdkr-d}d}n|}|}t|r?t|sGtd||||krS|d}|d}||fS)z^ Determine the outer bin edges to use, from either the data or the range argument Nz/max must be larger than min in range parameter.z(supplied range of [{}, {}] is not finiterz,autodetected range of [{}, {}] is not finiteg?)rnumpyisfinitersizeminmaxr )rrange first_edge last_edgerrr_get_outer_edgesXs8 r(cCs4d}d}t|tr tdt|tjst|dkrFt|tjr#|}nt|}|dd|ddkr:t dt|tjrEt|}n.t|dkrpzt |}Wn t y_t dw|dkrht dt ||\}}nt d |durt|||}t|tjrt|t}tj|||dd |d }|S) a5 Computes the bins used internally by `histogram`. Args: a (ndarray): Ravelled data array bins (int or ndarray): Forwarded argument from `histogram`. range (None or tuple): Forwarded argument from `histogram`. Returns: bin_edges (ndarray): Array of bin edges Nz+only integer and array bins are implementedrz1`bins` must increase monotonically, when an arrayrz0`bins` must be an integer, a string, or an arrayz(`bins` must be positive, when an integerz `bins` must be 1d, when an arrayT)endpointr )rstrNotImplementedErrorr rr ndimasarrayanyroperatorindex TypeErrorr( result_type issubdtypeintegerfloatlinspace)rbinsr% n_equal_bins bin_edgesr&r'bin_typerrr_get_bin_edgesysL         r< FcCs|jjdkr tdt|tjstdt||\}}t|||}|durtj |j dtj d}t D]}|t jkr|j dkr|j dkrt|tjsMJt|jtjrXt}nt|j|j}|tjkrltsltj}|j|dd }|j|d d } |jjd vr| d d7<n|jjd kr| d } t| | d| d <tjr|jtjdd }t||| } | durq4| }tjr|jtj dd }n q4t|||j |net |jtj!pt |jtj"} | std|jjdkr tj |j dtj"d}t#|||j |j$|j$t#|||j |j%|j%n&|jjdvrtj |j dt&d}n tj |j dtj!d}t#|||j |||rIt't(|tj!} || |)|fS||fS)a3Computes the histogram of a set of data. Args: x (cupy.ndarray): Input array. bins (int or cupy.ndarray): If ``bins`` is an int, it represents the number of bins. If ``bins`` is an :class:`~cupy.ndarray`, it represents a bin edges. range (2-tuple of float, optional): The lower and upper range of the bins. If not provided, range is simply ``(x.min(), x.max())``. Values outside the range are ignored. The first element of the range must be less than or equal to the second. `range` affects the automatic bin computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. density (bool, optional): If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. weights (cupy.ndarray, optional): An array of weights, of the same shape as `x`. Each value in `x` only contributes its associated weight towards the bin count (instead of 1). Returns: tuple: ``(hist, bin_edges)`` where ``hist`` is a :class:`cupy.ndarray` storing the values of the histogram, and ``bin_edges`` is a :class:`cupy.ndarray` storing the bin edges. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogram` czcomplex number is not supportedzx must be a cupy.ndarrayNrr F)copyTuir)fzJonly weights with dtype that can be cast to float or complex are supportedbui)*r kindr,rr rrrr<zerosr"int64rget_routine_acceleratorsACCELERATOR_CUBr r4r5r6r3float16r_is_fp16_supportedfloat32r nextafterris_hipuint64r cub_histogram_histogram_kernelcan_castfloat64 complex128_weighted_histogram_kernelrealimagintarraydiffsum)xr8r%densityrr:y acceleratorr; acc_bin_edgelastoutsimple_weightsdbrrr histograms~ !        recsttjrjdkrddtjfj\}}n tjddj\}}t|t }|dg|dg}|dur?t |}zt |} | |krLt dWnt y[||g}Ynw|dured|}n t ||krot dt|D]} t|| dkr|| dkrt d | tdd| f|| \} } t || d} t| | | | <ns  zhistogramdd..) minlengthzInternal Shape Error)rr rr-newaxisrstackr emptyrXr.lenrr2_rangerr(r7r/rZtupleravel_multi_indexbincountprodreshaperr6slicer[ RuntimeError)rnr8r%rr]nsamplesr-nbindedgesnbinsrksminsmaxnumncounton_edgexyhistcoresrrrlr histogramdd$s 2             $      rc Cszt|}Wn tyd}Ynw|dkr-|dkr-t|tjr)|}}||g}ntdt||g||||\} } | | d| dfS)aCompute the bi-dimensional histogram of two data samples. Args: x (cupy.ndarray): The first array of samples to be histogrammed. y (cupy.ndarray): The second array of samples to be histogrammed. bins (int or tuple of int or cupy.ndarray): The bin specification: * A sequence of arrays describing the monotonically increasing bin edges along each dimension. * The number of bins for each dimension (nx, ny) * The number of bins for all dimensions (nx=ny=bins). range (sequence, optional): A sequence of length two, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in `bins`. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of two None values. weights (cupy.ndarray): An array of values `w_i` weighing each sample `(x_i, y_i)`. The values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. density (bool, optional): If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. Returns: tuple: H (cupy.ndarray): The multidimensional histogram of sample x. See normed and weights for the different possible semantics. edges0 (tuple of cupy.ndarray): A list of D arrays describing the bin edges for the first dimension. edges1 (tuple of cupy.ndarray): A list of D arrays describing the bin edges for the second dimension. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.histogram2d` rrqz%array-like bins not supported in CuPyr)rur2rr rrr) r\r^r8r%rr]nxedgesyedgesrrmrrr histogram2ds+    rzS xz raw U binzatomicAdd(&bin[x], U(1))cupy_bincount_kernelzS x, T wzatomicAdd(&bin[x], w) cupy_bincount_with_weight_kernelcCsL|jdkr td|jdkrtd|jjdkrtd|dkr&td|dur4|j|jkr4td |durDt|}|dkrDtd tt |d}|durVt ||}|durtj |ft j d }t D]&}tjs|t jkr|jd kr|d krt|||d}|durqg|}|Sqgt|||Stj |ft jd }t||||S) a,Count number of occurrences of each value in array of non-negative ints. Args: x (cupy.ndarray): Input array. weights (cupy.ndarray): Weights array which has the same shape as ``x``. minlength (int): A minimum number of bins for the output array. Returns: cupy.ndarray: The result of binning the input array. The length of output is equal to ``max(cupy.max(x) + 1, minlength)``. .. warning:: This function may synchronize the device. .. seealso:: :func:`numpy.bincount` r!object too deep for desired array+object of too small depth for desired arrayrCzx must be int arrayrz3The first argument of bincount must be non-negativeNz0The weights and list don't have the same length.zminlength must be non-negativer?r@)r-rr rEr2r/rrXr r$rFr intprrHrrNrIr"rrP_bincount_kernelrS_bincount_with_weight_kernel)r\rrpr"br_rbrrrrysF         rycCsZ|jjdkr td|jdkrtd|jdkrtd|r dnd}tjj|||dd S) a~Finds the indices of the bins to which each value in input array belongs. .. note:: In order to avoid device synchronization, digitize does not raise an exception when the array is not monotonic Args: x (cupy.ndarray): Input array. bins (cupy.ndarray): Array of bins. It has to be 1-dimensional and monotonic increasing or decreasing. right (bool): Indicates whether the intervals include the right or the left bin edge. Returns: cupy.ndarray: Output array of indices, of same shape as ``x``. .. seealso:: :func:`numpy.digitize` r>zx may not be complexrrrleftrgNF) r rEr2r-rr _sortingsearch _searchsorted)r\r8rgrhrrrdigitizeGs    r)r=NFN)r=NNF)r=NNN)NN)F) __future__rr0rr r r cupy._corer cupy.cudarrrr%rvElementwiseKernelrQrUrr(r<rerrrrryrrrrrsT      ! ; p ; ?