chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/bilinear_kernel_impl.h

@@ -0,0 +1,74 @@
+// 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"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/utils/optional.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void BilinearKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const DenseTensor& y,
+                    const DenseTensor& weight,
+                    const optional<DenseTensor>& bias,
+                    DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+
+  auto y_mat = EigenMatrix<T>::From(y);
+  auto output_mat = EigenMatrix<T>::From(*out);
+
+  auto batch_size = x.dims()[0];
+  auto weight_dims = weight.dims();
+  int64_t out_dim = weight_dims[0];
+  auto x_dim = weight_dims[1];
+  auto y_dim = weight_dims[2];
+  auto& place = *dev_ctx.eigen_device();
+
+  // Create the intermediate variable to calculate the result of
+  // Input(X) multiplied by Input(Weight_i), the formula is:
+  // left_mul = X Weight_i.
+  DenseTensor left_mul;
+  left_mul.Resize({batch_size, y_dim});
+  dev_ctx.template Alloc<T>(&left_mul);
+  auto left_mul_mat = EigenMatrix<T>::From(left_mul);
+
+  for (int64_t i = 0; i < out_dim; ++i) {
+    auto output_col_vec = output_mat.chip(i, 1);
+    DenseTensor weight_mat = weight.Slice(i, i + 1).Resize({x_dim, y_dim});
+    funcs::GetBlas<Context, T>(dev_ctx).GEMM(CblasNoTrans,
+                                             CblasNoTrans,
+                                             batch_size,
+                                             y_dim,
+                                             x_dim,
+                                             1,
+                                             x.data<T>(),
+                                             weight_mat.data<T>(),
+                                             0,
+                                             left_mul.data<T>());
+    output_col_vec.device(place) =
+        (left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
+  }
+  if (bias.get_ptr()) {
+    auto bias_vec = EigenMatrix<T>::From(*(bias.get_ptr()));
+    Eigen::DSizes<int, 2> bcast(batch_size, 1);
+    output_mat.device(place) = bias_vec.broadcast(bcast) + output_mat;
+  }
+}
+
+}  // namespace phi