You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
2.6 KiB
78 lines
2.6 KiB
/* Copyright (c) 2018 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. */
|
|
|
|
#include <thrust/random.h>
|
|
#include <thrust/transform.h>
|
|
#include <limits>
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/framework/operator.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename T>
|
|
struct TruncatedNormal {
|
|
T mean, std;
|
|
T a_normal_cdf;
|
|
T b_normal_cdf;
|
|
unsigned int seed;
|
|
T numeric_min;
|
|
|
|
__host__ __device__ TruncatedNormal(T mean, T std, T numeric_min, int seed)
|
|
: mean(mean), std(std), seed(seed), numeric_min(numeric_min) {
|
|
a_normal_cdf = (1.0 + erff(-2.0 / sqrtf(2.0))) / 2.0;
|
|
b_normal_cdf = (1.0 + erff(2.0 / sqrtf(2.0))) / 2.0;
|
|
}
|
|
|
|
__host__ __device__ T operator()(const unsigned int n) const {
|
|
thrust::minstd_rand rng;
|
|
rng.seed(seed);
|
|
thrust::uniform_real_distribution<T> dist(numeric_min, 1);
|
|
rng.discard(n);
|
|
T value = dist(rng);
|
|
auto p = a_normal_cdf + (b_normal_cdf - a_normal_cdf) * value;
|
|
return std::sqrt(2.0) * erfinvf(2 * p - 1) * std + mean;
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
class GPUTruncatedGaussianRandomKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* tensor = context.Output<framework::Tensor>("Out");
|
|
T* data = tensor->mutable_data<T>(context.GetPlace());
|
|
|
|
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
|
|
if (seed == 0) {
|
|
std::random_device rd;
|
|
seed = rd();
|
|
}
|
|
T mean = static_cast<T>(context.Attr<float>("mean"));
|
|
T std = static_cast<T>(context.Attr<float>("std"));
|
|
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
|
|
int64_t size = tensor->numel();
|
|
thrust::transform(
|
|
index_sequence_begin, index_sequence_begin + size,
|
|
thrust::device_ptr<T>(data),
|
|
TruncatedNormal<T>(mean, std, std::numeric_limits<T>::min(), seed));
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
REGISTER_OP_CUDA_KERNEL(
|
|
truncated_gaussian_random,
|
|
paddle::operators::GPUTruncatedGaussianRandomKernel<float>);
|