chore: import upstream snapshot with attribution

paddle/phi/kernels/dist_kernel.h

@@ -0,0 +1,59 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+
+/**
+ * @brief Given two tensors x and y, compute Lp-norm of (x-y).
+ *        It is not a norm in a strict sense, only as a measure of distance.
+ *        The shapes of x and y must be broadcastable. Where, z = x - y,
+ *
+ *        When p = 0, defining $0^0 = 0$, the zero-norm of z is simply
+ *        the number of non-zero elements of z.
+ *        $$
+ *        ||z||_{0} = \lim_{p \rightarrow 0} \sum_{i=1}^{m} |z_i|^p
+ *        $$
+ *
+ *        When p = inf, the inf-norm of z is the maximum element of z.
+ *        $$
+ *        ||z||_\infty=\max_i |z_i|
+ *        $$
+ *
+ *        When p = -inf, the negative-inf-norm of z is the minimum element of z.
+ *        $$
+ *        ||z||_{-\infty}=\min_i |z_i|
+ *        $$
+ *
+ *        Otherwise, the p-norm of z follows the formula,
+ *        $$
+ *        ||z||_{p} = (\sum_{i=i}^{m} |z_i|^p)^{1/p}
+ *        $$
+ * @param  ctx     device context
+ * @param  x       the input Tensor of Dist
+ * @param  y       the Right-hand-side input Tensor of Dist
+ * @param  p       the norm to be computed
+ * @param  out     the output of Dist, which is the p-norm of (x - y)
+ */
+template <typename T, typename Context>
+void DistKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const DenseTensor& y,
+                float p,
+                DenseTensor* out);
+
+}  // namespace phi