// Copyright (c) 2022 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 "paddle/phi/kernels/temporal_shift_grad_kernel.h"

#include "paddle/common/layout.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T>
void TemporalShiftBwNCHW(const T* output_grad,
                         T* input_grad,
                         const int64_t ntchw,
                         const int64_t tchw,
                         const int64_t chw,
                         const int64_t hw,
                         const int t,
                         const int c1,
                         const int c2) {
  int src_it = 0;
  for (int64_t i = 0; i < ntchw; i++) {
    int64_t it = (i % tchw) / chw;
    int64_t ic = (i % chw) / hw;

    if (ic < c1) {
      src_it = it + 1;
    } else if (ic < c2) {
      src_it = it - 1;
    } else {
      src_it = it;
    }

    if (src_it >= 0 && src_it < t) {
      input_grad[i] = output_grad[i + (src_it - it) * chw];
    } else {
      input_grad[i] = 0;
    }
  }
}

template <typename T>
void TemporalShiftBwNHWC(const T* output_grad,
                         T* input_grad,
                         const int64_t nthwc,
                         const int64_t thwc,
                         const int64_t hwc,
                         const int t,
                         const int c,
                         const int c1,
                         const int c2) {
  int src_it = 0;
  for (int64_t i = 0; i < nthwc; i++) {
    int64_t it = (i % thwc) / hwc;
    int64_t ic = i % c;

    if (ic < c1) {
      src_it = it + 1;
    } else if (ic < c2) {
      src_it = it - 1;
    } else {
      src_it = it;
    }

    if (src_it >= 0 && src_it < t) {
      input_grad[i] = output_grad[i + (src_it - it) * hwc];
    } else {
      input_grad[i] = 0;
    }
  }
}

template <typename T, typename Context>
void TemporalShiftGradKernel(const Context& dev_ctx,
                             const DenseTensor& out_grad,
                             int seg_num,
                             float shift_ratio,
                             const std::string& data_format_str,
                             DenseTensor* x_grad) {
  if (x_grad && x_grad->numel() == 0) {
    dev_ctx.template Alloc<T>(x_grad);
    return;
  }
  auto* input_grad = x_grad;
  auto* output_grad = &out_grad;
  int t = seg_num;
  const DataLayout data_layout = StringToDataLayout(data_format_str);

  const int nt = static_cast<int>(output_grad->dims()[0]);
  const int c = static_cast<int>(data_layout == DataLayout::NCHW
                                     ? output_grad->dims()[1]
                                     : output_grad->dims()[3]);
  const int h = static_cast<int>(data_layout == DataLayout::NCHW
                                     ? output_grad->dims()[2]
                                     : output_grad->dims()[1]);
  const int w = static_cast<int>(data_layout == DataLayout::NCHW
                                     ? output_grad->dims()[3]
                                     : output_grad->dims()[2]);

  const int64_t hw = static_cast<int64_t>(h) * w;
  const int64_t chw = static_cast<int64_t>(c) * hw;
  const int64_t tchw = static_cast<int64_t>(t) * chw;
  const int64_t ntchw = static_cast<int64_t>(nt) * chw;

  const int c1 = static_cast<int>(static_cast<float>(c) * shift_ratio);
  const int c2 = static_cast<int>(static_cast<float>(c) * 2.f * shift_ratio);

  DDim in_grad_dims =
      (data_layout == DataLayout::NCHW ? make_ddim({nt, c, h, w})
                                       : make_ddim({nt, h, w, c}));
  const T* output_grad_data = output_grad->data<T>();
  input_grad->Resize(in_grad_dims);

  T* input_grad_data = dev_ctx.template Alloc<T>(input_grad);

  if (data_layout == DataLayout::NCHW) {
    TemporalShiftBwNCHW<T>(
        output_grad_data, input_grad_data, ntchw, tchw, chw, hw, t, c1, c2);
  } else {
    TemporalShiftBwNHWC<T>(
        output_grad_data, input_grad_data, ntchw, tchw, chw, t, c, c1, c2);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(temporal_shift_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::TemporalShiftGradKernel,
                   float,
                   double) {}