// 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/tril_triu_grad_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T, typename Context>
void TrilTriuGradKernel(const Context& dev_ctx,
                        const DenseTensor& out_grad,
                        int diagonal,
                        bool lower,
                        DenseTensor* x_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;
  dev_ctx.template Alloc<T>(x_grad);
  auto dy_shape = vectorize<int64_t>(out_grad.dims());
  int r = 0;
  if (lower) {
    r = xpu::tril(dev_ctx.x_context(),
                  reinterpret_cast<const XPUType*>(out_grad.data<T>()),
                  reinterpret_cast<XPUType*>(x_grad->data<T>()),
                  dy_shape,
                  static_cast<int64_t>(diagonal));
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "tril_op");
  } else {
    r = xpu::triu(dev_ctx.x_context(),
                  reinterpret_cast<const XPUType*>(out_grad.data<T>()),
                  reinterpret_cast<XPUType*>(x_grad->data<T>()),
                  dy_shape,
                  static_cast<int64_t>(diagonal));
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "triu_op");
  }
}

template <typename T, typename Context>
void TrilGradKernel(const Context& dev_ctx,
                    const DenseTensor& out_grad,
                    int diagonal,
                    DenseTensor* x_grad) {
  if (x_grad && x_grad->numel() == 0) {
    dev_ctx.template Alloc<T>(x_grad);
    return;
  }

  TrilTriuGradKernel<T, Context>(dev_ctx, out_grad, diagonal, true, x_grad);
}

template <typename T, typename Context>
void TriuGradKernel(const Context& dev_ctx,
                    const DenseTensor& out_grad,
                    int diagonal,
                    DenseTensor* x_grad) {
  if (x_grad && x_grad->numel() == 0) {
    dev_ctx.template Alloc<T>(x_grad);
    return;
  }

  TrilTriuGradKernel<T, Context>(dev_ctx, out_grad, diagonal, false, x_grad);
}

}  // namespace phi

PD_REGISTER_KERNEL(tril_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::TrilGradKernel,
                   int,
                   int64_t,
                   float,
                   phi::float16,
                   phi::bfloat16,
                   bool) {}
PD_REGISTER_KERNEL(triu_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::TriuGradKernel,
                   int,
                   int64_t,
                   float,
                   phi::float16,
                   phi::bfloat16,
                   bool) {}
PD_REGISTER_KERNEL(tril_triu_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::TrilTriuGradKernel,
                   int,
                   int64_t,
                   float,
                   phi::float16,
                   phi::bfloat16,
                   bool) {}