chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/p_norm_kernel.cc

@@ -0,0 +1,96 @@
+// 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/p_norm_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+inline void GetDims(
+    const DDim& dim, int axis, int* pre, int* n, int* post, bool asvector) {
+  *pre = 1;
+  *post = 1;
+  *n = static_cast<int>(dim[axis]);
+  if (asvector) {
+    *n = static_cast<int>(product(dim));
+  } else {
+    for (int i = 0; i < axis; ++i) {
+      (*pre) *= static_cast<int>(dim[i]);
+    }
+    for (int i = axis + 1; i < dim.size(); ++i) {
+      (*post) *= static_cast<int>(dim[i]);
+    }
+  }
+}
+
+template <typename T, typename Context>
+void PNormKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 double porder,
+                 int axis,
+                 float epsilon UNUSED,
+                 bool keepdim UNUSED,
+                 bool asvector,
+                 DenseTensor* out) {
+  auto* in_x = &x;
+  dev_ctx.template Alloc<T>(out);
+
+  auto xdim = in_x->dims();
+  if (axis < 0) axis = xdim.size() + axis;
+  int pre = 0, n = 0, post = 0;
+  GetDims(xdim, axis, &pre, &n, &post, asvector);
+
+  if (x.numel() == 0) {
+    if (out->numel() > 0) {
+      std::vector<int64_t> vec_dims = vectorize(out->dims());
+      Full<T, Context>(dev_ctx, vec_dims, static_cast<T>(0), out);
+    }
+    return;
+  }
+
+  auto* place = dev_ctx.eigen_device();
+
+  Eigen::DSizes<int64_t, 3> shape(pre, n, post);
+  Eigen::DSizes<int64_t, 2> norm_shape(pre, post);
+
+  auto x_e = EigenVector<T>::Flatten(*in_x);
+  auto norm_e = EigenVector<T>::Flatten(*out);
+
+  auto xr = x_e.reshape(shape);
+  auto norm = norm_e.reshape(norm_shape);
+
+  // p=0 means number of non-zero elements of (xr)
+  // p=inf means the maximum of |xr|
+  // p=-inf means the minimum of |xr|
+  // otherwise, Lp-norm = pow(sum(pow(|xr|, p)), 1/p)
+  Eigen::DSizes<int, 1> rdim(1);
+  if (porder == 0) {
+    norm.device(*place) = (xr != xr.constant(0)).template cast<T>().sum(rdim);
+  } else if (porder == INFINITY) {
+    norm.device(*place) = xr.abs().maximum(rdim);
+  } else if (porder == -INFINITY) {
+    norm.device(*place) = xr.abs().minimum(rdim);
+  } else {
+    norm.device(*place) = xr.abs().pow(porder).sum(rdim).pow(1.0 / porder);
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(p_norm, CPU, ALL_LAYOUT, phi::PNormKernel, float, double) {}