wehub-resource-sync
60 lines
2.0 KiB
C++
60 lines
2.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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/**
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* @brief Given two tensors x and y, compute Lp-norm of (x-y).
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* It is not a norm in a strict sense, only as a measure of distance.
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* The shapes of x and y must be broadcastable. Where, z = x - y,
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*
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* When p = 0, defining $0^0 = 0$, the zero-norm of z is simply
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* the number of non-zero elements of z.
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* $$
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* ||z||_{0} = \lim_{p \rightarrow 0} \sum_{i=1}^{m} |z_i|^p
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* $$
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*
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* When p = inf, the inf-norm of z is the maximum element of z.
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* $$
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* ||z||_\infty=\max_i |z_i|
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* $$
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*
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* When p = -inf, the negative-inf-norm of z is the minimum element of z.
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* $$
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* ||z||_{-\infty}=\min_i |z_i|
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* $$
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*
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* Otherwise, the p-norm of z follows the formula,
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* $$
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* ||z||_{p} = (\sum_{i=i}^{m} |z_i|^p)^{1/p}
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* $$
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* @param ctx device context
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* @param x the input Tensor of Dist
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* @param y the Right-hand-side input Tensor of Dist
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* @param p the norm to be computed
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* @param out the output of Dist, which is the p-norm of (x - y)
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*/
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template <typename T, typename Context>
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void DistKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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float p,
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DenseTensor* out);
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} // namespace phi
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