Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 46a1f69b 编写于 作者: P pangyoki 提交者： GitHub
浏览文件
操作

cherry pick 27861 Add truncated_gaussian_random XPU kernel, test=kunlun (#28060) 

* 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=kunlun
上级 b21409e0
隐藏空白更改
内联 并排
Showing with 266 addition and 133 deletion
paddle/fluid/operators/truncated_gaussian_random_op.cc
浏览文件 @ 46a1f69b
......@@ -14,146 +14,15 @@ limitations under the License. */
#include <limits>
#include <random>
#include <vector>
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/truncated_gaussian_random_op.h"
namespace paddle {
namespace operators {
// reference: https://gist.github.com/lakshayg/d80172fe5ae3c5d2c2aedb53c250320e
template <typename T>
T Erfinv(T x) {
if (x < -1 || x > 1) {
return std::numeric_limits<T>::quiet_NaN();
} else if (x == 1.0) {
return std::numeric_limits<T>::infinity();
} else if (x == -1.0) {
return -std::numeric_limits<T>::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 <typename T>
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;
}
};
template <typename T>
class CPUTruncatedGaussianRandomKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/truncated_gaussian_random_op.h 0 → 100644
浏览文件 @ 46a1f69b
/* 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 <limits>
#include <random>
#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 <typename T>
T Erfinv(T x) {
if (x < -1 || x > 1) {
return std::numeric_limits<T>::quiet_NaN();
} else if (x == 1.0) {
return std::numeric_limits<T>::infinity();
} else if (x == -1.0) {
return -std::numeric_limits<T>::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 <typename T>
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
paddle/fluid/operators/truncated_gaussian_random_op_xpu.cc 0 → 100644
浏览文件 @ 46a1f69b
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/truncated_gaussian_random_op.h"
#include <limits>
#include <random>
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class XPUTruncatedGaussianRandomKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
float mean = context.Attr<float>("mean");
float std = context.Attr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
std::uniform_real_distribution<T> dist(std::numeric_limits<float>::min(),
1.0);
TruncatedNormal<T> truncated_normal(mean, std);
int64_t size = tensor->numel();
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
// TODO(pangyoki): implement GetXPURandomEngine to set different seeds on
// corresponding XPU device.
auto engine = framework::GetCPURandomEngine(seed);
std::unique_ptr<T[]> data_cpu(new T[size]);
for (int64_t i = 0; i < size; ++i) {
data_cpu[i] = truncated_normal(dist(*engine));
}
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()), data,
platform::CPUPlace(), reinterpret_cast<void*>(data_cpu.get()),
size * sizeof(T));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(truncated_gaussian_random,
ops::XPUTruncatedGaussianRandomKernel<
paddle::platform::XPUDeviceContext, float>);
#endif // PADDLE_WITH_XPU
python/paddle/fluid/tests/unittests/xpu/test_truncated_gaussian_random_op_xpu.py 0 → 100644
浏览文件 @ 46a1f69b
# 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.
from __future__ import print_function
import sys
sys.path.append("..")
import unittest
import numpy
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from paddle.fluid.executor import Executor
from test_truncated_gaussian_random_op import TestTrunctedGaussianRandomOp
paddle.enable_static()
class TestXPUTrunctedGaussianRandomOp(TestTrunctedGaussianRandomOp):
def test_xpu(self):
if paddle.is_compiled_with_xpu():
self.gaussian_random_test(place=fluid.XPUPlace(0))
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或