/* 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 <algorithm>

#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/sequence_ops/sequence_expand_op.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"

namespace paddle {
namespace operators {

using LoDTensor = framework::LoDTensor;

template <typename T>
__global__ void sequence_expand_kernel(const T* x_data,
                                       const size_t* x_lod,
                                       const size_t* ref_lod,
                                       const size_t* offset,
                                       const size_t lod_size,
                                       /* default=1,
                                          the instance length*/
                                       const int x_item_length,
                                       T* out_data) {
  int bid = blockIdx.x;
  if (bid >= lod_size - 1) return;

  int x_item_count = x_lod[bid + 1] - x_lod[bid];
  int repeats = ref_lod[bid + 1] - ref_lod[bid];
  int out_offset = static_cast<int>(offset[bid]);
  int x_offset = x_lod[bid];
  for (int tid_z = threadIdx.z; tid_z < repeats; tid_z += blockDim.z) {
    for (int tid_y = threadIdx.y; tid_y < x_item_count; tid_y += blockDim.y) {
      for (int tid_x = threadIdx.x; tid_x < x_item_length;
           tid_x += blockDim.x) {
        out_data[(out_offset + tid_z * x_item_count + tid_y) * x_item_length +
                 tid_x] = x_data[(x_offset + tid_y) * x_item_length + tid_x];
      }
    }
  }
}

template <typename T>
__global__ void sequence_expand_grad_kernel(const T* dout_data,
                                            const size_t* ref_lod,
                                            const size_t* dx_lod,
                                            const size_t* offset,
                                            const size_t lod_size,
                                            /* default=1,
                                               the instance length*/
                                            const int x_item_length,
                                            T* dx_data) {
  int bid = blockIdx.x;
  if (bid >= lod_size - 1) return;
  int x_item_count = dx_lod[bid + 1] - dx_lod[bid];
  int repeats = ref_lod[bid + 1] - ref_lod[bid];
  int out_offset = static_cast<int>(offset[bid]);
  int x_offset = dx_lod[bid];

  for (int tid_z = threadIdx.z; tid_z < repeats; tid_z += blockDim.z) {
    for (int tid_y = threadIdx.y; tid_y < x_item_count; tid_y += blockDim.y) {
      for (int tid_x = threadIdx.x; tid_x < x_item_length;
           tid_x += blockDim.x) {
        platform::CudaAtomicAdd(
            &dx_data[(x_offset + tid_y) * x_item_length + tid_x],
            dout_data[(out_offset + tid_z * x_item_count + tid_y) *
                          x_item_length +
                      tid_x]);
      }
    }
  }
}

void GetOutputOffset(const framework::Vector<size_t>& x_lod,
                     const framework::Vector<size_t>& ref_lod,
                     framework::Vector<size_t>* out_offset) {
  size_t offset = 0;
  int lod_size = static_cast<int>(x_lod.size());
  for (int i = 0; i < static_cast<int>(x_lod.size()); ++i) {
    (*out_offset)[i] = offset;
    if (i < lod_size - 1) {
      offset += (ref_lod[i + 1] - ref_lod[i]) * (x_lod[i + 1] - x_lod[i]);
    }
  }
}

template <typename T>
static int ExpandByMemoryCopy(const platform::CUDADeviceContext& context,
                              const LoDTensor& x,
                              LoDTensor* out,
                              const framework::Vector<size_t>& x_lod,
                              const framework::Vector<size_t>& ref_lod,
                              bool do_copy) {
  auto out_data = out->data<T>();
  auto x_data = x.data<T>();

  const auto& gpu_place = context.GetPlace();

  int x_item_length = x.numel() / x.dims()[0];
  int out_offset = 0;
  int num_copys = 0;
  for (size_t i = 1; i < ref_lod.size(); ++i) {
    int repeat_num = ref_lod[i] - ref_lod[i - 1];
    int x_start = x_lod[i - 1];
    int x_end = x_lod[i];
    int x_seq_len = x_end - x_start;
    if (repeat_num > 0) {
      if (do_copy) {
        int out_start = out_offset;
        if (out->lod().size() == 1) {
          out_start = out->lod()[0][out_offset];
        }
        for (int j = 0; j < repeat_num; j++) {
          for (int k = 0; k < x_seq_len; k++) {
            memory::Copy(
                gpu_place,
                out_data + (out_start + j * x_seq_len + k) * x_item_length,
                gpu_place,
                x_data + (x_start + k) * x_item_length,
                sizeof(T) * x_item_length,
                context.stream());
          }
        }
      } else {
        num_copys += repeat_num * x_seq_len;
      }
    }
    out_offset += repeat_num;
  }
  return num_copys;
}

template <typename T>
struct SequenceExpandFunctor<platform::CUDADeviceContext, T> {
  void operator()(
      const platform::CUDADeviceContext& context,
      const LoDTensor& x,
      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
      LoDTensor* out) {
    int num_copys =
        ExpandByMemoryCopy<T>(context, x, out, x_lod, ref_lod, false);
    // Sometimes direct copies will be faster, this maybe need deeply analysis.
    if (num_copys < 5) {
      ExpandByMemoryCopy<T>(context, x, out, x_lod, ref_lod, true);
    } else {
      int x_item_length = x.numel() / x.dims()[0];
      size_t x_lod_size = x_lod.size();
      framework::Vector<size_t> out_offset(x_lod_size * 2 + ref_lod.size());
      GetOutputOffset(x_lod, ref_lod, &out_offset);

      for (size_t i = 0; i < x_lod_size; ++i) {
        out_offset[x_lod_size + i] = x_lod[i];
      }
      for (size_t i = 0; i < ref_lod.size(); ++i) {
        out_offset[2 * x_lod_size + i] = ref_lod[i];
      }

      paddle::framework::MixVector<size_t> mixv_out_offset(&out_offset);
      const size_t* out_offset_data =
          mixv_out_offset.CUDAData(context.GetPlace());
      const size_t* x_lod_data = out_offset_data + x_lod_size;
      const size_t* ref_lod_data = out_offset_data + 2 * x_lod_size;

      int thread_x =
          std::min(32, std::max(static_cast<int>(ref_lod.size()), 16));
      int thread_y = 16;
      int thread_z = 1024 / thread_x / thread_y;
      int block_x = static_cast<int>(ref_lod.size());
      dim3 block_size(thread_x, thread_y, thread_z);
      dim3 grid_size(block_x, 1);

      sequence_expand_kernel<<<grid_size, block_size, 0, context.stream()>>>(
          x.data<T>(),
          x_lod_data,
          ref_lod_data,
          out_offset_data,
          x_lod_size,
          x_item_length,
          out->mutable_data<T>(context.GetPlace()));
    }
  }
};

template <typename T>
struct SequenceExpandGradFunctor<platform::CUDADeviceContext, T> {
  void operator()(const platform::CUDADeviceContext& context,
                  const LoDTensor& dout,
                  const framework::Vector<size_t>& x_lod, /*expand source lod*/
                  const framework::Vector<size_t>& ref_lod, /*expand based lod*/
                  LoDTensor* dx) {
    int x_item_length = phi::product(dx->dims()) / dx->dims()[0];
    framework::Vector<size_t> out_offset(x_lod.size());
    GetOutputOffset(x_lod, ref_lod, &out_offset);

    int thread_x = std::min(32, std::max(static_cast<int>(ref_lod.size()), 16));
    int thread_y = 16;
    int thread_z = 1024 / thread_x / thread_y;
    int block_x = static_cast<int>(ref_lod.size());
    dim3 block_size(thread_x, thread_y, thread_z);
    dim3 grid_size(block_x, 1);
    paddle::framework::MixVector<size_t> mixv_ref_lod(&ref_lod);
    paddle::framework::MixVector<size_t> mixv_x_lod(&x_lod);
    paddle::framework::MixVector<size_t> mixv_out_offset(&out_offset);
    sequence_expand_grad_kernel<<<grid_size, block_size, 0, context.stream()>>>(
        dout.data<T>(),
        mixv_ref_lod.CUDAData(context.GetPlace()),
        mixv_x_lod.CUDAData(context.GetPlace()),
        mixv_out_offset.CUDAData(context.GetPlace()),
        ref_lod.size(),
        x_item_length,
        dx->mutable_data<T>(context.GetPlace()));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
    sequence_expand,
    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>,
    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, double>,
    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int>,
    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
    sequence_expand_grad,
    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>,
    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, double>,
    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, int>,
    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext,
                                  int64_t>);
