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125 lines
4.2 KiB
125 lines
4.2 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 <utility>
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#include <vector>
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#include "paddle/fluid/framework/tensor.h"
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namespace paddle {
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namespace operators {
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namespace math {
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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 DeviceContext, typename T, size_t D>
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void PadFunction(const framework::ExecutionContext& context,
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const std::vector<int>& pads, const framework::Tensor& src,
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T pad_value, framework::Tensor* out) {
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Eigen::array<std::pair<int, int>, D> paddings;
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for (size_t i = 0; i < paddings.size(); ++i) {
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paddings[i].first = pads[i * 2];
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paddings[i].second = pads[i * 2 + 1];
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}
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auto src_tensor = EigenTensor<T, D>::From(src);
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auto out_tensor = EigenTensor<T, D>::From(*out);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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out_tensor.device(place) = src_tensor.pad(paddings, pad_value);
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}
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template <typename DeviceContext, typename T, size_t D>
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void PadGradFunction(const framework::ExecutionContext& context,
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const std::vector<int>& pads, const framework::Tensor& src,
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framework::Tensor* d_out) {
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Eigen::array<std::pair<int, int>, D> paddings;
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for (size_t i = 0; i < paddings.size(); ++i) {
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paddings[i].first = -pads[i * 2];
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paddings[i].second = -pads[i * 2 + 1];
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}
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auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
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auto src_tensor = EigenTensor<T, D>::From(src);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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d_out_tensor.device(place) = src_tensor.pad(paddings, 0);
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}
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template <typename DeviceContext, typename T>
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void PaddingFunctor(int rank, const framework::ExecutionContext& context,
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const std::vector<int>& pads, T pad_value,
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const framework::Tensor& src, framework::Tensor* out) {
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switch (rank) {
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case 1:
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PadFunction<DeviceContext, T, 1>(context, pads, src, pad_value, out);
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break;
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case 2:
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PadFunction<DeviceContext, T, 2>(context, pads, src, pad_value, out);
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break;
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case 3:
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PadFunction<DeviceContext, T, 3>(context, pads, src, pad_value, out);
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break;
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case 4:
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PadFunction<DeviceContext, T, 4>(context, pads, src, pad_value, out);
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break;
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case 5:
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PadFunction<DeviceContext, T, 5>(context, pads, src, pad_value, out);
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break;
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case 6:
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PadFunction<DeviceContext, T, 6>(context, pads, src, pad_value, out);
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break;
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default:
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PADDLE_THROW(
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"PadOp only support tensors with no more than 6 dimensions.");
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}
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}
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template <typename DeviceContext, typename T>
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void PaddingGradFunctor(int rank, const framework::ExecutionContext& context,
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const std::vector<int>& pads,
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const framework::Tensor& src, framework::Tensor* out) {
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switch (rank) {
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case 1:
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PadGradFunction<DeviceContext, T, 1>(context, pads, src, out);
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break;
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case 2:
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PadGradFunction<DeviceContext, T, 2>(context, pads, src, out);
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break;
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case 3:
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PadGradFunction<DeviceContext, T, 3>(context, pads, src, out);
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break;
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case 4:
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PadGradFunction<DeviceContext, T, 4>(context, pads, src, out);
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break;
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case 5:
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PadGradFunction<DeviceContext, T, 5>(context, pads, src, out);
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break;
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case 6:
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PadGradFunction<DeviceContext, T, 6>(context, pads, src, out);
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break;
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default:
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PADDLE_THROW(
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"PadOp only support tensors with no more than 6 dimensions.");
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}
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}
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} // namespace math
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} // namespace operators
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} // namespace paddle
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