wehub-resource-sync
75 lines
2.8 KiB
C++
75 lines
2.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/utils/optional.h"
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namespace phi {
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template <typename T, typename Context>
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void BilinearKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& weight,
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const optional<DenseTensor>& bias,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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auto y_mat = EigenMatrix<T>::From(y);
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auto output_mat = EigenMatrix<T>::From(*out);
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auto batch_size = x.dims()[0];
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auto weight_dims = weight.dims();
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int64_t out_dim = weight_dims[0];
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auto x_dim = weight_dims[1];
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auto y_dim = weight_dims[2];
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auto& place = *dev_ctx.eigen_device();
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// Create the intermediate variable to calculate the result of
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// Input(X) multiplied by Input(Weight_i), the formula is:
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// left_mul = X Weight_i.
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DenseTensor left_mul;
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left_mul.Resize({batch_size, y_dim});
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dev_ctx.template Alloc<T>(&left_mul);
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auto left_mul_mat = EigenMatrix<T>::From(left_mul);
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for (int64_t i = 0; i < out_dim; ++i) {
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auto output_col_vec = output_mat.chip(i, 1);
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DenseTensor weight_mat = weight.Slice(i, i + 1).Resize({x_dim, y_dim});
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funcs::GetBlas<Context, T>(dev_ctx).GEMM(CblasNoTrans,
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CblasNoTrans,
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batch_size,
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y_dim,
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x_dim,
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1,
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x.data<T>(),
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weight_mat.data<T>(),
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0,
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left_mul.data<T>());
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output_col_vec.device(place) =
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(left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
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}
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if (bias.get_ptr()) {
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auto bias_vec = EigenMatrix<T>::From(*(bias.get_ptr()));
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Eigen::DSizes<int, 2> bcast(batch_size, 1);
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output_mat.device(place) = bias_vec.broadcast(bcast) + output_mat;
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}
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}
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} // namespace phi
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