/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. 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 #include #include #include #include "paddle/operators/type_alias.h" namespace paddle { namespace operators { template struct UniformGenerator { T min_, max_; unsigned int seed_; __host__ __device__ UniformGenerator(T min, T max, int seed) : min_(min), max_(max), seed_(seed) {} __host__ __device__ T operator()(const unsigned int n) const { thrust::minstd_rand rng; rng.seed(seed_); thrust::uniform_real_distribution dist(min_, max_); rng.discard(n); return dist(rng); } }; template class GPUUniformRandomKernel : public OpKernel { public: void Compute(const ExecutionContext& context) const override { auto* tensor = context.Output(0); T* data = tensor->mutable_data(context.GetPlace()); unsigned int seed = static_cast(context.op_.GetAttr("seed")); if (seed == 0) { seed = std::random_device()(); } T min = static_cast(context.op_.GetAttr("min")); T max = static_cast(context.op_.GetAttr("max")); thrust::counting_iterator index_sequence_begin(0); ssize_t N = framework::product(tensor->dims()); thrust::transform(index_sequence_begin, index_sequence_begin + N, thrust::device_ptr(data), UniformGenerator(min, max, seed)); } }; } // namespace operators } // namespace paddle REGISTER_OP_GPU_KERNEL(uniform_random, ops::GPUUniformRandomKernel);