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127 lines
4.3 KiB
127 lines
4.3 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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using LoDTensor = framework::LoDTensor;
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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 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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enum SeqPoolType {
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AVERAGE = 0,
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SUM = 1,
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SQRT = 2, // square_root_n
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MAX = 3,
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LAST = 4,
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FIRST = 5
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};
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template <typename Place, typename T>
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class SequencePoolKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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int strategy = context.Attr<int>("strategy");
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auto dims = in->dims();
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auto lod = in->lod();
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int64_t w = in->numel() / dims[0];
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// InferShape by lod
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PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
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PADDLE_ENFORCE_GE(
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dims[0],
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/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
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"The first dimension of Input(X) must be large than batch size.");
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dims[0] = lod[0].size() - 1;
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out->Resize({dims});
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auto lod_level_0 = lod[0];
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out->mutable_data<T>(context.GetPlace());
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auto place = context.GetEigenDevice<Place>();
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for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
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Tensor in_t = in->Slice<T>(static_cast<int>(lod_level_0[i]),
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static_cast<int>(lod_level_0[i + 1]));
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Tensor out_t = out->Slice<T>(i, i + 1);
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int64_t h = static_cast<int64_t>(lod_level_0[i + 1] - lod_level_0[i]);
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auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
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auto out_e = EigenVector<T>::Flatten(out_t);
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switch (strategy) {
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case AVERAGE:
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out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
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break;
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case SUM:
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out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
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break;
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default:
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PADDLE_THROW("unsupported pooling strategy");
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}
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}
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}
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};
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template <typename Place, typename T>
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class SequencePoolGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
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int strategy = context.Attr<int>("strategy");
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auto dims = in->dims();
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auto lod = in->lod()[0];
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int64_t w = in->numel() / dims[0];
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in_g->mutable_data<T>(context.GetPlace());
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auto place = context.GetEigenDevice<Place>();
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for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
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auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[i]),
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static_cast<int>(lod[i + 1]));
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auto out_g_t = out_g->Slice<T>(i, i + 1);
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int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
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auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
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auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
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Eigen::DSizes<int, 2> bcast(h, 1);
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switch (strategy) {
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case AVERAGE:
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in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
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break;
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case SUM:
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in_g_e.device(place) = (out_g_e).broadcast(bcast);
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break;
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default:
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PADDLE_THROW("unsupported pooling strategy");
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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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