// 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/frobenius_norm_kernel.h"

#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/funcs/activation_functor.h"
#include "paddle/phi/kernels/gpu/reduce.h"

namespace phi {

template <typename T, typename Context>
void FrobeniusNormKernel(const Context& dev_ctx,
                         const DenseTensor& x,
                         const IntArray& dims,
                         bool keep_dim,
                         bool reduce_all,
                         DenseTensor* out) {
  if (x.numel() == 0) {
    dev_ctx.template Alloc<T>(out);
    funcs::SetConstant<Context, T>()(dev_ctx, out, static_cast<T>(0));
    return;
  }
  reduce_all = recompute_reduce_all(x, dims.GetData(), reduce_all);
  auto out_dtype = x.dtype();
  Reduce<T, kps::AddFunctor, kps::SquareFunctor>(
      dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);

  SqrtKernel<T, Context>(dev_ctx, *out, out);
}

}  // namespace phi

PD_REGISTER_KERNEL(frobenius_norm,
                   GPU,
                   ALL_LAYOUT,
                   phi::FrobeniusNormKernel,
                   float,
                   double,
                   phi::complex64,
                   phi::complex128) {}