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126 lines
4.5 KiB
126 lines
4.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class SquaredL2DistanceKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in0 = context.Input<Tensor>("X");
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auto* in1 = context.Input<Tensor>("Y");
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auto* out0 = context.Output<Tensor>("sub_result");
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auto* out1 = context.Output<Tensor>("Out");
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auto in0_dims = in0->dims();
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auto in1_dims = in1->dims();
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int cols = in0->numel() / in0_dims[0];
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// reduce dimensions except the first
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auto x =
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EigenMatrix<T>::From(*in0, framework::make_ddim({in0_dims[0], cols}));
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auto y =
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EigenMatrix<T>::From(*in1, framework::make_ddim({in1_dims[0], cols}));
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out0->mutable_data<T>(context.GetPlace());
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out1->mutable_data<T>(context.GetPlace());
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auto sub_result = EigenMatrix<T>::From(*out0);
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auto z = EigenVector<T>::Flatten(*out1);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto x_dims = x.dimensions();
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auto y_dims = y.dimensions();
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// buffer the substraction result
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if (y_dims[0] == 1 && x_dims[0] > y_dims[0]) {
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sub_result.device(place) =
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x -
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y.broadcast(Eigen::array<int, 2>({{static_cast<int>(x_dims[0]), 1}}));
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} else {
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sub_result.device(place) = x - y;
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}
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auto sub_res_pow2 = sub_result * sub_result;
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z.device(place) = sub_res_pow2.sum(Eigen::array<int, 1>({{1}}));
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}
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};
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template <typename DeviceContext, typename T>
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class SquaredL2DistanceGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in0 = context.Input<Tensor>("sub_result");
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auto* in1 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* x_g = context.Output<Tensor>(framework::GradVarName("X"));
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auto* y_g = context.Output<Tensor>(framework::GradVarName("Y"));
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auto sub_result = EigenMatrix<T>::From(*in0);
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auto out_grad = EigenMatrix<T>::From(*in1);
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auto x_dims = x_g->dims();
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auto y_dims = y_g->dims();
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int cols = x_g->numel() / x_dims[0];
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// calculate gradient
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auto grad_mat = 2 *
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(out_grad.broadcast(Eigen::array<int, 2>({{1, cols}}))) *
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sub_result;
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// propagate back to input
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auto& eigen_place =
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*context.template device_context<DeviceContext>().eigen_device();
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if (x_g) {
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x_g->mutable_data<T>(context.GetPlace());
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// eigen matrix
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auto x_grad =
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EigenMatrix<T>::From(*x_g, framework::make_ddim({x_dims[0], cols}));
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// dimensions are same with subResult
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x_grad.device(eigen_place) = grad_mat;
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}
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if (y_g) {
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y_g->mutable_data<T>(context.GetPlace());
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PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0],
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"First dimension of gradient must be greater or "
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"equal than first dimension of target.");
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if (sub_result.dimensions()[0] == y_dims[0]) {
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auto y_grad =
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EigenMatrix<T>::From(*y_g, framework::make_ddim({y_dims[0], cols}));
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y_grad.device(eigen_place) = -1 * grad_mat;
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} else {
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auto col_sum_res = -1 * (grad_mat.sum(Eigen::array<int, 1>({{0}})));
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auto y_grad = EigenVector<T>::Flatten(*y_g);
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y_grad.device(eigen_place) = col_sum_res;
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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