/* 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. */

#define EIGEN_USE_GPU
#include <stdio.h>
#include "paddle/operators/crop_op.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename T, int D>
__global__ void CropKernel(const int N, const int64_t* out_shape,
                           const int64_t* x_shape, const int* crop_rules,
                           const T* x_data, T* out_data) {
  int64_t pos[D];
  int tmp;
  int64_t x_index;
  for (int out_index = blockIdx.x * blockDim.x + threadIdx.x; out_index < N;
       out_index += blockDim.x * gridDim.x) {
    tmp = out_index;
    for (int64_t i = D - 1; i >= 0; --i) {
      pos[i] = (tmp % out_shape[i]) + crop_rules[i * 2];
      tmp = tmp / out_shape[i];
    }

    x_index = pos[0];
    for (size_t i = 1; i < D; ++i) {
      x_index = x_index * x_shape[i] + pos[i];
    }
    out_data[out_index] = x_data[x_index];
  }
}

template <typename T, int D>
void CropCUDAFunctoin(const framework::ExecutionContext& context) {
  PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
                 "It must use GPUPlace.");
  LOG(INFO) << "CropCUDAFunctoin step1";
  auto* x = context.Input<Tensor>("X");
  auto* out = context.Output<Tensor>("Out");
  auto x_data = x->data<T>();
  T* out_data = out->mutable_data<T>(paddle::platform::GPUPlace());
  auto x_dims = x->dims();
  auto out_dims = out->dims();
  int64_t out_count = framework::product(out_dims);
  int64_t x_shape[D];
  int64_t out_shape[D];
  for (int i = 0; i < D; ++i) {
    x_shape[i] = x_dims[i];
    out_shape[i] = out_dims[i];
  }
  int64_t* x_shape_gpu;
  int64_t* out_shape_gpu;
  cudaMalloc((void**)&x_shape_gpu, sizeof(int64_t) * D);
  cudaMemcpy(x_shape_gpu, x_shape, sizeof(int64_t) * D, cudaMemcpyHostToDevice);
  cudaMalloc((void**)&out_shape_gpu, sizeof(int64_t) * D);
  cudaMemcpy(out_shape_gpu, out_shape, sizeof(int64_t) * D,
             cudaMemcpyHostToDevice);
  auto offsets = context.op().Attr<std::vector<int>>("offsets");
  PADDLE_ENFORCE_EQ(
      D, offsets.size(),
      "Offsets size should be equal to dimension size of input tensor.");

  int crop_rules[D * 2];
  for (size_t i = 0; i < x_dims.size(); ++i) {
    crop_rules[i * 2] = offsets[i];
    crop_rules[i * 2 + 1] = x_dims[i] - out_dims[i] - offsets[i];
  }

  int* crop_rules_gpu;
  cudaMalloc((void**)&crop_rules_gpu, sizeof(int) * D * 2);
  cudaMemcpy(crop_rules_gpu, crop_rules, sizeof(int) * D * 2,
             cudaMemcpyHostToDevice);

  int n = out_dims[0];
  int d = out_dims[1];
  int block = 512;
  int grid = (n * d + block - 1) / block;
  CropKernel<T, D><<<grid, block>>>(out_count, out_shape_gpu, x_shape_gpu,
                                    crop_rules_gpu, x_data, out_data);
  cudaFree(crop_rules_gpu);
  cudaFree(x_shape_gpu);
  cudaFree(out_shape_gpu);
}

template <typename T>
class CropOpCUDAKernel : public framework::OpKernel {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    size_t rank = context.Input<Tensor>("X")->dims().size();
    switch (rank) {
      case 1:
        CropCUDAFunctoin<T, 1>(context);
        break;
      case 2:
        CropCUDAFunctoin<T, 2>(context);
        break;
      case 3:
        CropCUDAFunctoin<T, 3>(context);
        break;
      case 4:
        CropCUDAFunctoin<T, 4>(context);
        break;
      case 5:
        CropCUDAFunctoin<T, 5>(context);
        break;
      case 6:
        CropCUDAFunctoin<T, 6>(context);
        break;
      default:
        PADDLE_THROW(
            "CropOp only support tensors with no more than 6 dimensions.");
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(crop, ops::CropOpCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(crop_grad,
                       ops::CropGradKernel<paddle::platform::GPUPlace, float>);