saad
/
ai-content-maker
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
ai-content-maker/.venv/Lib/site-packages/torch/include/ATen/native/Distributions.h

519 lines
21 KiB
C++
Raw Blame History

#pragma once
#include <ATen/native/Math.h>
#include <c10/macros/Macros.h>
#include <c10/util/MathConstants.h>
// ROCM hcc doesn't work well with using std:: in kernel functions
#if defined(__CUDA_ARCH__)
#include <c10/cuda/CUDAMathCompat.h>
#define compat_exp c10::cuda::compat::exp
#define compat_ceil c10::cuda::compat::ceil
#define compat_floor c10::cuda::compat::floor
#define compat_log c10::cuda::compat::log
#define compat_pow c10::cuda::compat::pow
#define compat_sqrt c10::cuda::compat::sqrt
#define compat_tan c10::cuda::compat::tan
#define compat_abs c10::cuda::compat::abs
#define compat_log1p c10::cuda::compat::log1p
#elif defined(__HIPCC__)
#include <c10/hip/HIPMathCompat.h>
#define compat_exp c10::hip::compat::exp
#define compat_ceil c10::hip::compat::ceil
#define compat_floor c10::hip::compat::floor
#define compat_log c10::hip::compat::log
#define compat_pow c10::hip::compat::pow
#define compat_sqrt c10::hip::compat::sqrt
#define compat_tan c10::hip::compat::tan
#define compat_abs c10::hip::compat::abs
#define compat_log1p c10::hip::compat::log1p
#else
#define compat_exp std::exp
#define compat_ceil std::ceil
#define compat_floor std::floor
#define compat_log std::log
#define compat_pow std::pow
#define compat_sqrt std::sqrt
#define compat_tan std::tan
#define compat_abs std::abs
#define compat_log1p std::log1p
#endif
namespace {
#if !defined(__CUDA_ARCH__) && !defined(__HIPCC__)
// we cannot use std::isnan directly due to some incompatibility of
// gcc constexpr'ing and nvcc
using std::isnan;
#endif
// Here sampler_t should be function type scalar_t(void). For gpu
// "sampler" is a device function, but since ROCM doesn't have
// equivalent to nvstd::function, we use a template type parameter to
// capture it.
template<typename scalar_t, typename sampler_t>
struct BaseSampler {
sampler_t sampler;
C10_DEVICE BaseSampler(const sampler_t& sampler): sampler(sampler) {}
C10_DEVICE scalar_t sample() {
return sampler();
}
};
// The function `sample_gamma` is
// is adapted from Numpy's distributions.c implementation.
// It is MIT licensed, so here is the copyright:
/* 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.
*/
template<typename scalar_t, typename accscalar_t, typename uniform_sampler_t, typename normal_sampler_t>
C10_DEVICE scalar_t sample_gamma(scalar_t alpha, BaseSampler<accscalar_t, uniform_sampler_t>& standard_uniform, BaseSampler<accscalar_t, normal_sampler_t>& standard_normal) {
accscalar_t scale = 1.0f;
// Boost alpha for higher acceptance probability.
if (alpha < 1.0f) {
if (alpha == 0.f) return 0.f;
scale *= compat_pow(1 - standard_uniform.sample(), 1.0f / alpha);
alpha += 1.0f;
}
// This implements the acceptance-rejection method of Marsaglia and Tsang (2000)
// doi:10.1145/358407.358414
const accscalar_t d = alpha - 1.0f / 3.0f;
const accscalar_t c = 1.0f / compat_sqrt(9.0f * d);
for (;;) {
accscalar_t x, y;
do {
x = standard_normal.sample();
y = 1.0f + c * x;
} while (y <= 0);
const accscalar_t v = y * y * y;
const accscalar_t u = 1 - standard_uniform.sample();
const accscalar_t xx = x * x;
if (u < 1.0f - 0.0331f * xx * xx)
return static_cast<scalar_t>(scale * d * v);
if (compat_log(u) < 0.5f * xx + d * (1.0f - v + compat_log(v)))
return static_cast<scalar_t>(scale * d * v);
}
}
/* the functions stirling_approx_tail, binomial_inversion, and btrs are adapted
* from TensorFlow's random_binomial_op.cc implementation. That code is under
* copyright: 2019 The TensorFlow Authors.
*
* It was released under the Apache License, Version 2.0 (the "License"), available at:
* http://www.apache.org/licenses/LICENSE-2.0
*/
template<typename scalar_t>
C10_DEVICE scalar_t stirling_approx_tail(scalar_t k) {
const static scalar_t kTailValues[] = {
0.0810614667953272,
0.0413406959554092,
0.0276779256849983,
0.02079067210376509,
0.0166446911898211,
0.0138761288230707,
0.0118967099458917,
0.0104112652619720,
0.00925546218271273,
0.00833056343336287
};
if (k <= 9) {
return kTailValues[static_cast<size_t>(k)];
}
scalar_t kp1sq = (k + 1) * (k + 1);
return (1.0 / 12 - (1.0 / 360 - 1.0 / 1260 / kp1sq) / kp1sq) / (k + 1);
}
template<typename scalar_t, typename accscalar_t, typename uniform_sampler_t>
C10_DEVICE scalar_t binomial_inversion(scalar_t count, scalar_t prob, BaseSampler<accscalar_t, uniform_sampler_t>& standard_uniform) {
accscalar_t U;
accscalar_t geom_sum = 0;
scalar_t num_geom = 0;
accscalar_t logprob = compat_log1p(-prob);
while (1) {
U = standard_uniform.sample();
accscalar_t geom = compat_ceil(compat_log(U) / logprob);
geom_sum += geom;
if (geom_sum > count) {
break;
}
num_geom = num_geom + 1;
}
return num_geom;
}
template<typename scalar_t, typename accscalar_t, typename uniform_sampler_t>
C10_DEVICE scalar_t btrs(scalar_t count, scalar_t prob, BaseSampler<accscalar_t, uniform_sampler_t>& standard_uniform) {
scalar_t k;
accscalar_t U, V, us;
// This is spq in the paper.
const accscalar_t stddev = compat_sqrt(count * prob * (1 - prob));
// Other coefficients for Transformed Rejection sampling.
const accscalar_t b = 1.15 + 2.53 * stddev;
const accscalar_t a = -0.0873 + 0.0248 * b + 0.01 * prob;
const accscalar_t c = count * prob + 0.5;
const accscalar_t v_r = 0.92 - 4.2 / b;
const accscalar_t r = prob / (1 - prob);
const accscalar_t alpha = (2.83 + 5.1 / b) * stddev;
const accscalar_t m = compat_floor((count + 1) * prob);
while (1) {
U = standard_uniform.sample() - 0.5;
V = standard_uniform.sample();
us = 0.5 - compat_abs(U);
k = static_cast<scalar_t>(compat_floor((2 * a / us + b) * U + c));
// Reject non-sensical answers.
if (k < 0 || k > count) {
continue;
}
// Region for which the box is tight, and we can return our calculated value.
// This should happen 0.86 * v_r times. In the limit as n * p is large,
// the acceptance rate converges to ~79% (and in the lower regime it is ~24%).
if (us >= 0.07 && V <= v_r) {
return k;
}
// This deviates from Hormann's BTRS algorithm, as there is a log missing.
// For all (u, v) pairs outside of the bounding box, this calculates the
// transformed-reject ratio.
V = compat_log(V * alpha / (a / (us * us) + b));
accscalar_t upperbound =
((m + 0.5) * compat_log((m + 1) / (r * (count - m + 1))) +
(count + 1) * compat_log((count - m + 1) / (count - k + 1)) +
(k + 0.5) * compat_log(r * (count - k + 1) / (k + 1)) +
stirling_approx_tail<accscalar_t>(m) + stirling_approx_tail<accscalar_t>(count - m) -
stirling_approx_tail<accscalar_t>(k) - stirling_approx_tail<accscalar_t>(count - k));
if (V <= upperbound) {
return k;
}
}
}
template<typename scalar_t, typename accscalar_t, typename uniform_sampler_t>
C10_DEVICE scalar_t sample_binomial(scalar_t count, scalar_t prob, BaseSampler<accscalar_t, uniform_sampler_t>& standard_uniform) {
if (count <= 0.0 || prob <= 0.0) {
return 0;
} else if (prob >= 1.0) {
return count;
} else if (prob <= 0.5) {
if (count * prob >= 10.0) {
// btrs
return btrs<scalar_t, accscalar_t, uniform_sampler_t>(count, prob, standard_uniform);
} else {
// binomial inversion
return binomial_inversion<scalar_t, accscalar_t, uniform_sampler_t>(count, prob, standard_uniform);
}
} else if (prob > 0.5) {
scalar_t qprob = 1.0 - prob;
if (count * qprob >= 10.0) {
// btrs
return count - btrs<scalar_t, accscalar_t, uniform_sampler_t>(count, qprob, standard_uniform);
} else {
// count - binomial inversion
return count - binomial_inversion<scalar_t, accscalar_t, uniform_sampler_t>(count, qprob, standard_uniform);
}
} else {
// prob is nan?
return static_cast<scalar_t>(NAN);
}
}
/*
* This function is derived from the implementation of the digamma function in the Cephes Math Library.
* See note [3-Clause BSD License for the Cephes Math Library] in ATen/native/Math.h.
*/
template<typename scalar_t, typename accscalar_t>
C10_DEVICE static inline scalar_t digamma_one(scalar_t x) {
constexpr accscalar_t PSI_10 = 2.25175258906672110764;
if (x == 0) {
return INFINITY;
}
accscalar_t additional_summand = 0;
int x_is_integer = x == compat_floor(x);
if (x < 0) {
if (x_is_integer) {
return INFINITY;
}
// it is more standard to write this as recursion, but
// nvcc does not like that
additional_summand = -c10::pi<scalar_t> /
compat_tan(c10::pi<scalar_t> * x);
x = 1 - x;
}
// Push x to be >= 10
accscalar_t result = 0;
while (x < 10) {
result -= 1 / x;
x += 1;
}
if (x == 10) {
return result + PSI_10 + additional_summand;
}
// Compute asymptotic digamma
static const accscalar_t A[] = {
8.33333333333333333333E-2,
-2.10927960927960927961E-2,
7.57575757575757575758E-3,
-4.16666666666666666667E-3,
3.96825396825396825397E-3,
-8.33333333333333333333E-3,
8.33333333333333333333E-2,
};
accscalar_t y = 0;
if (x < 1.0e17f) {
accscalar_t z = 1.0 / (x * x);
y = z * polevl<accscalar_t>(z, A, 6);
}
return static_cast<scalar_t>(
result + compat_log(x) - (0.5f / x) - y + additional_summand);
}
// Computes the reparameterized gradient -(d/dalpha cdf(x;alpha)) / pdf(x;alpha)
// for random number x drawn from a standard Gamma distribution Gamma(alpha).
template <typename scalar_t, typename accscalar_t>
C10_HOST_DEVICE scalar_t standard_gamma_grad_one(scalar_t alpha_, scalar_t x_) {
// Use a Taylor series expansion for small x.
accscalar_t x = static_cast<accscalar_t>(x_);
accscalar_t alpha = static_cast<accscalar_t>(alpha_);
if (x < 0.8f) {
accscalar_t numer = 1;
accscalar_t denom = alpha;
auto series1 = numer / denom;
auto series2 = numer / (denom * denom);
for (int i = 1; i <= 5; ++i) {
numer *= -x / static_cast<accscalar_t>(i);
denom += 1;
series1 += numer / denom;
series2 += numer / (denom * denom);
}
const auto pow_x_alpha = compat_pow(x, alpha);
const auto gamma_pdf = compat_pow(x, alpha - 1) * compat_exp(-x);
const auto gamma_cdf = pow_x_alpha * series1;
const auto gamma_cdf_alpha =
(compat_log(x) - digamma_one<accscalar_t, accscalar_t>(alpha)) *
gamma_cdf -
pow_x_alpha * series2;
const auto result = -gamma_cdf_alpha / gamma_pdf;
return isnan(result) ? static_cast<scalar_t>( 0.f ) : static_cast<scalar_t>(result);
}
// Use a Rice saddle point expansion for large alpha.
if (alpha > 8.0f) {
if (0.9f * alpha <= x && x <= 1.1f * alpha) {
const auto numer_1 = 1 + 24 * alpha * (1 + 12 * alpha);
const auto numer_2 = 1440 * (alpha * alpha) + 6 * x * (53 - 120 * x)
- 65 * x * x / alpha + alpha * (107 + 3600 * x);
const auto denom = 1244160 * (alpha * alpha) * (alpha * alpha);
return static_cast<scalar_t>(numer_1 * numer_2 / denom);
}
const auto denom = compat_sqrt(8 * alpha);
const auto term2 = denom / (alpha - x);
const auto term3 = compat_pow(
x - alpha - alpha * compat_log(x / alpha),
static_cast<accscalar_t>(-1.5));
const auto term23 = (x < alpha) ? term2 - term3 : term2 + term3;
const auto term1 = compat_log(x / alpha) * term23 -
compat_sqrt(2 / alpha) * (alpha + x) / ((alpha - x) * (alpha - x));
const auto stirling = 1 + 1 / (12 * alpha) * (1 + 1 / (24 * alpha));
const auto numer = x * term1;
return static_cast<scalar_t>(-stirling * numer / denom);
}
// Use a bivariate rational approximation to the reparameterized gradient.
const auto u = compat_log(x / alpha);
const auto v = compat_log(alpha);
static const accscalar_t coef_uv[3][8] = {
{0.16009398, -0.094634809, 0.025146376, -0.0030648343,
1, 0.32668115, 0.10406089, 0.0014179084},
{0.53487893, 0.1298071, 0.065735949, -0.0015649758,
0.16639465, 0.020070113, -0.0035938915, -0.00058392623},
{0.040121004, -0.0065914022, -0.0026286047, -0.0013441777,
0.017050642, -0.0021309326, 0.00085092367, -1.5247877e-07},
};
accscalar_t coef_v[8];
for (int i = 0; i < 8; ++ i) {
coef_v[i] = coef_uv[0][i] + u * (coef_uv[1][i] + u * coef_uv[2][i]);
}
const auto p = coef_v[0] + v * (coef_v[1] + v * (coef_v[2] + v * coef_v[3]));
const auto q = coef_v[4] + v * (coef_v[5] + v * (coef_v[6] + v * coef_v[7]));
return static_cast<scalar_t>(compat_exp(p / q));
}
// Approximate reparameterized gradient of Beta(x,alpha,beta) wrt alpha.
// Assumes x is close to zero and uses a Taylor expansion.
template <typename scalar_t, typename accscalar_t>
C10_DEVICE static inline scalar_t _beta_grad_alpha_small(scalar_t x, scalar_t alpha, scalar_t beta) {
const scalar_t factor = digamma_one<scalar_t, accscalar_t>(alpha)
- digamma_one<scalar_t, accscalar_t>(alpha + beta) - compat_log(x);
scalar_t numer = 1;
scalar_t series = numer / alpha * (factor + 1 / alpha);
for (int i = 1; i <= 10; ++i) {
scalar_t casted_i = static_cast<scalar_t>(i);
numer *= (casted_i - beta) * x / casted_i;
const scalar_t denom = alpha + casted_i;
series += numer / denom * (factor + 1 / denom);
}
const scalar_t result = x * compat_pow(1 - x, -beta) * series;
return isnan(result) ? static_cast<scalar_t>( 0.f ) : result;
}
// Approximate reparameterized gradient of Beta(x,alpha,beta) wrt beta.
// Assumes x is close to zero and uses a Taylor expansion.
template <typename scalar_t, typename accscalar_t>
C10_DEVICE static inline scalar_t _beta_grad_beta_small(scalar_t x, scalar_t alpha, scalar_t beta) {
const scalar_t factor = digamma_one<scalar_t, accscalar_t>(alpha + beta) - digamma_one<scalar_t, accscalar_t>(beta);
scalar_t numer = 1, betas = 1, dbetas = 0, series = factor / alpha;
for (int i = 1; i <= 8; ++i) {
scalar_t casted_i = static_cast<scalar_t>(i);
numer *= -x / casted_i;
dbetas = dbetas * (beta - casted_i) + betas;
betas = betas * (beta - casted_i);
series += numer / (alpha + casted_i) * (dbetas + factor * betas);
}
const scalar_t result = -compat_pow(1 - x, 1 - beta) * series;
return isnan(result) ? static_cast<scalar_t>( 0.f ) : result;
}
// Approximate reparameterized gradient of Beta(x,alpha,beta) wrt alpha.
// Assumes alpha and beta are both large and uses a Rice saddle point expansion.
// To ensure numerical stability, this computation is performed at higher precision.
template<typename scalar_t, typename accscalar_t>
C10_DEVICE static inline scalar_t _beta_grad_alpha_mid(accscalar_t x, accscalar_t alpha, accscalar_t beta) {
const accscalar_t total = alpha + beta;
const accscalar_t mean = alpha / total;
const accscalar_t std = compat_sqrt(alpha * beta / (total + 1)) / total;
if (mean - 0.1 * std <= x && x <= mean + 0.1 * std) {
// Avoid the singularity at x = mean.
const accscalar_t poly = 47 * x * (beta * beta) * (beta * beta) + alpha * (
(43 + 20 * (16 + 27 * beta) * x) * (beta * beta) * beta + alpha * (
3 * (59 + 180 * beta - 90 * x) * (beta * beta) + alpha * (
(453 + 1620 * beta * (1 - x) - 455 * x) * beta + alpha * (
8 * (1 - x) * (135 * beta - 11)))));
const accscalar_t prefactor_num = (1 + 12 * alpha) * (1 + 12 * beta) / (total * total);
const accscalar_t prefactor_den = 12960 * alpha * alpha * alpha * beta * beta * (1 + 12 * total);
return prefactor_num / (1 - x) * poly / prefactor_den;
}
const accscalar_t prefactor = -x / compat_sqrt(2 * alpha * beta / total);
const accscalar_t stirling = (1 + 1 / (12 * alpha) + 1 / (288 * alpha * alpha))
* (1 + 1 / (12 * beta) + 1 / (288 * beta * beta))
/ (1 + 1 / (12 * total) + 1 / (288 * total * total));
const accscalar_t term1_num = 2 * (alpha * alpha) * (x - 1) + alpha * beta * (x - 1) - x * (beta * beta);
const accscalar_t axbx = alpha * (x - 1) + beta * x;
const accscalar_t term1_den = compat_sqrt(2 * alpha / beta) * compat_pow(total, static_cast<accscalar_t>(1.5f)) * axbx * axbx;
const accscalar_t term1 = term1_num / term1_den;
const accscalar_t term2 = 0.5f * compat_log(alpha / (total * x));
const accscalar_t term3_num = compat_sqrt(8 * alpha * beta / total);
const accscalar_t term3_den = beta * x + alpha * (x - 1);
const accscalar_t term3 = term3_num / term3_den;
const accscalar_t term4_base = beta * compat_log(beta / (total * (1 - x))) +
alpha * compat_log(alpha / (total * x));
const accscalar_t term4 = compat_pow(term4_base, static_cast<accscalar_t>(-1.5f));
const accscalar_t term1234 = term1 + term2 * (term3 + (x < mean ? term4 : -term4));
return static_cast<scalar_t>(stirling * prefactor * term1234);
}
// Computes a scaled reparameterized gradient
// -(d/dalpha cdf(x;alpha,beta)) / pdf(x;alpha,beta) / (1-x)
// for random number x drawn from a Beta distribution Beta(alpha,beta).
// This function inputs total=alpha+beta to make it easy to implement
// Dirichlet reparameterized gradients in terms of Betas.
template<typename scalar_t, typename accscalar_t>
C10_HOST_DEVICE static inline scalar_t dirichlet_grad_one(scalar_t x, scalar_t alpha, scalar_t total) {
accscalar_t x_ = static_cast<accscalar_t>(x);
accscalar_t alpha_ = static_cast<accscalar_t>(alpha);
accscalar_t total_ = static_cast<accscalar_t>(total);
const scalar_t beta = total - alpha;
const accscalar_t beta_ = total_ - alpha_;
const scalar_t boundary = total * x * (1 - x);
// Use an asymptotic approximation for x close to 0.
if (x <= 0.5f && boundary < 2.5f) {
return _beta_grad_alpha_small<scalar_t, accscalar_t>(x, alpha, beta);
}
// Use an asymptotic approximation for x close to 1.
if (x >= 0.5f && boundary < 0.75f) {
return -_beta_grad_beta_small<scalar_t, accscalar_t>(1 - x, beta, alpha);
}
// Use an asymptotic approximation when alpha and (total - alpha) are both large.
if (alpha > 6 && beta > 6) {
return _beta_grad_alpha_mid<scalar_t, accscalar_t>(x_, alpha_, beta_);
}
// Use a rational correction to an analytic approximation.
static const accscalar_t c[2][3][3][4] = {
{{{1.003668233, -0.01061107488, -0.0657888334, 0.01201642863},
{0.6336835991, -0.3557432599, 0.05486251648, -0.001465281033},
{-0.03276231906, 0.004474107445, 0.002429354597, -0.0001557569013}},
{{0.221950385, -0.3187676331, 0.01799915743, 0.01074823814},
{-0.2951249643, 0.06219954479, 0.01535556598, 0.001550077057},
{0.02155310298, 0.004170831599, 0.001292462449, 6.976601077e-05}},
{{-0.05980841433, 0.008441916499, 0.01085618172, 0.002319392565},
{0.02911413504, 0.01400243777, -0.002721828457, 0.000751041181},
{0.005900514878, -0.001936558688, -9.495446725e-06, 5.385558597e-05}}},
{{{1, -0.02924021934, -0.04438342661, 0.007285809825},
{0.6357567472, -0.3473456711, 0.05454656494, -0.002407477521},
{-0.03301322327, 0.004845219414, 0.00231480583, -0.0002307248149}},
{{0.5925320577, -0.1757678135, 0.01505928619, 0.000564515273},
{0.1014815858, -0.06589186703, 0.01272886114, -0.0007316646956},
{-0.007258481865, 0.001096195486, 0.0003934994223, -4.12701925e-05}},
{{0.06469649321, -0.0236701437, 0.002902096474, -5.896963079e-05},
{0.001925008108, -0.002869809258, 0.0008000589141, -6.063713228e-05},
{-0.0003477407336, 6.959756487e-05, 1.097287507e-05, -1.650964693e-06}}},
};
const accscalar_t u = compat_log(x_);
const accscalar_t a = compat_log(alpha_) - u;
const accscalar_t b = compat_log(total_) - a;
const accscalar_t pow_u[3] = {1, u, u * u};
const accscalar_t pow_a[3] = {1, a, a * a};
accscalar_t p = 0.0;
accscalar_t q = 0.0;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
const accscalar_t ua = pow_u[i] * pow_a[j];
p += ua * (c[0][i][j][0] + b * (c[0][i][j][1] + b * (c[0][i][j][2] + b * c[0][i][j][3])));
q += ua * (c[1][i][j][0] + b * (c[1][i][j][1] + b * (c[1][i][j][2] + b * c[1][i][j][3])));
}
}
const accscalar_t approx = x_ * (digamma_one<scalar_t, accscalar_t>(total_) - digamma_one<scalar_t, accscalar_t>(alpha_)) / beta_;
return static_cast<scalar_t>(p / q * approx);
}
} // namespace
Reference in New Issue View Git Blame Copy Permalink