vllm/lib/python3.10/site-packages/cupy/random/_kernels.py

"""
Random kit 1.3

Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)

The rk_random and rk_seed functions algorithms and the original design of
the Mersenne Twister RNG:

  Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
  All rights reserved.

  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions
  are met:

  1. Redistributions of source code must retain the above copyright
  notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright
  notice, this list of conditions and the following disclaimer in the
  documentation and/or other materials provided with the distribution.

  3. The names of its contributors may not be used to endorse or promote
  products derived from this software without specific prior written
  permission.

  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
  PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
  LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Original algorithm for the implementation of rk_interval function from
Richard J. Wagner's implementation of the Mersenne Twister RNG, optimised by
Magnus Jonsson.

Constants used in the rk_double implementation by Isaku Wada.

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""  # NOQA

from cupy import _core


rk_use_binominal = '''
#define CUPY_USE_BINOMIAL
'''

rk_basic_definition = '''
typedef struct {
    unsigned int xor128[4];
    double gauss;
    int has_gauss; // !=0: gauss contains a gaussian deviate

#ifdef CUPY_USE_BINOMIAL
    int has_binomial; // !=0: following parameters initialized for binomial
    /* The rk_state structure has been extended to store the following
     * information for the binomial generator. If the input values of n or p
     * are different than nsave and psave, then the other parameters will be
     * recomputed. RTK 2005-09-02 */
    int nsave, m;
    double psave, r, q, fm, p1, xm, xl, xr, c, laml, lamr, p2, p3, p4;
#endif
} rk_state;


__device__ void rk_seed(unsigned long long s, rk_state *state) {
    for (int i = 1; i <= 4; i++) {
        s = 1812433253U * (s ^ (s >> 30)) + i;
        state->xor128[i - 1] = s;
    }
    state->has_gauss = 0;
#ifdef CUPY_USE_BINOMIAL
    state->has_binomial = 0;
#endif
}


__device__ unsigned long rk_random(rk_state *state) {
    unsigned int *xor128 = state->xor128;
    unsigned int t = xor128[0] ^ (xor128[0] << 11);
    xor128[0] = xor128[1];
    xor128[1] = xor128[2];
    xor128[2] = xor128[3];
    return xor128[3] ^= (xor128[3] >> 19) ^ t ^ (t >> 8);
}


__device__ double rk_double(rk_state *state) {
    /* shifts : 67108864 = 0x4000000, 9007199254740992 = 0x20000000000000 */
    int a = rk_random(state) >> 5, b = rk_random(state) >> 6;
    return (a * 67108864.0 + b) / 9007199254740992.0;
}
'''


# The kernels for distributions are based on
# numpy/random/mtrand/distributions.c
# with the following licenses:
"""
/* Copyright 2005 Robert Kern (robert.kern@gmail.com)
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and associated documentation files (the
 * "Software"), to deal in the Software without restriction, including
 * without limitation the rights to use, copy, modify, merge, publish,
 * distribute, sublicense, and/or sell copies of the Software, and to
 * permit persons to whom the Software is furnished to do so, subject to
 * the following conditions:
 *
 * The above copyright notice and this permission notice shall be included
 * in all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
 * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
 * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
 * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
 * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

/* The implementations of rk_hypergeometric_hyp(), rk_hypergeometric_hrua(),
 * and rk_triangular() were adapted from Ivan Frohne's rv.py which has this
 * license:
 *
 *            Copyright 1998 by Ivan Frohne; Wasilla, Alaska, U.S.A.
 *                            All Rights Reserved
 *
 * Permission to use, copy, modify and distribute this software and its
 * documentation for any purpose, free of charge, is granted subject to the
 * following conditions:
 *   The above copyright notice and this permission notice shall be included in
 *   all copies or substantial portions of the software.
 *
 *   THE SOFTWARE AND DOCUMENTATION IS PROVIDED WITHOUT WARRANTY OF ANY KIND,
 *   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO MERCHANTABILITY, FITNESS
 *   FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE AUTHOR
 *   OR COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM OR DAMAGES IN A CONTRACT
 *   ACTION, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 *   SOFTWARE OR ITS DOCUMENTATION.
 */
"""  # NOQA

loggam_definition = '''
/*
 * log-gamma function to support some of these distributions. The
 * algorithm comes from SPECFUN by Shanjie Zhang and Jianming Jin and their
 * book "Computation of Special Functions", 1996, John Wiley & Sons, Inc.
 */
static __device__ double loggam(double x) {
    double x0, x2, xp, gl, gl0;
    long k, n;
    double a[10] = {8.333333333333333e-02,-2.777777777777778e-03,
         7.936507936507937e-04,-5.952380952380952e-04,
         8.417508417508418e-04,-1.917526917526918e-03,
         6.410256410256410e-03,-2.955065359477124e-02,
         1.796443723688307e-01,-1.39243221690590e+00};
    x0 = x;
    n = 0;
    if ((x == 1.0) || (x == 2.0)) {
        return 0.0;
    } else if (x <= 7.0) {
        n = (long)(7 - x);
        x0 = x + n;
    }
    x2 = 1.0/(x0*x0);
    xp = 2*M_PI;
    gl0 = a[9];
    for (k=8; k>=0; k--) {
        gl0 *= x2;
        gl0 += a[k];
    }
    gl = gl0/x0 + 0.5*log(xp) + (x0-0.5)*log(x0) - x0;
    if (x <= 7.0) {
        for (k=1; k<=n; k++) {
            gl -= log(x0-1.0);
            x0 -= 1.0;
        }
    }
    return gl;
}
'''

rk_standard_exponential_definition = '''
__device__ double rk_standard_exponential(rk_state *state) {
    /* We use -log(1-U) since U is [0, 1) */
    return -log(1.0 - rk_double(state));
}
'''

rk_standard_gamma_definition = '''
__device__ double rk_standard_gamma(rk_state *state, double shape) {
    double b, c;
    double U, V, X, Y;
    if (shape == 1.0) {
        return rk_standard_exponential(state);
    } else if (shape < 1.0) {
        for (;;) {
            U = rk_double(state);
            V = rk_standard_exponential(state);
            if (U <= 1.0 - shape) {
                X = pow(U, 1./shape);
                if (X <= V) {
                    return X;
                }
            } else {
                Y = -log((1-U)/shape);
                X = pow(1.0 - shape + shape*Y, 1./shape);
                if (X <= (V + Y)) {
                    return X;
                }
            }
        }
    } else {
        b = shape - 1./3.;
        c = 1./sqrt(9*b);
        for (;;) {
            do {
                X = rk_gauss(state);
                V = 1.0 + c*X;
            } while (V <= 0.0);
            V = V*V*V;
            U = rk_double(state);
            if (U < 1.0 - 0.0331*(X*X)*(X*X)) return (b*V);
            if (log(U) < 0.5*X*X + b*(1. - V + log(V))) return (b*V);
        }
    }
}
'''

rk_beta_definition = '''
__device__ double rk_beta(rk_state *state, double a, double b) {
    double Ga, Gb;
    if ((a <= 1.0) && (b <= 1.0)) {
        double U, V, X, Y;
        /* Use Johnk's algorithm */
        while (1) {
            U = rk_double(state);
            V = rk_double(state);
            X = pow(U, 1.0/a);
            Y = pow(V, 1.0/b);
            if ((X + Y) <= 1.0) {
                if (X +Y > 0) {
                    return X / (X + Y);
                } else {
                    double logX = log(U) / a;
                    double logY = log(V) / b;
                    double logM = logX > logY ? logX : logY;
                    logX -= logM;
                    logY -= logM;
                    return exp(logX - log(exp(logX) + exp(logY)));
                }
            }
        }
    } else {
        Ga = rk_standard_gamma(state, a);
        Gb = rk_standard_gamma(state, b);
        return Ga/(Ga + Gb);
    }
}
'''

rk_chisquare_definition = '''
__device__ double rk_chisquare(rk_state *state, double df) {
    return 2.0*rk_standard_gamma(state, df/2.0);
}
'''

rk_noncentral_chisquare_definition = '''
__device__ double rk_noncentral_chisquare(
    rk_state *state, double df, double nonc)
{
    if (nonc == 0){
        return rk_chisquare(state, df);
    }
    if(1 < df)
    {
        const double Chi2 = rk_chisquare(state, df - 1);
        const double N = rk_gauss(state) + sqrt(nonc);
        return Chi2 + N*N;
    }
    else
    {
        const long i = rk_poisson(state, nonc / 2.0);
        return rk_chisquare(state, df + 2 * i);
    }
}
'''

rk_f_definition = '''
__device__ double rk_f(rk_state *state, double dfnum, double dfden) {
    return ((rk_chisquare(state, dfnum) * dfden) /
            (rk_chisquare(state, dfden) * dfnum));
}
'''

rk_noncentral_f_definition = '''
__device__ double rk_noncentral_f(
    rk_state *state, double dfnum, double dfden, double nonc)
{
    double t = rk_noncentral_chisquare(state, dfnum, nonc) * dfden;
    return t / (rk_chisquare(state, dfden) * dfnum);
}
'''

rk_binomial_definition = '''
__device__ long rk_binomial_btpe(rk_state *state, long n, double p) {
    double r,q,fm,p1,xm,xl,xr,c,laml,lamr,p2,p3,p4;
    double a,u,v,s,F,rho,t,A,nrq,x1,x2,f1,f2,z,z2,w,w2,x;
    int m,y,k,i;
    if (!(state->has_binomial) ||
         (state->nsave != n) ||
         (state->psave != p)) {
        /* initialize */
        state->nsave = n;
        state->psave = p;
        state->has_binomial = 1;
        state->r = r = min(p, 1.0-p);
        state->q = q = 1.0 - r;
        state->fm = fm = n*r+r;
        state->m = m = (long)floor(state->fm);
        state->p1 = p1 = floor(2.195*sqrt(n*r*q)-4.6*q) + 0.5;
        state->xm = xm = m + 0.5;
        state->xl = xl = xm - p1;
        state->xr = xr = xm + p1;
        state->c = c = 0.134 + 20.5/(15.3 + m);
        a = (fm - xl)/(fm-xl*r);
        state->laml = laml = a*(1.0 + a/2.0);
        a = (xr - fm)/(xr*q);
        state->lamr = lamr = a*(1.0 + a/2.0);
        state->p2 = p2 = p1*(1.0 + 2.0*c);
        state->p3 = p3 = p2 + c/laml;
        state->p4 = p4 = p3 + c/lamr;
    } else {
        r = state->r;
        q = state->q;
        fm = state->fm;
        m = state->m;
        p1 = state->p1;
        xm = state->xm;
        xl = state->xl;
        xr = state->xr;
        c = state->c;
        laml = state->laml;
        lamr = state->lamr;
        p2 = state->p2;
        p3 = state->p3;
        p4 = state->p4;
    }
  /* sigh ... */
  Step10:
    nrq = n*r*q;
    u = rk_double(state)*p4;
    v = rk_double(state);
    if (u > p1) goto Step20;
    y = (long)floor(xm - p1*v + u);
    goto Step60;
  Step20:
    if (u > p2) goto Step30;
    x = xl + (u - p1)/c;
    v = v*c + 1.0 - fabs(m - x + 0.5)/p1;
    if (v > 1.0) goto Step10;
    y = (long)floor(x);
    goto Step50;
  Step30:
    if (u > p3) goto Step40;
    y = (long)floor(xl + log(v)/laml);
    if (y < 0) goto Step10;
    v = v*(u-p2)*laml;
    goto Step50;
  Step40:
    y = (long)floor(xr - log(v)/lamr);
    if (y > n) goto Step10;
    v = v*(u-p3)*lamr;
  Step50:
    k = labs(y - m);
    if ((k > 20) && (k < ((nrq)/2.0 - 1))) goto Step52;
    s = r/q;
    a = s*(n+1);
    F = 1.0;
    if (m < y) {
        for (i=m+1; i<=y; i++) {
            F *= (a/i - s);
        }
    } else if (m > y) {
        for (i=y+1; i<=m; i++) {
            F /= (a/i - s);
        }
    }
    if (v > F) goto Step10;
    goto Step60;
    Step52:
    rho = (k/(nrq))*((k*(k/3.0 + 0.625) + 0.16666666666666666)/nrq + 0.5);
    t = -k*k/(2*nrq);
    A = log(v);
    if (A < (t - rho)) goto Step60;
    if (A > (t + rho)) goto Step10;
    x1 = y+1;
    f1 = m+1;
    z = n+1-m;
    w = n-y+1;
    x2 = x1*x1;
    f2 = f1*f1;
    z2 = z*z;
    w2 = w*w;
    if (A > (xm*log(f1/x1)
           + (n-m+0.5)*log(z/w)
           + (y-m)*log(w*r/(x1*q))
           + (13680.-(462.-(132.-(99.-140./f2)/f2)/f2)/f2)/f1/166320.
           + (13680.-(462.-(132.-(99.-140./z2)/z2)/z2)/z2)/z/166320.
           + (13680.-(462.-(132.-(99.-140./x2)/x2)/x2)/x2)/x1/166320.
           + (13680.-(462.-(132.-(99.-140./w2)/w2)/w2)/w2)/w/166320.)) {
        goto Step10;
    }
  Step60:
    if (p > 0.5) {
        y = n - y;
    }
    return y;
}

__device__ long rk_binomial_inversion(rk_state *state, int n, double p) {
    double q, qn, np, px, U;
    int X, bound;
    if (!(state->has_binomial) ||
         (state->nsave != n) ||
         (state->psave != p)) {
        state->nsave = n;
        state->psave = p;
        state->has_binomial = 1;
        state->q = q = 1.0 - p;
        state->r = qn = exp(n * log(q));
        state->c = np = n*p;
        state->m = bound = min((double)n, np + 10.0*sqrt(np*q + 1));
    } else {
        q = state->q;
        qn = state->r;
        np = state->c;
        bound = state->m;
    }
    X = 0;
    px = qn;
    U = rk_double(state);
    while (U > px) {
        X++;
        if (X > bound) {
            X = 0;
            px = qn;
            U = rk_double(state);
        } else {
            U -= px;
            px  = ((n-X+1) * p * px)/(X*q);
        }
    }
    return X;
}

__device__ long rk_binomial(rk_state *state, int n, double p) {
    double q;
    if (p <= 0.5) {
        if (p*n <= 30.0) {
            return rk_binomial_inversion(state, n, p);
        } else {
            return rk_binomial_btpe(state, n, p);
        }
    } else {
        q = 1.0-p;
        if (q*n <= 30.0) {
            return n - rk_binomial_inversion(state, n, q);
        } else {
            return n - rk_binomial_btpe(state, n, q);
        }
    }
}
'''

rk_poisson_mult_definition = '''
__device__ long rk_poisson_mult(rk_state *state, double lam) {
    long X;
    double prod, U, enlam;
    enlam = exp(-lam);
    X = 0;
    prod = 1.0;
    while (1) {
        U = rk_double(state);
        prod *= U;
        if (prod > enlam) {
            X += 1;
        } else {
            return X;
        }
    }
}
'''

rk_poisson_ptrs_definition = '''
/*
 * The transformed rejection method for generating Poisson random variables
 * W. Hoermann
 * Insurance: Mathematics and Economics 12, 39-45 (1993)
 */
#define LS2PI 0.91893853320467267
#define TWELFTH 0.083333333333333333333333
__device__ long rk_poisson_ptrs(rk_state *state, double lam) {
    long k;
    double U, V, slam, loglam, a, b, invalpha, vr, us;
    slam = sqrt(lam);
    loglam = log(lam);
    b = 0.931 + 2.53*slam;
    a = -0.059 + 0.02483*b;
    invalpha = 1.1239 + 1.1328/(b-3.4);
    vr = 0.9277 - 3.6224/(b-2);
    while (1) {
        U = rk_double(state) - 0.5;
        V = rk_double(state);
        us = 0.5 - fabs(U);
        k = (long)floor((2*a/us + b)*U + lam + 0.43);
        if ((us >= 0.07) && (V <= vr)) {
            return k;
        }
        if ((k < 0) ||
            ((us < 0.013) && (V > us))) {
            continue;
        }
        if ((log(V) + log(invalpha) - log(a/(us*us)+b)) <=
            (-lam + k*loglam - loggam(k+1))) {
            return k;
        }
    }
}
'''

rk_poisson_definition = '''
__device__ long rk_poisson(rk_state *state, double lam) {
    if (lam >= 10) {
        return rk_poisson_ptrs(state, lam);
    } else if (lam == 0) {
        return 0;
    } else {
        return rk_poisson_mult(state, lam);
    }
}
'''

rk_standard_t_definition = '''
__device__ double rk_standard_t(rk_state *state, double df) {
    return sqrt(df/2)*rk_gauss(state)/sqrt(rk_standard_gamma(state, df/2));
}
'''

rk_vonmises_definition = '''
__device__ double rk_vonmises(rk_state *state, double mu, double kappa)
{
    double s;
    double U, V, W, Y, Z;
    double result, mod;
    int neg;

    if (kappa < 1e-8)
    {
        return M_PI * (2*rk_double(state)-1);
    }
    else
    {
        /* with double precision rho is zero until 1.4e-8 */
        if (kappa < 1e-5) {
            /*
             * second order taylor expansion around kappa = 0
             * precise until relatively large kappas as second order is 0
             */
            s = (1./kappa + kappa);
        }
        else {
            double r = 1 + sqrt(1 + 4*kappa*kappa);
            double rho = (r - sqrt(2*r)) / (2*kappa);
            s = (1 + rho*rho)/(2*rho);
        }

        while (1)
        {
        U = rk_double(state);
            Z = cos(M_PI*U);
            W = (1 + s*Z)/(s + Z);
            Y = kappa * (s - W);
            V = rk_double(state);
            if ((Y*(2-Y) - V >= 0) || (log(Y/V)+1 - Y >= 0))
            {
                break;
            }
        }

        U = rk_double(state);

        result = acos(W);
        if (U < 0.5)
        {
        result = -result;
        }
        result += mu;
        neg = (result < 0);
        mod = fabs(result);
        mod = (fmod(mod+M_PI, 2*M_PI)-M_PI);
        if (neg)
        {
            mod *= -1;
        }

        return mod;
    }
}
'''

rk_zipf_definition = '''
__device__ long rk_zipf(rk_state *state, double a)
{
    double am1, b;

    am1 = a - 1.0;
    b = pow(2.0, am1);
    while (1) {
        double T, U, V, X;

        U = 1.0 - rk_double(state);
        V = rk_double(state);
        X = floor(pow(U, -1.0/am1));

        if (X < 1.0) {
            continue;
        }

        T = pow(1.0 + 1.0/X, am1);
        if (V*X*(T - 1.0)/(b - 1.0) <= T/b) {
            return (long)X;
        }
    }
}
'''

rk_geometric_definition = '''
__device__ long rk_geometric_search(rk_state *state, double p) {
    double U;
    long X;
    double sum, prod, q;
    X = 1;
    sum = prod = p;
    q = 1.0 - p;
    U = rk_double(state);
    while (U > sum) {
        prod *= q;
        sum += prod;
        X++;
    }
    return X;
}

__device__ long rk_geometric_inversion(rk_state *state, double p) {
    return (long)ceil(log(1.0-rk_double(state))/log(1.0-p));
}

__device__ long rk_geometric(rk_state *state, double p) {
    if (p >= 0.333333333333333333333333) {
        return rk_geometric_search(state, p);
    } else {
        return rk_geometric_inversion(state, p);
    }
}
'''

# min and max for the long type are not defined in cuda90 but in cuda75.
long_min_max_definition = '''
__device__ long long_min(long a, long b)
{
    return a < b ? a : b;
}

__device__ long long_max(long a, long b)
{
    return a > b ? a : b;
}
'''

rk_hypergeometric_definition = '''
__device__ long rk_hypergeometric_hyp(
    rk_state *state, long good, long bad, long sample)
{
    long d1, K, Z;
    double d2, U, Y;

    d1 = bad + good - sample;
    d2 = (double)long_min(bad, good);

    Y = d2;
    K = sample;
    while (Y > 0.0)
    {
        U = rk_double(state);
        Y -= (long)floor(U + Y/(d1 + K));
        K--;
        if (K == 0) break;
    }
    Z = (long)(d2 - Y);
    if (good > bad) Z = sample - Z;
    return Z;
}

/* D1 = 2*sqrt(2/e) */
/* D2 = 3 - 2*sqrt(3/e) */
#define D1 1.7155277699214135
#define D2 0.8989161620588988
__device__ long rk_hypergeometric_hrua(
    rk_state *state, long good, long bad, long sample)
{
    long mingoodbad, maxgoodbad, popsize, m, d9;
    double d4, d5, d6, d7, d8, d10, d11;
    long Z;
    double T, W, X, Y;

    mingoodbad = long_min(good, bad);
    popsize = good + bad;
    maxgoodbad = long_max(good, bad);
    m = long_min(sample, popsize - sample);
    d4 = ((double)mingoodbad) / popsize;
    d5 = 1.0 - d4;
    d6 = m*d4 + 0.5;
    d7 = sqrt((double)(popsize - m) * sample * d4 * d5 / (popsize - 1) + 0.5);
    d8 = D1*d7 + D2;
    d9 = (long)floor((double)(m + 1) * (mingoodbad + 1) / (popsize + 2));
    d10 = (loggam(d9+1) + loggam(mingoodbad-d9+1) + loggam(m-d9+1) +
           loggam(maxgoodbad-m+d9+1));
    d11 = min(long_min(m, mingoodbad)+1.0, floor(d6+16*d7));
    /* 16 for 16-decimal-digit precision in D1 and D2 */

    while (1)
    {
        X = rk_double(state);
        Y = rk_double(state);
        W = d6 + d8*(Y- 0.5)/X;

        /* fast rejection: */
        if ((W < 0.0) || (W >= d11)) continue;

        Z = (long)floor(W);
        T = d10 - (loggam(Z+1) + loggam(mingoodbad-Z+1) + loggam(m-Z+1) +
                   loggam(maxgoodbad-m+Z+1));

        /* fast acceptance: */
        if ((X*(4.0-X)-3.0) <= T) break;

        /* fast rejection: */
        if (X*(X-T) >= 1) continue;

        if (2.0*log(X) <= T) break;  /* acceptance */
    }

    /* this is a correction to HRUA* by Ivan Frohne in rv.py */
    if (good > bad) Z = m - Z;

    /* another fix from rv.py to allow sample to exceed popsize/2 */
    if (m < sample) Z = good - Z;

    return Z;
}
#undef D1
#undef D2

__device__ long rk_hypergeometric(
    rk_state *state, long good, long bad, long sample)
{
    if (sample > 10)
    {
        return rk_hypergeometric_hrua(state, good, bad, sample);
    } else
    {
        return rk_hypergeometric_hyp(state, good, bad, sample);
    }
}
'''

rk_logseries_definition = '''
__device__ long rk_logseries(rk_state *state, double p)
{
    double q, r, U, V;
    long result;

    r = log(1.0 - p);

    while (1) {
        V = rk_double(state);
        if (V >= p) {
            return 1;
        }
        U = rk_double(state);
        q = 1.0 - exp(r*U);
        if (V <= q*q) {
            result = (long)floor(1 + log(V)/log(q));
            if (result < 1) {
                continue;
            }
            else {
                return result;
            }
        }
        if (V >= q) {
            return 1;
        }
        return 2;
    }
}
'''

rk_gauss_definition = '''
__device__ double rk_gauss(rk_state *state) {
    if (state->has_gauss) {
        const double tmp = state->gauss;
        state->gauss = 0;
        state->has_gauss = 0;
        return tmp;
    } else {
        double f, x1, x2, r2;
        do {
            x1 = 2.0*rk_double(state) - 1.0;
            x2 = 2.0*rk_double(state) - 1.0;
            r2 = x1*x1 + x2*x2;
        }
        while (r2 >= 1.0 || r2 == 0.0);
        /* Box-Muller transform */
        f = sqrt(-2.0*log(r2)/r2);
        /* Keep for next call */
        state->gauss = f*x1;
        state->has_gauss = 1;
        return f*x2;
    }
}
'''

open_uniform_definition = '''
__device__ void open_uniform(rk_state *state, double *U) {
    do {
        *U = rk_double(state);
    } while (*U <= 0.0 || *U >= 1.0);
}

__device__ void open_uniform(rk_state *state, float *U) {
    do {
        *U = rk_double(state);
    } while (*U <= 0.0 || *U >= 1.0);
}
'''

definitions = [
    rk_basic_definition, rk_gauss_definition,
    rk_standard_exponential_definition, rk_standard_gamma_definition,
    rk_beta_definition]
beta_kernel = _core.ElementwiseKernel(
    'S a, T b, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_beta(&internal_state, a, b);
    ''',
    'cupy_beta_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_use_binominal, rk_basic_definition, rk_binomial_definition]
binomial_kernel = _core.ElementwiseKernel(
    'S n, T p, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_binomial(&internal_state, n, p);
    ''',
    'cupy_binomial_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, rk_gauss_definition,
     rk_standard_exponential_definition, rk_standard_gamma_definition,
     rk_standard_t_definition]
standard_t_kernel = _core.ElementwiseKernel(
    'S df, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_standard_t(&internal_state, df);
    ''',
    'cupy_standard_t_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, rk_gauss_definition,
     rk_standard_exponential_definition, rk_standard_gamma_definition,
     rk_chisquare_definition]
chisquare_kernel = _core.ElementwiseKernel(
    'T df, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_chisquare(&internal_state, df);
    ''',
    'cupy_chisquare_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, rk_gauss_definition,
     rk_standard_exponential_definition, rk_standard_gamma_definition,
     rk_chisquare_definition, rk_f_definition]
f_kernel = _core.ElementwiseKernel(
    'S dfnum, T dfden, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_f(&internal_state, dfnum, dfden);
    ''',
    'cupy_f_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, rk_geometric_definition]
geometric_kernel = _core.ElementwiseKernel(
    'T p, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_geometric(&internal_state, p);
    ''',
    'cupy_geometric_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, loggam_definition, long_min_max_definition,
     rk_hypergeometric_definition]
hypergeometric_kernel = _core.ElementwiseKernel(
    'S good, T bad, U sample, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_hypergeometric(&internal_state, good, bad, sample);
    ''',
    'cupy_hypergeometric_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, rk_logseries_definition]
logseries_kernel = _core.ElementwiseKernel(
    'T p, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_logseries(&internal_state, p);
    ''',
    'cupy_logseries_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, loggam_definition, rk_gauss_definition,
    rk_standard_exponential_definition, rk_standard_gamma_definition,
    rk_chisquare_definition, rk_poisson_mult_definition,
    rk_poisson_ptrs_definition, rk_poisson_definition,
    rk_noncentral_chisquare_definition]
noncentral_chisquare_kernel = _core.ElementwiseKernel(
    'S df, T nonc, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_noncentral_chisquare(&internal_state, df, nonc);
    ''',
    'cupy_noncentral_chisquare_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, loggam_definition, rk_gauss_definition,
    rk_standard_exponential_definition, rk_standard_gamma_definition,
    rk_chisquare_definition, rk_poisson_mult_definition,
    rk_poisson_ptrs_definition, rk_poisson_definition,
    rk_noncentral_chisquare_definition, rk_noncentral_f_definition]
noncentral_f_kernel = _core.ElementwiseKernel(
    'S dfnum, T dfden, U nonc, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_noncentral_f(&internal_state, dfnum, dfden, nonc);
    ''',
    'cupy_noncentral_f_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = \
    [rk_basic_definition, loggam_definition,
     rk_poisson_mult_definition, rk_poisson_ptrs_definition,
     rk_poisson_definition]
poisson_kernel = _core.ElementwiseKernel(
    'T lam, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_poisson(&internal_state, lam);
    ''',
    'cupy_poisson_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, rk_gauss_definition,
    rk_standard_exponential_definition, rk_standard_gamma_definition]
standard_gamma_kernel = _core.ElementwiseKernel(
    'T shape, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_standard_gamma(&internal_state, shape);
    ''',
    'cupy_standard_gamma_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, rk_vonmises_definition]
vonmises_kernel = _core.ElementwiseKernel(
    'S mu, T kappa, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_vonmises(&internal_state, mu, kappa);
    ''',
    'cupy_vonmises_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, rk_zipf_definition]
zipf_kernel = _core.ElementwiseKernel(
    'T a, uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    y = rk_zipf(&internal_state, a);
    ''',
    'cupy_zipf_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)

definitions = [
    rk_basic_definition, open_uniform_definition]
open_uniform_kernel = _core.ElementwiseKernel(
    'uint64 seed', 'Y y',
    '''
    rk_seed(seed + i, &internal_state);
    open_uniform(&internal_state, &y);
    ''',
    'cupy_open_uniform_kernel',
    preamble=''.join(definitions),
    loop_prep='rk_state internal_state;'
)