chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/norm_kernel.cc

@@ -0,0 +1,81 @@
+// 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/norm_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void NormKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                int axis,
+                float epsilon,
+                bool is_test,
+                DenseTensor* out,
+                DenseTensor* norm) {
+  auto xdim = x.dims();
+  T eps = epsilon;
+  if (axis < 0) axis = xdim.size() + axis;
+  int64_t pre = 0, n = 0, post = 0;
+  funcs::GetPrePostNumel(xdim, axis, &pre, &n, &post);
+
+  DenseTensor* out_norm = nullptr;
+  DenseTensor out_norm_tmp;
+  if (is_test) {
+    auto out_dim = x.dims();
+    out_dim[axis] = 1;
+    out_norm = &out_norm_tmp;
+    out_norm->Resize(out_dim);
+  } else {
+    out_norm = norm;
+  }
+
+  dev_ctx.template Alloc<T>(out);
+  dev_ctx.template Alloc<T>(out_norm);
+
+  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(x);
+  auto y_e = EigenVector<T>::Flatten(*out);
+  auto norm_e = EigenVector<T>::Flatten(*out_norm);
+  auto x_r = x_e.reshape(shape);
+  auto y = y_e.reshape(shape);
+  auto norm_reshape = norm_e.reshape(norm_shape);
+
+  Eigen::DSizes<int, 1> rdim(1);
+  // y = x / sqrt((sum(x * x) + epsilon))
+  // norm = sqrt(sum(x * x) + epsilon)
+  auto x2 = x_r * x_r;
+  auto sum = x2.sum(rdim) + eps;
+  norm_reshape.device(*place) = sum.sqrt();
+
+  // y = x / norm
+  Eigen::DSizes<int64_t, 3> rshape(pre, static_cast<int64_t>(1), post);
+  Eigen::DSizes<int64_t, 3> bcast(
+      static_cast<int64_t>(1), n, static_cast<int64_t>(1));
+  y.device(*place) = x_r / norm_reshape.reshape(rshape).broadcast(bcast);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(norm, CPU, ALL_LAYOUT, phi::NormKernel, float, double) {}