/* Copyright (c) 2016 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 #include #include #include #include "paddle/fluid/framework/generator.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/operators/fill_constant_op.h" namespace paddle { namespace operators { template struct GaussianGenerator { T mean_, std_; unsigned int seed_; unsigned int offset_ = 0; __host__ __device__ GaussianGenerator(T mean, T std, int seed) : mean_(mean), std_(std), seed_(seed) {} __host__ __device__ GaussianGenerator(T mean, T std, int seed, int offset) : mean_(mean), std_(std), seed_(seed), offset_(offset) {} __host__ __device__ T operator()(const unsigned int n) const { thrust::minstd_rand rng; rng.seed(seed_); thrust::normal_distribution dist(mean_, std_); unsigned int new_n = n + offset_; rng.discard(new_n); return dist(rng); } }; template class GPUGaussianRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); unsigned int seed = static_cast(context.Attr("seed")); bool seed_flag = false; if (seed == 0) { std::random_device rd; seed = rd(); seed_flag = true; } T mean = static_cast(context.Attr("mean")); T std = static_cast(context.Attr("std")); thrust::counting_iterator index_sequence_begin(0); auto shape = GetShape(context); tensor->Resize(shape); T* data = tensor->mutable_data(context.GetPlace()); int64_t size = tensor->numel(); int device_id = BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace()).GetDeviceId(); auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id); if (gen_cuda->GetIsInitPy() && seed_flag) { auto seed_offset = gen_cuda->IncrementOffset(1); int gen_offset = size * seed_offset.second; thrust::transform( index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), GaussianGenerator(mean, std, seed_offset.first, gen_offset)); } else { thrust::transform(index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), GaussianGenerator(mean, std, seed)); } } }; template class GPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); T* data = tensor->mutable_data(context.GetPlace()); unsigned int seed = static_cast(context.Attr("seed")); bool seed_flag = false; if (seed == 0) { std::random_device rd; seed = rd(); seed_flag = true; } T mean = static_cast(context.Attr("mean")); T std = static_cast(context.Attr("std")); thrust::counting_iterator index_sequence_begin(0); int64_t size = tensor->numel(); int device_id = BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace()).GetDeviceId(); auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id); if (gen_cuda->GetIsInitPy() && seed_flag) { auto seed_offset = gen_cuda->IncrementOffset(1); int gen_offset = size * seed_offset.second; thrust::transform(index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), GaussianGenerator(mean, std, seed_offset.first, seed_offset.second)); } else { thrust::transform(index_sequence_begin, index_sequence_begin + size, thrust::device_ptr(data), GaussianGenerator(mean, std, seed)); } } }; } // namespace operators } // namespace paddle REGISTER_OP_CUDA_KERNEL(gaussian_random, paddle::operators::GPUGaussianRandomKernel, paddle::operators::GPUGaussianRandomKernel); REGISTER_OP_CUDA_KERNEL( gaussian_random_batch_size_like, paddle::operators::GPUGaussianRandomBatchSizeLikeKernel, paddle::operators::GPUGaussianRandomBatchSizeLikeKernel);