// Copyright (c) 2024 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.

#pragma once

#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/device_context.h"
#include "paddle/phi/kernels/full_kernel.h"

namespace phi {

template <typename T, typename Context>
void SwiGLUGradKernelImpl(const Context &dev_ctx,
                          const T *x,
                          const T *y,
                          const T *dz,
                          T *dx,
                          T *dy,
                          int64_t m,
                          int64_t n);

template <typename T, typename Context>
void SwiGLUGradKernel(const Context &dev_ctx,
                      const DenseTensor &x,
                      const optional<DenseTensor> &y,
                      const DenseTensor &dz,
                      DenseTensor *dx,
                      DenseTensor *dy) {
  if (dx && dx->numel() == 0) {
    dev_ctx.template Alloc<T>(dx);
    if (dy) {
      Full<T, Context>(dev_ctx, dy->dims(), 0, dy);
    }
    return;
  }

  if (dy && dy->numel() == 0) {
    dev_ctx.template Alloc<T>(dy);
    if (dx) {
      Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
    }
    return;
  }

  const auto *x_ptr = x.data<T>();
  const auto *dz_ptr = dz.data<T>();
  auto *dx_ptr = dx ? dev_ctx.template Alloc<T>(dx) : nullptr;
  auto *dy_ptr = y && dy ? dev_ctx.template Alloc<T>(dy) : nullptr;
  const auto &dims = x.dims();

  if (y) {
    const auto &y_tensor = y.get();
    const auto &y_dims = y_tensor.dims();
    const auto &dz_dims = dz.dims();
    PADDLE_ENFORCE_EQ(y_dims,
                      dims,
                      common::errors::InvalidArgument(
                          "The shape of Input(Y):[%s] must be equal "
                          "to the shape of Input(X):[%s].",
                          y_dims,
                          dims));
    PADDLE_ENFORCE_EQ(dz_dims,
                      dims,
                      common::errors::InvalidArgument(
                          "The shape of Input(dz):[%s] must be equal "
                          "to the shape of Input(X):[%s].",
                          dz_dims,
                          dims));
    SwiGLUGradKernelImpl<T, Context>(dev_ctx,
                                     x_ptr,
                                     y_tensor.data<T>(),
                                     dz_ptr,
                                     dx_ptr,
                                     dy_ptr,
                                     x.numel(),
                                     1);
  } else {
    auto dims_2d = flatten_to_2d(dims, dims.size() - 1);
    int64_t m = dims_2d[0], n = dims_2d[1];
    PADDLE_ENFORCE_EQ(n % 2,
                      0,
                      common::errors::InvalidArgument(
                          "The last dim of Input(X) should be exactly divided "
                          "by 2 when Input(Y) is None, but got %d",
                          n));
    const auto &dz_dims = dz.dims();
    PADDLE_ENFORCE_EQ(
        dz_dims.size(),
        dims.size(),
        common::errors::InvalidArgument(
            "The rank of Input(dz):[%d] must be equal to the rank of "
            "Input(X):[%d] when Input(Y) is None.",
            dz_dims.size(),
            dims.size()));
    for (int i = 0; i < dims.size() - 1; ++i) {
      PADDLE_ENFORCE_EQ(dz_dims[i],
                        dims[i],
                        common::errors::InvalidArgument(
                            "The shape of Input(dz):[%s] must be equal to "
                            "the shape of Input(X):[%s] except the last dim "
                            "when Input(Y) is None.",
                            dz_dims,
                            dims));
    }
    PADDLE_ENFORCE_EQ(
        dz_dims[dz_dims.size() - 1],
        n / 2,
        common::errors::InvalidArgument(
            "The last dim of Input(dz):[%d] must be equal to half of the "
            "last dim of Input(X):[%d] when Input(Y) is None.",
            dz_dims[dz_dims.size() - 1],
            n));
    SwiGLUGradKernelImpl<T, Context>(
        dev_ctx, x_ptr, nullptr, dz_ptr, dx_ptr, nullptr, m, n / 2);
  }
}

}  // namespace phi