// 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/put_along_axis_grad_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/funcs/gather_scatter_functor.h"

namespace phi {

template <typename T, typename Context>
void PutAlongAxisGradKernel(const Context& dev_ctx,
                            const DenseTensor& x,
                            const DenseTensor& index,
                            const DenseTensor& value,
                            const DenseTensor& out,
                            const DenseTensor& out_grad,
                            int axis,
                            const std::string& reduce,
                            bool include_self,
                            DenseTensor* x_grad,
                            DenseTensor* value_grad) {
  if (x.numel() == 0) {
    if (x_grad) {
      dev_ctx.template Alloc<T>(x_grad);
    }
    if (value_grad) {
      dev_ctx.template Alloc<T>(value_grad);
    }
    return;
  }
  const auto& index_type = index.dtype();
  if (x_grad) {
    Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
    if (include_self == false || reduce == "assign") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_input_grad_kernel<T, int32_t>(
            // Here passing an unused argument out_grad, because it's
            // convenient to instantiate a bunch of template function with the
            // same arguments list.
            out_grad,
            axis,
            index,
            *x_grad,
            include_self,
            dev_ctx);
      } else {
        funcs::cpu_scatter_input_grad_kernel<T, int64_t>(
            out_grad, axis, index, *x_grad, include_self, dev_ctx);
      }
    } else if (reduce == "multiply" || reduce == "mul" || reduce == "amin" ||
               reduce == "amax") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_mul_min_max_input_grad_kernel<T, int32_t>(
            out_grad,
            axis,
            index,
            out,
            x,
            value,
            *x_grad,
            reduce,
            include_self,
            dev_ctx);
      } else {
        funcs::cpu_scatter_mul_min_max_input_grad_kernel<T, int64_t>(
            out_grad,
            axis,
            index,
            out,
            x,
            value,
            *x_grad,
            reduce,
            include_self,
            dev_ctx);
      }
    } else if (reduce == "mean") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_mean_input_grad_kernel<T, int32_t>(
            // Here passing an unused argument out_grad, because it's
            // convenient to instantiate a bunch of template function with the
            // same arguments list.
            out_grad,
            axis,
            index,
            *x_grad,
            include_self,
            dev_ctx);
      } else {
        funcs::cpu_scatter_mean_input_grad_kernel<T, int64_t>(
            out_grad, axis, index, *x_grad, include_self, dev_ctx);
      }
    }
  }

  if (value_grad) {
    value_grad->Resize(index.dims());
    dev_ctx.template Alloc<T>(value_grad);
    auto* grad_data = value_grad->data<T>();
    int64_t grad_size = value_grad->numel();
    memset(grad_data, 0, sizeof(T) * grad_size);
    if (reduce == "assign") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_value_grad_kernel<T, int32_t>(
            out_grad, axis, index, *value_grad, include_self, dev_ctx);
      } else if (index_type == DataType::INT64) {
        funcs::cpu_scatter_value_grad_kernel<T, int64_t>(
            out_grad, axis, index, *value_grad, include_self, dev_ctx);
      }
    } else if (reduce == "add" || reduce == "mean") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_add_mean_value_grad_kernel<T, int32_t>(out_grad,
                                                                  axis,
                                                                  index,
                                                                  out,
                                                                  x,
                                                                  value,
                                                                  *value_grad,
                                                                  reduce,
                                                                  include_self,
                                                                  dev_ctx);
      } else {
        funcs::cpu_scatter_add_mean_value_grad_kernel<T, int64_t>(out_grad,
                                                                  axis,
                                                                  index,
                                                                  out,
                                                                  x,
                                                                  value,
                                                                  *value_grad,
                                                                  reduce,
                                                                  include_self,
                                                                  dev_ctx);
      }
    } else if (reduce == "mul" || reduce == "multiply" || reduce == "amin" ||
               reduce == "amax") {
      if (index_type == DataType::INT32) {
        funcs::cpu_scatter_mul_min_max_value_grad_kernel<T, int32_t>(
            out_grad,
            axis,
            index,
            out,
            x,
            value,
            *value_grad,
            reduce,
            include_self,
            dev_ctx);
      } else {
        funcs::cpu_scatter_mul_min_max_value_grad_kernel<T, int64_t>(
            out_grad,
            axis,
            index,
            out,
            x,
            value,
            *value_grad,
            reduce,
            include_self,
            dev_ctx);
      }
    }
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(put_along_axis_grad,
                   CPU,
                   ALL_LAYOUT,
                   phi::PutAlongAxisGradKernel,
                   float,
                   double,
                   int,
                   int16_t,
                   uint8_t,
                   int64_t) {}