dropout_impl.cu.h

/* Copyright (c) 2021 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. */

#pragma once

#include <string>

#ifdef PADDLE_WITH_CUDA
#include <cuda.h>
#include <curand_kernel.h>
#include "paddle/fluid/platform/dynload/curand.h"
#endif
#ifdef PADDLE_WITH_HIP
#include <hip/hip_runtime.h>
#include <hiprand_kernel.h>
#include "paddle/fluid/platform/dynload/hiprand.h"
#endif

#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/amp/fp16_type_traits.h"
#include "paddle/fluid/operators/dropout_impl_util.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_impl.cu.h"
#include "paddle/fluid/platform/aligned_vector.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/kernels/funcs/distribution_helper.h"
#include "paddle/phi/kernels/funcs/functors.h"

namespace paddle {
namespace operators {

template <typename T1, typename T2 = T1, typename OutT = T1>
struct DstMaskFunctor {
  const float retain_prob_;
  const bool is_upscale_in_train_;
  using MT = typename details::MPTypeTrait<T1>::Type;
  MT factor;
  HOSTDEVICE inline DstMaskFunctor(const float retain_prob,
                                   const bool is_upscale_in_train)
      : retain_prob_(retain_prob), is_upscale_in_train_(is_upscale_in_train) {
    factor = static_cast<MT>(1.0f / retain_prob_);
  }

  HOSTDEVICE inline void operator()(OutT* dst, const T1* src_val,
                                    const T2* rand, int num) const {
    static constexpr int kCount =
        phi::funcs::uniform_distribution<T2>::kReturnsCount;
// 0 ~ kCount -1 is dist , kCount ~ 2 * kCount - 1 is mask
#pragma unroll
    for (int i = 0; i < kCount; i++) {
      if (rand[i] < retain_prob_) {
        dst[i] = is_upscale_in_train_
                     ? static_cast<T1>(static_cast<MT>(src_val[i]) * factor)
                     : static_cast<T1>(src_val[i]);
        dst[i + kCount] = static_cast<T1>(1);
      } else {
        dst[i] = static_cast<T1>(0);
        dst[i + kCount] = dst[i];
      }
    }
  }
};

template <typename T, typename MaskType>
__global__ void VectorizedRandomGenerator(const size_t n, uint64_t seed,
                                          const float dropout_prob,
                                          const T* src, MaskType* mask, T* dst,
                                          bool is_upscale_in_train,
                                          uint64_t increment,
                                          size_t main_offset) {
  size_t idx = static_cast<size_t>(BLOCK_ID_X * BLOCK_NUM_X);
  static constexpr int kCount =
      phi::funcs::uniform_distribution<float>::kReturnsCount;
  size_t stride = BLOCK_NUM_X * GRID_NUM_X * kCount;
#ifdef PADDLE_WITH_HIP
  hiprandStatePhilox4_32_10_t state;
  hiprand_init(seed, idx + THREAD_ID_X, increment, &state);
  using SType = hiprandStatePhilox4_32_10_t;
#else
  curandStatePhilox4_32_10_t state;
  curand_init(seed, idx + THREAD_ID_X, increment, &state);
  using SType = curandStatePhilox4_32_10_t;
#endif
  T dst_mask[kCount * 2];  // 0 ~ kCount -1 : dst;kCount ~ 2 * kCount - 1: mask
  float rands[kCount];
  MaskType mask_result[kCount];
  using Rand = phi::funcs::uniform_distribution<float>;
  using Cast = kps::IdentityFunctor<T>;
  int deal_size = BLOCK_NUM_X * kCount;

  size_t fix = idx * kCount;

  auto dst_functor =
      DstMaskFunctor<T, float>(1.0f - dropout_prob, is_upscale_in_train);
  for (; fix < main_offset; fix += stride) {
    kps::ReadData<T, kCount, 1, 1, false>(&dst_mask[0], src + fix, deal_size);
    kps::ElementwiseRandom<SType, float, kCount, 1, Rand>(&rands[0], Rand(),
                                                          &state);
    // dst
    kps::OperatorTernary<T, float, T, DstMaskFunctor<T, float>>(
        &dst_mask[0], &dst_mask[0], &rands[0], dst_functor, kCount);
    kps::WriteData<T, kCount, 1, 1, false>(dst + fix, &dst_mask[0], deal_size);
    // mask
    kps::ElementwiseUnary<T, MaskType, kCount, 1, 1, Cast>(
        &mask_result[0], &dst_mask[kCount], Cast());
    kps::WriteData<MaskType, kCount, 1, 1, false>(mask + fix, &mask_result[0],
                                                  deal_size);
    if (fix > idx * kCount + 1) {
      __syncthreads();
    }
  }
  int remainder = n - fix;
  if (remainder > 0) {
    kps::ReadData<T, kCount, 1, 1, true>(&dst_mask[0], src + fix, remainder);
    kps::ElementwiseRandom<SType, float, kCount, 1, Rand>(&rands[0], Rand(),
                                                          &state);
    // dst
    kps::OperatorTernary<T, float, T, DstMaskFunctor<T, float>>(
        &dst_mask[0], &dst_mask[0], &rands[0], dst_functor, kCount);
    kps::WriteData<T, kCount, 1, 1, true>(dst + fix, &dst_mask[0], remainder);
    // mask
    kps::ElementwiseUnary<T, MaskType, kCount, 1, 1, Cast>(
        &mask_result[0], &dst_mask[kCount], Cast());
    kps::WriteData<MaskType, kCount, 1, 1, true>(mask + fix, &mask_result[0],
                                                 remainder);
    __syncthreads();
  }
}

template <typename T>
void DropoutFwGPUKernelDriver(const phi::GPUContext& dev_ctx, bool is_test,
                              const std::string dropout_implementation,
                              float dropout_prob, bool upscale_in_train,
                              bool is_fix_seed, int seed_val,
                              const framework::Tensor& x,
                              const framework::Tensor* seed,
                              framework::Tensor* mask, framework::Tensor* y) {
  auto& place = *dev_ctx.eigen_device();
  int64_t x_numel = x.numel();
  auto stream = dev_ctx.stream();
  auto* x_data = x.data<T>();
  auto* y_data = y->data<T>();

  if (!is_test) {
    auto* mask_data = mask->data<uint8_t>();
    size_t size = phi::product(mask->dims());

    if (dropout_prob == 1.0f) {
#ifdef PADDLE_WITH_HIP
      PADDLE_ENFORCE_GPU_SUCCESS(
          hipMemsetAsync(y_data, 0, x_numel * sizeof(T), stream));
      PADDLE_ENFORCE_GPU_SUCCESS(
          hipMemsetAsync(mask_data, 0, x_numel * sizeof(*mask_data), stream));
#else
      PADDLE_ENFORCE_GPU_SUCCESS(
          cudaMemsetAsync(y_data, 0, x_numel * sizeof(T), stream));
      PADDLE_ENFORCE_GPU_SUCCESS(
          cudaMemsetAsync(mask_data, 0, x_numel * sizeof(*mask_data), stream));
#endif
      return;
    }

    uint64_t seed_data;
    uint64_t increment;
    // VectorizedRandomGenerator use curand_uniform4, so kVecSize is 4;
    constexpr int kVecSize =
        phi::funcs::uniform_distribution<float>::kReturnsCount;
    auto gpu_config =
        phi::backends::gpu::GetGpuLaunchConfig1D(dev_ctx, x_numel, kVecSize);
    size_t grid_size = gpu_config.GetGridSize();
    size_t block_size = gpu_config.GetBlockSize();

    int64_t device_id = dev_ctx.GetPlace().GetDeviceId();
    const auto& prop = platform::GetDeviceProperties(device_id);
    size_t max_grid_size = prop.maxThreadsPerMultiProcessor *
                           prop.multiProcessorCount / block_size;
    grid_size = std::min(grid_size, max_grid_size);

    auto offset =
        ((x_numel - 1) / (grid_size * block_size * kVecSize) + 1) * kVecSize;
    GetSeedDataAndIncrement(dev_ctx, seed, is_fix_seed, seed_val, offset,
                            &seed_data, &increment);
    size_t main_offset =
        size / (block_size * kVecSize) * (block_size * kVecSize);

    VectorizedRandomGenerator<T, uint8_t><<<grid_size, block_size, 0, stream>>>(
        size, seed_data, dropout_prob, x_data, mask_data, y_data,
        upscale_in_train, increment, main_offset);
  } else {
    if (upscale_in_train) {
// todo: can y share with data with x directly?
#ifdef PADDLE_WITH_HIP
      PADDLE_ENFORCE_GPU_SUCCESS(
          hipMemcpyAsync(y_data, x_data, sizeof(T) * x_numel,
                         hipMemcpyDeviceToDevice, stream));
#else
      PADDLE_ENFORCE_GPU_SUCCESS(
          cudaMemcpyAsync(y_data, x_data, sizeof(T) * x_numel,
                          cudaMemcpyDeviceToDevice, stream));
#endif
    } else {
      using MT = typename details::MPTypeTrait<T>::Type;
      MT factor = static_cast<MT>(1.0f - dropout_prob);
      std::vector<const framework::Tensor*> ins = {&x};
      std::vector<framework::Tensor*> outs = {y};
      auto functor = phi::funcs::ScaleFunctor<T>(factor);
      paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(dev_ctx, ins,
                                                                &outs, functor);
    }
  }
}

template <typename T, typename MaskType>
struct CudaDropoutGradFunctor {
  using MT = typename details::MPTypeTrait<T>::Type;

  explicit CudaDropoutGradFunctor(const MT factor) : factor_(factor) {}

  __device__ __forceinline__ T operator()(const T dout,
                                          const MaskType mask) const {
    return static_cast<T>(static_cast<MT>(dout) * static_cast<MT>(mask) *
                          factor_);
  }

 private:
  MT factor_;
};

template <typename T>
void DropoutGradGPUKernelDriver(const phi::GPUContext& dev_ctx,
                                const std::string dropout_implementation,
                                float dropout_prob,
                                const framework::Tensor& grad_y,
                                const framework::Tensor& mask, int64_t size,
                                framework::Tensor* grad_x,
                                bool is_test = false) {
  using MT = typename details::MPTypeTrait<T>::Type;
  auto stream = dev_ctx.stream();
  MT factor;
  if (is_test) {
    if (dropout_implementation == "upscale_in_train") {
      factor = static_cast<MT>(1.0f);
    } else {
      factor = static_cast<MT>(1.0f - dropout_prob);
    }
    std::vector<const framework::Tensor*> ins = {&grad_y};
    std::vector<framework::Tensor*> outs = {grad_x};
    auto functor = phi::funcs::ScaleFunctor<T>(factor);
    paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(dev_ctx, ins,
                                                              &outs, functor);
  } else {
    std::vector<const framework::Tensor*> ins = {&grad_y, &mask};
    std::vector<framework::Tensor*> outs = {grad_x};
    if (dropout_implementation == "upscale_in_train") {
      if (dropout_prob == 1.0f) {
#ifdef PADDLE_WITH_HIP
        hipMemset(grad_x->data<T>(), 0, size * sizeof(T));
#else
        cudaMemset(grad_x->data<T>(), 0, size * sizeof(T));
#endif
      } else {
        factor = static_cast<MT>(1.0f / (1.0f - dropout_prob));
        paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(
            dev_ctx, ins, &outs, CudaDropoutGradFunctor<T, uint8_t>(factor));
      }
    } else {
      factor = static_cast<MT>(1.0f);
      paddle::operators::LaunchSameDimsElementwiseCudaKernel<T>(
          dev_ctx, ins, &outs, CudaDropoutGradFunctor<T, uint8_t>(factor));
    }
  }
}

}  // namespace operators
}  // namespace paddle