// 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/infermeta/unary.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/diag_kernel.h"
#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/impl/diag_embed_impl.h"
#include "paddle/phi/kernels/matmul_kernel.h"
#include "paddle/phi/kernels/slice_kernel.h"
#include "paddle/phi/kernels/svd_kernel.h"
#include "paddle/phi/kernels/transpose_kernel.h"

namespace phi {

template <typename T, typename Context>
void SvdvalsGradKernel(const Context& dev_ctx,
                       const DenseTensor& x,
                       const DenseTensor& s_grad,
                       DenseTensor* x_grad) {
  if (x_grad && x_grad->numel() == 0) {
    dev_ctx.template Alloc<T>(x_grad);
    return;
  }
  auto x_dims = x.dims();
  int64_t rows = x_dims[x_dims.size() - 2];
  int64_t cols = x_dims[x_dims.size() - 1];
  int64_t batches = x.numel() / (rows * cols);
  DenseTensor dX_term;
  if (batches == 1) {
    dX_term = Diag<T, Context>(dev_ctx, s_grad, 0, 0);
  } else {
    MetaTensor meta_dX(&dX_term);
    DiagEmbedInferMeta(s_grad, 0, -1, -2, &meta_dX);
    DiagEmbedKernel<T, Context>(dev_ctx, s_grad, 0, -1, -2, &dX_term);
  }

  DenseTensor U, VH, S_recomputed;
  MetaTensor meta_u(&U), meta_s(&S_recomputed), meta_vh(&VH);
  SvdInferMeta(x, false, &meta_u, &meta_s, &meta_vh);
  SvdKernel<T, Context>(dev_ctx,
                        x,
                        false,
                        &U,
                        &S_recomputed,
                        &VH);  // Crucial: recomputing SVD
  *x_grad =
      Matmul<T, Context>(dev_ctx, Matmul<T, Context>(dev_ctx, U, dX_term), VH);
}
}  // namespace phi