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// 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) {}