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108 lines
4.4 KiB
108 lines
4.4 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 EigenMatrix = framework::EigenMatrix<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 EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename Place, typename T>
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class CosSimKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input_x = context.Input<Tensor>("X");
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auto* input_y = context.Input<Tensor>("Y");
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auto* output_z = context.Output<Tensor>("Out");
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auto* output_x_norm = context.Output<Tensor>("XNorm");
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auto* output_y_norm = context.Output<Tensor>("YNorm");
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output_z->mutable_data<T>(context.GetPlace());
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output_x_norm->mutable_data<T>(context.GetPlace());
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output_y_norm->mutable_data<T>(context.GetPlace());
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auto dims = input_x->dims();
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int64_t size = input_x->numel();
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auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
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auto x = EigenMatrix<T>::From(*input_x, new_dims);
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auto y = EigenMatrix<T>::From(*input_y, new_dims);
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auto z = EigenVector<T>::Flatten(*output_z);
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auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
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auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
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auto place = context.GetEigenDevice<Place>();
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auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
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x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
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y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
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z.device(place) = xy / x_norm / y_norm;
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}
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};
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template <typename Place, typename T>
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class CosSimGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input_x = context.Input<Tensor>("X");
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auto* input_y = context.Input<Tensor>("Y");
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auto* input_z = context.Input<Tensor>("Out");
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auto* input_x_norm = context.Input<Tensor>("XNorm");
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auto* input_y_norm = context.Input<Tensor>("YNorm");
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auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
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auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
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auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
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auto dims = input_x->dims();
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int64_t size = input_x->numel();
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auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
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auto x = EigenMatrix<T>::From(*input_x, new_dims);
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auto y = EigenMatrix<T>::From(*input_y, new_dims);
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auto z = EigenMatrix<T>::From(*input_z);
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auto x_norm = EigenMatrix<T>::From(*input_x_norm);
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auto y_norm = EigenMatrix<T>::From(*input_y_norm);
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auto dz = EigenMatrix<T>::From(*input_grad_z);
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Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
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auto z_bcast = z.broadcast(bcast);
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auto dz_bcast = dz.broadcast(bcast);
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auto place = context.GetEigenDevice<Place>();
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auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
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auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
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auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
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if (output_grad_x) {
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output_grad_x->mutable_data<T>(context.GetPlace());
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auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
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dx.device(place) =
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dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
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}
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if (output_grad_y) {
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output_grad_y->mutable_data<T>(context.GetPlace());
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auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
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dy.device(place) =
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dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
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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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