beam_search_kernel.cpp 8.6 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261
/* Copyright (c) 2018 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. */

#ifdef BEAM_SEARCH_OP

#include "operators/kernel/beam_search_kernel.h"
#include <numeric>

namespace paddle_mobile {
namespace operators {

template <typename Device, typename T>
class BeamSearchFunctor {
 public:
  void operator()(const framework::LoDTensor *pre_ids,
                  const framework::LoDTensor *pre_scores,
                  const framework::LoDTensor *ids,
                  const framework::LoDTensor *scores,
                  framework::LoDTensor *selected_ids,
                  framework::LoDTensor *selected_scores,
                  framework::Tensor *parent_idx, size_t level, size_t beam_size,
                  int end_id, bool is_accumulated) {
    auto abs_lod = framework::ToAbsOffset(scores->lod());
    auto &high_level = abs_lod[level];

    auto items = SelectTopBeamSizeItems(pre_ids, pre_scores, ids, scores, level,
                                        beam_size, end_id, is_accumulated);
    auto selected_items = ToMap(items, high_level.back());

    PruneEndBeams(pre_ids, abs_lod, &selected_items, level, end_id);
    // calculate the output tensor's height
    size_t num_instances = std::accumulate(
        std::begin(selected_items), std::end(selected_items), 0,
        [](size_t a, std::vector<Item> &b) { return a + b.size(); });
    // the output tensor shape should be [num_instances, 1]
    auto dims = framework::make_ddim(
        std::vector<int64_t>({static_cast<int>(num_instances), 1}));
    selected_ids->Resize(dims);
    selected_scores->Resize(dims);
    parent_idx->Resize({static_cast<int64_t>(num_instances)});

    auto *selected_ids_data = selected_ids->mutable_data<int64_t>();
    auto *selected_scores_data = selected_scores->mutable_data<float>();
    auto *parent_idx_data = parent_idx->mutable_data<int>();

    // fill in data
    std::vector<size_t> low_level;
    size_t low_offset = 0;
    for (auto &items : selected_items) {
      low_level.push_back(low_offset);
      for (auto &item : items) {
        parent_idx_data[low_offset] = static_cast<int>(low_level.size() - 1);
        selected_ids_data[low_offset] = item.id;
        selected_scores_data[low_offset] = item.score;
        low_offset++;
      }
    }
    low_level.push_back(low_offset);

    // fill lod
    framework::LoD lod(2);
    lod[0].assign(high_level.begin(), high_level.end());
    lod[1].assign(low_level.begin(), low_level.end());
    selected_ids->set_lod(lod);
    selected_scores->set_lod(lod);
  }

  /*
   * The basic items help to sort.
   */
  struct Item {
    Item() {}
    Item(size_t offset, size_t id, float score)
        : offset(offset), id(id), score(score) {}
    // offset in the higher lod level.
    size_t offset;
    // prefix id in the lower lod level.
    // size_t prefix;
    // the candidate id
    size_t id;
    // the corresponding score
    float score;

    inline bool operator<(const Item &in) const {
      return (score < in.score) ||
             ((score == in.score) && (offset < in.offset));
    }

    inline void operator=(const Item &in) {
      offset = in.offset;
      id = in.id;
      score = in.score;
    }
  };

 protected:
  /*
   * Prune the source sentences all branchs finished, and it is optional.
   * Pruning must one step later than finishing (thus pre_ids is needed here),
   * since the end tokens must be writed out.
   */
  void PruneEndBeams(const framework::LoDTensor *pre_ids,
                     const framework::LoD &abs_lod,
                     std::vector<std::vector<Item>> *items, size_t lod_level,
                     int end_id) {
    auto *pre_ids_data = pre_ids->data<int64_t>();
    auto &high_level = abs_lod[lod_level];
    for (size_t src_idx = 0; src_idx < high_level.size() - 1; ++src_idx) {
      size_t src_prefix_start = high_level[src_idx];
      size_t src_prefix_end = high_level[src_idx + 1];
      bool finish_flag = true;
      for (size_t offset = src_prefix_start; offset < src_prefix_end;
           offset++) {
        for (auto &item : items->at(offset)) {
          if (item.id != static_cast<size_t>(end_id) ||
              pre_ids_data[offset] != end_id) {
            finish_flag = false;
            break;
          }
        }
        if (!finish_flag) break;
      }
      if (finish_flag) {  // all branchs of the beam (source sentence) end and
                          // prune this beam
        for (size_t offset = src_prefix_start; offset < src_prefix_end;
             offset++)
          items->at(offset).clear();
      }
    }
  }

  /*
   * Transform the items into a map whose key is offset, value is the items.
   * NOTE low performance.
   */
  std::vector<std::vector<Item>> ToMap(
      const std::vector<std::vector<Item>> &items, size_t element_num) {
    std::vector<std::vector<Item>> result;
    result.resize(element_num);
    for (auto &entries : items) {
      for (const auto &item : entries) {
        result[item.offset].push_back(item);
      }
    }
    return result;
  }

  void Insert(std::vector<Item> *top_beam_ptr, const Item &item,
              size_t beam_size) {
    std::vector<Item> &top_beam = *top_beam_ptr;

    size_t num_beams = top_beam.size();
    if (num_beams < beam_size) {
      top_beam.resize(num_beams + 1);
      num_beams++;
    } else {
      if (item < top_beam[beam_size - 1]) {
        return;
      }
    }

    for (int k = static_cast<int>(num_beams) - 2; k >= 0; --k) {
      if (top_beam[k] < item) {
        top_beam[k + 1] = top_beam[k];
      } else {
        top_beam[k + 1] = item;
        return;
      }
    }
    top_beam[0] = item;
  }

  /*
   * For each source, select top beam_size records.
   */
  std::vector<std::vector<Item>> SelectTopBeamSizeItems(
      const framework::LoDTensor *pre_ids,
      const framework::LoDTensor *pre_scores, const framework::LoDTensor *ids,
      const framework::LoDTensor *scores, size_t lod_level, size_t beam_size,
      int end_id, bool is_accumulated) {
    std::vector<std::vector<Item>> result;

    // find the current candidates
    auto abs_lod = framework::ToAbsOffset(scores->lod());

    auto *pre_ids_data = pre_ids->data<int64_t>();
    auto *pre_scores_data = pre_scores->data<float>();

    auto *ids_data = ids ? ids->data<int64_t>() : nullptr;
    auto *scores_data = scores->data<float>();

    size_t num_seqs = scores->NumElements(lod_level);
    size_t seq_width = 1;
    for (int i = 1; i < scores->dims().size(); i++) {
      seq_width *= scores->dims()[i];
    }

    for (size_t seq_id = 0; seq_id < num_seqs; ++seq_id) {
      size_t seq_offset_start = abs_lod[lod_level][seq_id];
      size_t seq_offset_end = abs_lod[lod_level][seq_id + 1];

      std::vector<Item> top_beam;
      top_beam.reserve(beam_size);

      for (size_t offset = seq_offset_start; offset < seq_offset_end;
           ++offset) {
        auto pre_id = pre_ids_data[offset];
        auto pre_score = pre_scores_data[offset];
        if (pre_id == end_id) {
          // Allocate all probability mass to end_id for finished branchs and
          // the other candidate ids can be ignored.
          Item item(offset, end_id, pre_score);
          Insert(&top_beam, item, beam_size);
        } else {
          size_t index = offset * seq_width;
          for (size_t d = 0; d < seq_width; d++, index++) {
            int64_t id = ids_data ? ids_data[index] : static_cast<int64_t>(d);
            float score = is_accumulated
                              ? scores_data[index]
                              : pre_score + std::log(scores_data[index]);
            Item item(offset, id, score);
            Insert(&top_beam, item, beam_size);
          }
        }
      }

      result.emplace_back(top_beam);
    }

    return result;
  }
};

template <>
bool BeamSearchKernel<CPU, float>::Init(BeamSearchParam<CPU> *param) {
  return true;
}

template <>
void BeamSearchKernel<CPU, float>::Compute(const BeamSearchParam<CPU> &param) {
  BeamSearchFunctor<CPU, float> alg;
  alg(param.pre_ids_, param.pre_scores_, param.ids_, param.scores_,
      param.selected_ids_, param.selected_scores_, param.parent_idx_,
      param.level_, param.beam_size_, param.end_id_, param.is_accumulated_);
}

}  // namespace operators
}  // namespace paddle_mobile

#endif