wehub-resource-sync
148 lines
3.6 KiB
C++
148 lines
3.6 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/for_range.h"
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#define MAXLOG 7.09782712893383996732E2
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#define MACHEP 1.11022302462515654042E-16
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namespace phi {
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template <typename T>
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HOSTDEVICE T igam(const T a, const T x);
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template <typename T>
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HOSTDEVICE T igamc(const T a, const T x);
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template <typename T>
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HOSTDEVICE T igam(const T a, const T x) {
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if ((x <= T{0}) || (a <= T{0})) return (T{0.0});
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if ((x > T{1.0}) && (x > a)) return (T{1.0} - igamc(a, x));
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/* Compute x**a * exp(-x) / gamma(a) */
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T ax = a * log(x) - x - std::lgamma(a);
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if (ax < -MAXLOG) {
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return (T{0.0});
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}
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ax = exp(ax);
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/* power series */
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T r = a;
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T c = T{1.0};
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T ans = T{1.0};
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do {
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r += T{1.0};
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c *= x / r;
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ans += c;
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} while (c / ans > MACHEP);
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return (ans * ax / a);
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}
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template <typename T>
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HOSTDEVICE T igamc(const T a, const T x) {
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static const T big = 4.503599627370496e15;
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static const T biginv = 2.22044604925031308085e-16;
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if ((x <= T{0}) || (a <= T{0})) return (T{1.0});
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if ((x < T{1.0}) || (x < a)) return (T{1.0} - igam(a, x));
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T ax = a * log(x) - x - std::lgamma(a);
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if (ax < -MAXLOG) {
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return (T{0.0});
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}
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ax = exp(ax);
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/* continued fraction */
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T y = T{1.0} - a;
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T z = x + y + T{1.0};
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T c = T{0.0};
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T pkm2 = T{1.0};
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T qkm2 = x;
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T pkm1 = x + T{1.0};
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T qkm1 = z * x;
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T ans = pkm1 / qkm1;
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T t;
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do {
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c += T{1.0};
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y += T{1.0};
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z += T{2.0};
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T yc = y * c;
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T pk = pkm1 * z - pkm2 * yc;
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T qk = qkm1 * z - qkm2 * yc;
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if (qk != T{0}) {
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T r = pk / qk;
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t = fabs((ans - r) / r);
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ans = r;
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} else {
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t = T{1.0};
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}
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pkm2 = pkm1;
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pkm1 = pk;
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qkm2 = qkm1;
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qkm1 = qk;
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if (fabs(pk) > big) {
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pkm2 *= biginv;
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pkm1 *= biginv;
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qkm2 *= biginv;
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qkm1 *= biginv;
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}
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} while (t > MACHEP);
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return (ans * ax);
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}
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template <typename T>
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struct IgammaFunctor {
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IgammaFunctor(const T* x, const T* a, T* output, int64_t numel)
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: x_(x), a_(a), output_(output), numel_(numel) {}
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HOSTDEVICE void operator()(int64_t idx) const {
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using MT = typename MPTypeTrait<T>::Type;
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const MT mp_x = static_cast<MT>(x_[idx]);
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const MT mp_a = static_cast<MT>(a_[idx]);
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output_[idx] = static_cast<T>(igamc<MT>(mp_a, mp_x));
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}
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private:
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const T* x_;
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const T* a_;
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T* output_;
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int64_t numel_;
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};
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template <typename T, typename Context>
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void GammainccKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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auto numel = x.numel();
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auto* x_data = x.data<T>();
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auto* y_data = y.data<T>();
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auto* out_data = dev_ctx.template Alloc<T>(out);
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funcs::ForRange<Context> for_range(dev_ctx, numel);
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IgammaFunctor<T> functor(y_data, x_data, out_data, numel);
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for_range(functor);
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}
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} // namespace phi
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