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132 lines
4.1 KiB
132 lines
4.1 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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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 Place, typename T, size_t D>
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void PadFunction(const framework::ExecutionContext& context) {
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auto pads = context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
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Eigen::array<std::pair<int, int>, D> paddings;
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for (int i = 0; i < pads.size(); ++i) {
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paddings[i] = pads[i];
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}
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T pad_value = context.op_.GetAttr<T>("pad_value");
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auto* X = context.Input<Tensor>("X");
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auto* Out = context.Output<Tensor>("Out");
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Out->mutable_data<T>(context.GetPlace());
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auto dims = X->dims();
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auto X_tensor = EigenTensor<T, D>::From(*X);
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auto Out_tensor = EigenTensor<T, D>::From(*Out);
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auto place = context.GetEigenDevice<Place>();
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Out_tensor.device(place) = X_tensor.pad(paddings, pad_value);
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}
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template <typename Place, typename T>
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class PadKernel : 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 dim = context.Input<Tensor>("X")->dims().size();
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switch (dim) {
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case 1:
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PadFunction<Place, T, 1>(context);
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break;
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case 2:
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PadFunction<Place, T, 2>(context);
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break;
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case 3:
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PadFunction<Place, T, 3>(context);
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break;
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case 4:
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PadFunction<Place, T, 4>(context);
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break;
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case 5:
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PadFunction<Place, T, 5>(context);
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break;
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case 6:
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PadFunction<Place, T, 6>(context);
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break;
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default:
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LOG(ERROR) << "Only ranks up to 6 supported.";
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}
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}
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};
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template <typename Place, typename T, size_t D>
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void PadGradFunction(const framework::ExecutionContext& context) {
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auto pads = context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
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Eigen::array<std::pair<int, int>, D> paddings;
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for (int i = 0; i < pads.size(); ++i) {
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paddings[0].first = -paddings[0].first;
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paddings[1].second = -paddings[1].second;
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}
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auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
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dX->mutable_data<T>(context.GetPlace());
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auto dX_tensor = EigenTensor<T, D>::From(*dX);
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auto dOut_tensor = EigenTensor<T, D>::From(*dOut);
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auto place = context.GetEigenDevice<Place>();
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dX_tensor.device(place) = dOut_tensor.pad(paddings, 0);
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}
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template <typename Place, typename T>
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class PadGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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size_t dim =
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context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
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switch (dim) {
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case 1:
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PadGradFunction<Place, T, 1>(context);
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break;
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case 2:
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PadGradFunction<Place, T, 2>(context);
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break;
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case 3:
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PadGradFunction<Place, T, 3>(context);
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break;
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case 4:
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PadGradFunction<Place, T, 4>(context);
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break;
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case 5:
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PadGradFunction<Place, T, 5>(context);
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break;
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case 6:
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PadGradFunction<Place, T, 6>(context);
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break;
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default:
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LOG(ERROR) << "Only ranks up to 6 supported.";
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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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