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56 lines
1.7 KiB
56 lines
1.7 KiB
7 years ago
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/* Copyright (c) 2016 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 "paddle/framework/lod_tensor.h"
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namespace paddle {
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namespace framework {
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// LoD Rank Table stores the `level` of `lod` which is ordered by sequence
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// length in descending order. It is useful when implement dynamic RNN and is
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// shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice
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// output operators.
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//
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// The table item contains two element. The length of sequence and the index of
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// sequence in that level.
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//
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// LoDRankTable also stores the coarse_lod, which is the lod information whose
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// level is less than input level, in order to restore the output LoD
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// information.
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class LoDRankTable {
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public:
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struct TableItem {
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size_t index;
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size_t length;
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};
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LoDRankTable() {}
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void Reset(const LoD& lod, size_t level);
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const std::vector<TableItem>& items() const { return this->items_; }
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const LoD& coarse_lod() const { return this->coarse_lod_; }
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size_t level() const { return coarse_lod_.size(); }
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private:
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LoD coarse_lod_;
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std::vector<TableItem> items_;
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};
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} // namespace framework
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} // namespace paddle
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