Add truncated_gaussian_random XPU kernel (#27861)
* Add truncated_gaussian_random_op XPU kernel * Add truncated_gaussian_random_op XPU kernel, test=kunlun * little change, test=kunlun * change boost_get to BOOST_GET_CONST * change boost_get to BOOST_GET_CONST, test=kunlun * little change, test=kunlun * use Generator to generate random number and optimize format, test=kunlun * little change, test=kunlun * add TODO, test=kunlunswt-req
pangyoki
5 years ago
committed by
GitHub
parent
5b8e500135
commit
4c5b779a99
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <limits>
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#include <random>
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#include "paddle/fluid/framework/generator.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
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template <typename T>
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T Erfinv(T x) {
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if (x < -1 || x > 1) {
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return std::numeric_limits<T>::quiet_NaN();
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} else if (x == 1.0) {
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return std::numeric_limits<T>::infinity();
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} else if (x == -1.0) {
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return -std::numeric_limits<T>::infinity();
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}
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const T LN2 = 6.931471805599453094172321214581e-1;
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const T A0 = 1.1975323115670912564578e0;
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const T A1 = 4.7072688112383978012285e1;
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const T A2 = 6.9706266534389598238465e2;
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const T A3 = 4.8548868893843886794648e3;
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const T A4 = 1.6235862515167575384252e4;
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const T A5 = 2.3782041382114385731252e4;
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const T A6 = 1.1819493347062294404278e4;
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const T A7 = 8.8709406962545514830200e2;
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const T B0 = 1.0000000000000000000e0;
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const T B1 = 4.2313330701600911252e1;
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const T B2 = 6.8718700749205790830e2;
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const T B3 = 5.3941960214247511077e3;
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const T B4 = 2.1213794301586595867e4;
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const T B5 = 3.9307895800092710610e4;
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const T B6 = 2.8729085735721942674e4;
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const T B7 = 5.2264952788528545610e3;
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const T C0 = 1.42343711074968357734e0;
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const T C1 = 4.63033784615654529590e0;
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const T C2 = 5.76949722146069140550e0;
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const T C3 = 3.64784832476320460504e0;
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const T C4 = 1.27045825245236838258e0;
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const T C5 = 2.41780725177450611770e-1;
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const T C6 = 2.27238449892691845833e-2;
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const T C7 = 7.74545014278341407640e-4;
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const T D0 = 1.4142135623730950488016887e0;
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const T D1 = 2.9036514445419946173133295e0;
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const T D2 = 2.3707661626024532365971225e0;
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const T D3 = 9.7547832001787427186894837e-1;
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const T D4 = 2.0945065210512749128288442e-1;
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const T D5 = 2.1494160384252876777097297e-2;
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const T D6 = 7.7441459065157709165577218e-4;
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const T D7 = 1.4859850019840355905497876e-9;
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const T E0 = 6.65790464350110377720e0;
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const T E1 = 5.46378491116411436990e0;
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const T E2 = 1.78482653991729133580e0;
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const T E3 = 2.96560571828504891230e-1;
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const T E4 = 2.65321895265761230930e-2;
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const T E5 = 1.24266094738807843860e-3;
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const T E6 = 2.71155556874348757815e-5;
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const T E7 = 2.01033439929228813265e-7;
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const T F0 = 1.414213562373095048801689e0;
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const T F1 = 8.482908416595164588112026e-1;
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const T F2 = 1.936480946950659106176712e-1;
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const T F3 = 2.103693768272068968719679e-2;
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const T F4 = 1.112800997078859844711555e-3;
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const T F5 = 2.611088405080593625138020e-5;
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const T F6 = 2.010321207683943062279931e-7;
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const T F7 = 2.891024605872965461538222e-15;
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T abs_x = abs(x);
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if (abs_x <= 0.85) {
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T r = 0.180625 - 0.25 * x * x;
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T num =
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(((((((A7 * r + A6) * r + A5) * r + A4) * r + A3) * r + A2) * r + A1) *
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r +
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A0);
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T den =
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(((((((B7 * r + B6) * r + B5) * r + B4) * r + B3) * r + B2) * r + B1) *
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r +
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B0);
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return x * num / den;
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}
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T r = sqrt(LN2 - log(1.0 - abs_x));
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T num, den;
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if (r <= 5.0) {
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r = r - 1.6;
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num =
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(((((((C7 * r + C6) * r + C5) * r + C4) * r + C3) * r + C2) * r + C1) *
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r +
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C0);
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den =
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(((((((D7 * r + D6) * r + D5) * r + D4) * r + D3) * r + D2) * r + D1) *
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r +
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D0);
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} else {
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r = r - 5.0;
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num =
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(((((((E7 * r + E6) * r + E5) * r + E4) * r + E3) * r + E2) * r + E1) *
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r +
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E0);
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den =
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(((((((F7 * r + F6) * r + F5) * r + F4) * r + F3) * r + F2) * r + F1) *
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r +
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F0);
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}
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if (x < 0) {
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return -num / den;
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} else {
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return num / den;
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}
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}
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template <typename T>
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struct TruncatedNormal {
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T mean, std;
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T a_normal_cdf;
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T b_normal_cdf;
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TruncatedNormal(T mean, T std) : mean(mean), std(std) {
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auto normal_cdf = [](T x) {
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return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
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};
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a_normal_cdf = normal_cdf(-2.0);
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b_normal_cdf = normal_cdf(2.0);
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}
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T operator()(T value) const {
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auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
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return std::sqrt(2.0) * Erfinv(2 * p - 1) * std + mean;
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,65 @@
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/truncated_gaussian_random_op.h"
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#include <limits>
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#include <random>
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#include "paddle/fluid/framework/generator.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class XPUTruncatedGaussianRandomKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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float mean = context.Attr<float>("mean");
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float std = context.Attr<float>("std");
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auto* tensor = context.Output<framework::Tensor>("Out");
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T* data = tensor->mutable_data<T>(context.GetPlace());
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std::uniform_real_distribution<T> dist(std::numeric_limits<float>::min(),
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1.0);
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TruncatedNormal<T> truncated_normal(mean, std);
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int64_t size = tensor->numel();
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unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
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// TODO(pangyoki): implement GetXPURandomEngine to set different seeds on
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// corresponding XPU device.
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auto engine = framework::GetCPURandomEngine(seed);
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std::unique_ptr<T[]> data_cpu(new T[size]);
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for (int64_t i = 0; i < size; ++i) {
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data_cpu[i] = truncated_normal(dist(*engine));
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}
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()), data,
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platform::CPUPlace(), reinterpret_cast<void*>(data_cpu.get()),
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size * sizeof(T));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(truncated_gaussian_random,
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ops::XPUTruncatedGaussianRandomKernel<
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paddle::platform::XPUDeviceContext, float>);
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#endif // PADDLE_WITH_XPU
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@ -0,0 +1,39 @@
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# Copyright (c) 2020 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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from __future__ import print_function
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import sys
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sys.path.append("..")
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import unittest
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import numpy
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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from paddle.fluid.executor import Executor
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from test_truncated_gaussian_random_op import TestTrunctedGaussianRandomOp
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paddle.enable_static()
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class TestXPUTrunctedGaussianRandomOp(TestTrunctedGaussianRandomOp):
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def test_xpu(self):
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if paddle.is_compiled_with_xpu():
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self.gaussian_random_test(place=fluid.XPUPlace(0))
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if __name__ == "__main__":
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unittest.main()
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