gaussian_random_op.cu

/* 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 <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/random.h>
#include <thrust/transform.h>
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/amp/fp16_type_traits.h"
#include "paddle/fluid/operators/distribution_helper.h"
#include "paddle/fluid/operators/fill_constant_op.h"
#include "paddle/fluid/operators/index_impl.cu.h"

DECLARE_bool(use_curand);

namespace paddle {
namespace operators {

template <typename T>
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_);
    using MT = typename details::MPTypeTrait<T>::Type;
    thrust::normal_distribution<MT> dist(mean_, std_);
    unsigned int new_n = n + offset_;
    rng.discard(new_n);
    MT out = dist(rng);
    return static_cast<T>(out);
  }
};

template <typename T>
class GPUGaussianRandomKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* tensor = context.Output<framework::Tensor>("Out");
    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
    bool seed_flag = false;
    if (seed == 0) {
      std::random_device rd;
      seed = rd();
      seed_flag = true;
    }
    T mean = static_cast<T>(context.Attr<float>("mean"));
    T std = static_cast<T>(context.Attr<float>("std"));
    auto shape = GetShape(context);
    tensor->Resize(shape);

    auto& dev_cxt =
        context.template device_context<platform::CUDADeviceContext>();
    T* data = tensor->mutable_data<T>(dev_cxt.GetPlace());

    int64_t size = tensor->numel();

    int device_id = context.GetPlace().GetDeviceId();
    auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);

    if (gen_cuda->GetIsInitPy() && seed_flag) {
      if (FLAGS_use_curand) {
        using MT = typename details::MPTypeTrait<T>::Type;
        distribution::normal_distribution<MT> dist;
        distribution::normal_transform<MT> trans(mean, std);
        distribution::distribution_and_transform<T>(dev_cxt, tensor, dist,
                                                    trans);
      } else {
        auto seed_offset = gen_cuda->IncrementOffset(1);
        int64_t gen_offset = size * seed_offset.second;
        auto func =
            GaussianGenerator<T>(mean, std, seed_offset.first, gen_offset);
        IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
      }
    } else {
      auto func = GaussianGenerator<T>(mean, std, seed);
      IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
    }
  }
};

template <typename T>
class GPUGaussianRandomBatchSizeLikeKernel : 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"));
    bool seed_flag = false;
    if (seed == 0) {
      std::random_device rd;
      seed = rd();
      seed_flag = true;
    }
    T mean = static_cast<T>(context.Attr<float>("mean"));
    T std = static_cast<T>(context.Attr<float>("std"));
    int64_t size = tensor->numel();

    int device_id = context.GetPlace().GetDeviceId();
    auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
    auto& dev_cxt =
        context.template device_context<platform::CUDADeviceContext>();

    if (gen_cuda->GetIsInitPy() && seed_flag) {
      auto seed_offset = gen_cuda->IncrementOffset(1);
      int64_t gen_offset = size * seed_offset.second;
      auto func = GaussianGenerator<T>(mean, std, seed_offset.first,
                                       seed_offset.second);
      IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
    } else {
      auto func = GaussianGenerator<T>(mean, std, seed);
      IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
    }
  }
};
}  // namespace operators
}  // namespace paddle

REGISTER_OP_CUDA_KERNEL(
    gaussian_random,
    paddle::operators::GPUGaussianRandomKernel<paddle::platform::float16>,
    paddle::operators::GPUGaussianRandomKernel<float>,
    paddle::operators::GPUGaussianRandomKernel<double>);
REGISTER_OP_CUDA_KERNEL(
    gaussian_random_batch_size_like,
    paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<
        paddle::platform::float16>,
    paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<float>,
    paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<double>);