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252 lines
8.2 KiB
252 lines
8.2 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/operators/math/math_function.h"
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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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using DDim = framework::DDim;
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template <typename T, size_t D, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
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struct SumFunctor {
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template <typename Place, typename In, typename Out, typename Dim>
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void operator()(const Place& place, In& in, Out& out, const Dim& dim) {
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out.device(place) = in.sum(dim);
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}
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};
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struct SumGradFunctor {
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template <typename Place, typename In, typename In_Const, typename Out,
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typename Dim>
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void operator()(const Place& place, In_Const& in, In& in_grad, Out& out,
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Out& out_grad, const Dim& dim, int size) {
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in_grad.device(place) = out_grad.broadcast(dim);
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}
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};
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struct MeanFunctor {
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template <typename Place, typename In, typename Out, typename Dim>
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void operator()(const Place& place, In& in, Out& out, const Dim& dim) {
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out.device(place) = in.mean(dim);
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}
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};
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struct MeanGradFunctor {
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template <typename Place, typename In, typename In_Const, typename Out,
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typename Dim>
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void operator()(const Place& place, In_Const& in, In& in_grad, Out& out,
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Out& out_grad, const Dim& dim, int size) {
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in_grad.device(place) = out_grad.broadcast(dim) / in_grad.constant(size);
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}
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};
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struct MaxFunctor {
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template <typename Place, typename In, typename Out, typename Dim>
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void operator()(const Place& place, In& in, Out& out, const Dim& dim) {
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out.device(place) = in.maximum(dim);
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}
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};
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struct MinFunctor {
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template <typename Place, typename In, typename Out, typename Dim>
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void operator()(const Place& place, In& in, Out& out, const Dim& dim) {
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out.device(place) = in.minimum(dim);
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}
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};
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struct MaxOrMinGradFunctor {
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template <typename Place, typename In, typename In_Const, typename Out,
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typename Dim>
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void operator()(const Place& place, In_Const& in, In& in_grad, Out& out,
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Out& out_grad, const Dim& dim, int size) {
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auto equals = in == out.broadcast(dim);
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auto ones = in_grad.constant(1);
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auto zeros = in_grad.constant(0);
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in_grad.device(place) =
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out_grad.broadcast(dim) * equals.select(ones, zeros);
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}
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};
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template <typename Place, typename T, typename Functor>
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class ReduceKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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int rank = context.Input<Tensor>("X")->dims().size();
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switch (rank) {
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case 1:
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ReduceCompute<1>(context);
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break;
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case 2:
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ReduceCompute<2>(context);
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break;
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case 3:
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ReduceCompute<3>(context);
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break;
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case 4:
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ReduceCompute<4>(context);
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break;
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case 5:
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ReduceCompute<5>(context);
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break;
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case 6:
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ReduceCompute<6>(context);
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break;
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}
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}
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private:
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template <size_t D>
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void ReduceCompute(const framework::ExecutionContext& context) const {
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auto* input = context.Input<Tensor>("X");
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auto* output = context.Output<Tensor>("Out");
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output->mutable_data<T>(context.GetPlace());
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auto x = EigenTensor<T, D>::From(*input);
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auto x_rank = static_cast<int>(x.dimensions().size());
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int dim = static_cast<int>(context.Attr<int>("dim"));
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if (dim < 0) dim = x_rank + dim;
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auto reduce_dim = Eigen::array<int, 1>({{dim}});
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// construct the squeezed output tensor
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bool keep_dim = context.Attr<int>("keep_dim") == 1;
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DDim dims = output->dims();
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auto dims_vector = vectorize(dims);
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if (keep_dim && x_rank > 1) {
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dims_vector.erase(dims_vector.begin() + dim);
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dims = framework::make_ddim(dims_vector);
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}
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auto out = EigenTensor < T, D == 1 ? 1 : (D - 1) > ::From(*output, dims);
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auto& place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, out, reduce_dim);
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}
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};
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template <typename Place, typename T, typename Functor>
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class ReduceGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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int rank = context.Input<Tensor>("X")->dims().size();
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switch (rank) {
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case 1:
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ReduceCompute<1>(context);
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break;
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case 2:
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ReduceCompute<2>(context);
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break;
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case 3:
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ReduceCompute<3>(context);
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break;
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case 4:
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ReduceCompute<4>(context);
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break;
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case 5:
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ReduceCompute<5>(context);
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break;
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case 6:
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ReduceCompute<6>(context);
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break;
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}
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}
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private:
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template <size_t D>
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void ReduceCompute(const framework::ExecutionContext& context) const {
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auto* input0 = context.Input<Tensor>("X");
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auto* input1 = context.Input<Tensor>("Out");
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auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* output = context.Output<Tensor>(framework::GradVarName("X"));
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if (output != nullptr) {
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output->mutable_data<T>(context.GetPlace());
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auto x = EigenTensor<T, D>::From(*input0);
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auto x_grad = EigenTensor<T, D>::From(*output);
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auto x_rank = static_cast<int>(x.dimensions().size());
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int dim = static_cast<int>(context.Attr<int>("dim"));
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if (dim < 0) dim = x_rank + dim;
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DDim dims = input0->dims();
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dims[dim] = 1;
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auto x_reduce = EigenTensor<T, D>::From(*input1, dims);
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auto x_reduce_grad = EigenTensor<T, D>::From(*input2, dims);
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Eigen::array<int, D> braodcast_dim;
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for (size_t i = 0; i < D; ++i) braodcast_dim[i] = 1;
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braodcast_dim[dim] = input0->dims()[dim];
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auto& place = context.GetEigenDevice<Place>();
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Functor functor;
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functor(place, x, x_grad, x_reduce, x_reduce_grad, braodcast_dim,
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braodcast_dim[dim]);
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}
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}
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};
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// For EigenTensor unsupported reduce
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template <typename T, typename Functor>
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class ReduceGradEigenFreeKernel : 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* x = context.Input<Tensor>("X");
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auto* out = context.Input<Tensor>("Out");
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auto* x_grad = context.Output<Tensor>(framework::GradVarName("X"));
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auto* out_grad = context.Input<Tensor>(framework::GradVarName("Out"));
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if (x_grad != nullptr) {
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DDim dims = x->dims();
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int rank = dims.size();
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int dim = static_cast<int>(context.Attr<int>("dim"));
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if (dim < 0) dim = rank + dim;
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auto* x_data = x->data<T>();
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auto* x_grad_data = x_grad->mutable_data<T>(context.GetPlace());
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auto* out_data = out->data<T>();
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auto* out_grad_data = out_grad->data<T>();
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int outer_count = 1;
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int inner_count = 1;
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int mid_count = dims[dim];
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for (int i = 0; i < dim; ++i) {
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outer_count *= dims[i];
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}
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for (int i = dim + 1; i < rank; ++i) {
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inner_count *= dims[i];
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}
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int x_offset = 0; // offset on raw data
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int out_offset = 0; // offset on reduced data
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Functor functor;
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for (int i = 0; i < outer_count; ++i) {
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for (int j = 0; j < inner_count; ++j) {
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out_offset = inner_count * i + j;
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for (int k = 0; k < mid_count; ++k) {
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x_offset = (inner_count * mid_count) * i + inner_count * k + j;
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functor(x_data + x_offset, x_grad_data + x_offset,
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out_data + out_offset, out_grad_data + out_offset,
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mid_count);
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}
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}
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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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