sequence2batch.h

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#pragma once
#include <algorithm>
#include <vector>

#include "lite/core/context.h"
#include "lite/core/tensor.h"
#include "lite/fluid/eigen.h"
#include "lite/fluid/lod.h"
#include "lite/utils/paddle_enforce.h"

namespace paddle {
namespace lite {
namespace x86 {
namespace math {

template <typename T,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenMatrix = lite::fluid::EigenMatrix<T, MajorType, IndexType>;

template <lite::TargetType Target, typename T>
class CopyMatrixRowsFunctor {
 public:
  // If is_src_index is true,
  // copy the indexed rows of input src to the output dst.
  // If is_src_index is false,
  // copy the input src to the indexed rows of output dst.
  // The indexed rows are based on the input index.
  void operator()(const lite::Context<Target>& context,
                  const lite::Tensor& src,
                  std::vector<size_t> index_lod,
                  lite::Tensor* dst,
                  bool is_src_index);
};

template <lite::TargetType Target, typename T>
class LoDTensor2BatchFunctor {
  // Calculate the length of each sequence and
  // sort sequence index by the length.
  // example:  sequences = {s0, s1, s2}
  //           s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
  //           seq_info[3] = {(4, 5, 1), (0, 4, 0), (9, 3, 2)}
  //
  struct SeqInfo {
    SeqInfo(int start, int length, int seq_idx)
        : start(start), length(length), seq_idx(seq_idx) {}
    int start;
    int length;
    int seq_idx;
  };

 public:
  void operator()(const lite::Context<Target>& context,
                  const lite::Tensor& lod_tensor,
                  lite::Tensor* batch,
                  bool is_cal_batch_lod,
                  bool is_reverse = false) const {
    if (!is_cal_batch_lod) {
      auto lods = batch->lod();
      PADDLE_ENFORCE_GT(lods.size(),
                        2UL,
                        "The LoD of LoDTensor should inlcude at least 2-level "
                        "sequence information.");
      PADDLE_ENFORCE_EQ(
          lods[1].size(),
          static_cast<size_t>(lod_tensor.dims()[0]),
          "The LoD information should be consistent with the dims.");
      CopyMatrixRowsFunctor<Target, T> to_batch;
      to_batch(context, lod_tensor, lods[1], batch, true);
      return;
    }

    auto lods = lod_tensor.lod();
    PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");

    const auto& lod = lods[0];

    std::vector<SeqInfo> seq_info;
    for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
      int length = lod[seq_id + 1] - lod[seq_id];
      seq_info.emplace_back(lod[seq_id], length, seq_id);
    }

    std::sort(seq_info.begin(), seq_info.end(), [](SeqInfo a, SeqInfo b) {
      return a.length > b.length;
    });

    // Calculate the start position of each batch.
    // example:  sequences = {s0, s1, s2}
    //           s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
    //           max_seqlen = 5,
    //           batchIndex = {b0, b1, b2, b3, b4}
    //           b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
    //           batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
    //              batch_start_positions[0] = len(b0)
    //              batch_start_positions[1] = len(b0) + len(b1)
    //              batch_start_positions[2] = len(b0) + len(b1) + len(b2)
    //              ...
    //           seq2batch_idx[12] = {4, 0, 9,
    //                                5, 1, 10,
    //                                6, 2, 11,
    //                                7, 3,
    //                                8}
    //           seq_order = {1, 0, 2}, the sort order.
    //               where 1 is the second sequence,
    //                     0 is the first sequence,
    //                     2 is the third sequence.
    // The max_seqlen represents batch size after rearranging the
    // input LodTensor. It is also the maximum length of input sequence.

    lite::LoD batch_lods;
    batch_lods.emplace_back(std::vector<size_t>{0});
    batch_lods.emplace_back(std::vector<size_t>{0});
    batch_lods.emplace_back(std::vector<size_t>{0});

    // batch_lods[0] is the start positions for batch LoDTensor
    int max_seqlen = seq_info[0].length;
    batch_lods[0].resize(static_cast<size_t>(max_seqlen + 1));
    // batch_lods[1] is the raw index in the input LoDTensor
    batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
    // batch_lods[2] is the sort order for the input LoDTensor.
    batch_lods[2].resize(seq_info.size());

    size_t* batch_starts = batch_lods[0].data();
    size_t* seq2batch_idx = batch_lods[1].data();
    batch_starts[0] = 0;
    for (int n = 0; n < max_seqlen; n++) {
      auto batch_id = static_cast<int>(batch_starts[n]);
      for (size_t i = 0; i < seq_info.size(); ++i) {
        int seq_len = seq_info[i].length;
        int start = seq_info[i].start;
        if (n < seq_len) {
          seq2batch_idx[batch_id] =
              is_reverse ? start + seq_len - 1 - n : start + n;
          batch_id++;
        } else {
          break;
        }
      }
      batch_starts[n + 1] = static_cast<size_t>(batch_id);
    }
    size_t* seq_order = batch_lods[2].data();
    for (size_t i = 0; i < seq_info.size(); ++i) {
      seq_order[i] = seq_info[i].seq_idx;
    }
    batch->set_lod(batch_lods);

    CopyMatrixRowsFunctor<Target, T> to_batch;
    to_batch(context, lod_tensor, batch_lods[1], batch, true);
  }
};

template <lite::TargetType Target, typename T>
class Batch2LoDTensorFunctor {
 public:
  void operator()(const lite::Context<Target>& context,
                  const lite::Tensor& batch,
                  lite::Tensor* lod_tensor) const {
    auto in_lod = batch.lod();
    PADDLE_ENFORCE_GT(in_lod.size(),
                      2UL,
                      "The LoD of LoDTensor should inlcude at least 2-level "
                      "sequence information.");
    PADDLE_ENFORCE_EQ(
        in_lod[1].size(),
        static_cast<size_t>(lod_tensor->dims()[0]),
        "The LoD information should be consistent with the dims.");
    CopyMatrixRowsFunctor<Target, T> to_seq;
    to_seq(context, batch, in_lod[1], lod_tensor, false);
  }
};

}  // namespace math
}  // namespace x86
}  // namespace lite
}  // namespace paddle