/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #pragma once #include #include #include "paddle/fluid/framework/generator.h" #include "paddle/fluid/framework/op_registry.h" namespace paddle { namespace operators { // reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e template T Erfinv(T x) { if (x < -1 || x > 1) { return std::numeric_limits::quiet_NaN(); } else if (x == 1.0) { return std::numeric_limits::infinity(); } else if (x == -1.0) { return -std::numeric_limits::infinity(); } const T LN2 = 6.931471805599453094172321214581e-1; const T A0 = 1.1975323115670912564578e0; const T A1 = 4.7072688112383978012285e1; const T A2 = 6.9706266534389598238465e2; const T A3 = 4.8548868893843886794648e3; const T A4 = 1.6235862515167575384252e4; const T A5 = 2.3782041382114385731252e4; const T A6 = 1.1819493347062294404278e4; const T A7 = 8.8709406962545514830200e2; const T B0 = 1.0000000000000000000e0; const T B1 = 4.2313330701600911252e1; const T B2 = 6.8718700749205790830e2; const T B3 = 5.3941960214247511077e3; const T B4 = 2.1213794301586595867e4; const T B5 = 3.9307895800092710610e4; const T B6 = 2.8729085735721942674e4; const T B7 = 5.2264952788528545610e3; const T C0 = 1.42343711074968357734e0; const T C1 = 4.63033784615654529590e0; const T C2 = 5.76949722146069140550e0; const T C3 = 3.64784832476320460504e0; const T C4 = 1.27045825245236838258e0; const T C5 = 2.41780725177450611770e-1; const T C6 = 2.27238449892691845833e-2; const T C7 = 7.74545014278341407640e-4; const T D0 = 1.4142135623730950488016887e0; const T D1 = 2.9036514445419946173133295e0; const T D2 = 2.3707661626024532365971225e0; const T D3 = 9.7547832001787427186894837e-1; const T D4 = 2.0945065210512749128288442e-1; const T D5 = 2.1494160384252876777097297e-2; const T D6 = 7.7441459065157709165577218e-4; const T D7 = 1.4859850019840355905497876e-9; const T E0 = 6.65790464350110377720e0; const T E1 = 5.46378491116411436990e0; const T E2 = 1.78482653991729133580e0; const T E3 = 2.96560571828504891230e-1; const T E4 = 2.65321895265761230930e-2; const T E5 = 1.24266094738807843860e-3; const T E6 = 2.71155556874348757815e-5; const T E7 = 2.01033439929228813265e-7; const T F0 = 1.414213562373095048801689e0; const T F1 = 8.482908416595164588112026e-1; const T F2 = 1.936480946950659106176712e-1; const T F3 = 2.103693768272068968719679e-2; const T F4 = 1.112800997078859844711555e-3; const T F5 = 2.611088405080593625138020e-5; const T F6 = 2.010321207683943062279931e-7; const T F7 = 2.891024605872965461538222e-15; T abs_x = abs(x); if (abs_x <= 0.85) { T r = 0.180625 - 0.25 * x * x; T num = (((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) * r + A0); T den = (((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) * r + B0); return x * num / den; } T r = sqrt(LN2 - log(1.0 - abs_x)); T num, den; if (r <= 5.0) { r = r - 1.6; num = (((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) * r + C0); den = (((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) * r + D0); } else { r = r - 5.0; num = (((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) * r + E0); den = (((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) * r + F0); } if (x < 0) { return -num / den; } else { return num / den; } } template struct TruncatedNormal { T mean, std; T a_normal_cdf; T b_normal_cdf; TruncatedNormal(T mean, T std) : mean(mean), std(std) { auto normal_cdf = [](T x) { return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0; }; a_normal_cdf = normal_cdf(-2.0); b_normal_cdf = normal_cdf(2.0); } T operator()(T value) const { auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value; return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean; } }; } // namespace operators } // namespace paddle