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110 lines
4.1 KiB
110 lines
4.1 KiB
// Copyright (c) 2018 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 <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/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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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenScalar = framework::EigenScalar<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 DeviceContext, typename T, size_t D, size_t R_D,
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typename Functor>
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void ReduceFunctor(const DeviceContext& context, const framework::Tensor& input,
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framework::Tensor* output, const std::vector<int>& dims,
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bool keep_dim) {
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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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auto reduce_dim = Eigen::array<int, R_D>();
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std::vector<int> dims_ref = dims;
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for (size_t i = 0; i < dims_ref.size(); ++i) {
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if (dims_ref[i] < 0) dims_ref[i] = x_rank + dims_ref[i];
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reduce_dim[i] = dims_ref[i];
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}
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// construct the squeezed output tensor
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DDim out_dims = output->dims();
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if (keep_dim && x_rank > 1) {
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const int kDelFlag = -2;
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auto dims_vector = framework::vectorize(out_dims);
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for (size_t i = 0; i < dims_ref.size(); ++i) {
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dims_vector[dims_ref[i]] = kDelFlag;
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}
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dims_vector.erase(remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
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dims_vector.end());
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out_dims = framework::make_ddim(dims_vector);
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}
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auto& place = *context.eigen_device();
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Functor functor;
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if (D == 1) {
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auto out = EigenScalar<T>::From(*output);
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functor(place, &x, &out, reduce_dim);
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} else {
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auto out = EigenTensor<T, (D - R_D)>::From(*output, out_dims);
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functor(place, &x, &out, reduce_dim);
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}
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}
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template <typename DeviceContext, typename T, size_t D, typename Functor>
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void ReduceGradFunctor(const DeviceContext& context,
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const framework::Tensor& input0,
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const framework::Tensor& input1,
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const framework::Tensor& input2,
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framework::Tensor* output,
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const std::vector<int>& dims) {
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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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auto x_dims = input0.dims();
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auto reduced_dims_v = framework::vectorize(x_dims);
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std::vector<int> dims_ref = dims;
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Eigen::array<int, D> broadcast_dim;
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for (size_t i = 0; i < D; ++i) broadcast_dim[i] = 1;
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int broad_cats_times = 1;
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for (size_t i = 0; i < dims_ref.size(); ++i) {
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if (dims_ref[i] < 0) {
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dims_ref[i] = x_rank + dims_ref[i];
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}
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reduced_dims_v[dims_ref[i]] = 1;
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broadcast_dim[dims_ref[i]] = x_dims[dims_ref[i]];
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broad_cats_times *= x_dims[dims_ref[i]];
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}
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auto reduced_dims = framework::make_ddim(reduced_dims_v);
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auto x_reduce = EigenTensor<T, D>::From(input1, reduced_dims);
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auto x_reduce_grad = EigenTensor<T, D>::From(input2, reduced_dims);
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auto& place = *context.eigen_device();
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Functor functor;
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functor(place, &x, &x_reduce, &x_grad, &x_reduce_grad, broadcast_dim,
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broad_cats_times);
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}
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} // namespace operators
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} // namespace paddle
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