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69 lines
2.5 KiB
69 lines
2.5 KiB
/* Copyright (c) 2016 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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#include <thrust/random.h>
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#include <thrust/transform.h>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct GaussianGenerator {
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T mean_, std_;
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unsigned int seed_;
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__host__ __device__ GaussianGenerator(T mean, T std, int seed)
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: mean_(mean), std_(std), seed_(seed) {}
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__host__ __device__ T operator()(const unsigned int n) const {
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thrust::minstd_rand rng;
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rng.seed(seed_);
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thrust::normal_distribution<T> dist(mean_, std_);
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rng.discard(n);
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return dist(rng);
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}
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};
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template <typename T>
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class GPUGaussianRandomKernel : 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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auto* tensor = context.Output<framework::Tensor>("Out");
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T* data = tensor->mutable_data<T>(context.GetPlace());
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unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
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if (seed == 0) {
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std::random_device rd;
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seed = rd();
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}
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T mean = static_cast<T>(context.Attr<float>("mean"));
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T std = static_cast<T>(context.Attr<float>("std"));
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thrust::counting_iterator<unsigned int> index_sequence_begin(0);
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int64_t size = tensor->numel();
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thrust::transform(index_sequence_begin, index_sequence_begin + size,
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thrust::device_ptr<T>(data),
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GaussianGenerator<T>(mean, std, seed));
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_CUDA_KERNEL(gaussian_random,
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paddle::operators::GPUGaussianRandomKernel<float>,
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paddle::operators::GPUGaussianRandomKernel<double>);
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REGISTER_OP_CUDA_KERNEL(gaussian_random_batch_size_like,
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paddle::operators::GPUGaussianRandomKernel<float>,
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paddle::operators::GPUGaussianRandomKernel<double>);
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