chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/lerp_kernel_impl.h

@@ -0,0 +1,130 @@
+// 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/backends/cpu/cpu_context.h"
+#include "paddle/phi/common/amp_type_traits.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename Context, typename T, size_t D>
+static void LerpFunction(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& y,
+                         const DenseTensor& weight,
+                         DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  const auto& out_dims = out->dims();
+  auto x_dims = funcs::ExtendDims2Rank(x.dims(), D);
+  auto y_dims = funcs::ExtendDims2Rank(y.dims(), D);
+  auto w_dims = funcs::ExtendDims2Rank(weight.dims(), D);
+  Eigen::DSizes<int, D> x_bcast_dims;
+  Eigen::DSizes<int, D> y_bcast_dims;
+  Eigen::DSizes<int, D> w_bcast_dims;
+  funcs::GetBroadcastDims<D>(x_dims, out_dims, &x_bcast_dims);
+  funcs::GetBroadcastDims<D>(y_dims, out_dims, &y_bcast_dims);
+  funcs::GetBroadcastDims<D>(w_dims, out_dims, &w_bcast_dims);
+
+  auto eigen_x = EigenTensor<T, D>::From(x, x_dims);
+  auto eigen_y = EigenTensor<T, D>::From(y, y_dims);
+  auto eigen_w = EigenTensor<T, D>::From(weight, w_dims);
+  auto eigen_out = EigenTensor<T, D>::From(*out);
+
+  using MPType = typename MPTypeTrait<T>::Type;
+  auto& place = *dev_ctx.eigen_device();
+  eigen_out.device(place) =
+      (eigen_x.broadcast(x_bcast_dims).template cast<MPType>() +
+       eigen_w.broadcast(w_bcast_dims).template cast<MPType>() *
+           (eigen_y.broadcast(y_bcast_dims).template cast<MPType>() -
+            eigen_x.broadcast(x_bcast_dims).template cast<MPType>()))
+          .template cast<T>();
+}
+
+template <typename Context, typename T>
+static void LerpFunctionZero(const Context& dev_ctx,
+                             const DenseTensor& x,
+                             const DenseTensor& y,
+                             const DenseTensor& weight,
+                             DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+
+  auto dim = make_ddim(std::vector<int64_t>(1, 1));
+  auto eigen_x = EigenTensor<T, 1>::From(x, dim);
+  auto eigen_y = EigenTensor<T, 1>::From(y, dim);
+  auto eigen_w = EigenTensor<T, 1>::From(weight, dim);
+  auto eigen_out = EigenTensor<T, 1>::From(*out, dim);
+
+  using MPType = typename MPTypeTrait<T>::Type;
+  auto& place = *dev_ctx.eigen_device();
+  eigen_out.device(place) =
+      (eigen_x.template cast<MPType>() +
+       eigen_w.template cast<MPType>() *
+           (eigen_y.template cast<MPType>() - eigen_x.template cast<MPType>()))
+          .template cast<T>();
+}
+
+template <typename T, typename Context>
+void LerpKernel(const Context& dev_ctx,
+                const DenseTensor& x,
+                const DenseTensor& y,
+                const DenseTensor& weight,
+                DenseTensor* out) {
+  if (out && out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  int rank = out->dims().size();
+  PADDLE_ENFORCE_GE(
+      rank,
+      0,
+      common::errors::InvalidArgument(
+          "The number of dimensions for LerpOp must be "
+          "greater than or equal to 0, but the value received is %d.",
+          rank));
+  PADDLE_ENFORCE_LE(
+      rank,
+      6,
+      common::errors::InvalidArgument(
+          "The number of dimensions for LerpOp must be "
+          "less than or equal to 6, but the value received is %d.",
+          rank));
+  switch (rank) {
+    case 0:
+      LerpFunctionZero<Context, T>(dev_ctx, x, y, weight, out);
+      break;
+    case 1:
+      LerpFunction<Context, T, 1>(dev_ctx, x, y, weight, out);
+      break;
+    case 2:
+      LerpFunction<Context, T, 2>(dev_ctx, x, y, weight, out);
+      break;
+    case 3:
+      LerpFunction<Context, T, 3>(dev_ctx, x, y, weight, out);
+      break;
+    case 4:
+      LerpFunction<Context, T, 4>(dev_ctx, x, y, weight, out);
+      break;
+    case 5:
+      LerpFunction<Context, T, 5>(dev_ctx, x, y, weight, out);
+      break;
+    case 6:
+      LerpFunction<Context, T, 6>(dev_ctx, x, y, weight, out);
+      break;
+  }
+}
+
+}  // namespace phi