chunk_eval_op.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 <set>
#include <string>
#include <vector>

#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;

template <typename DeviceContext, typename T>
class ChunkEvalKernel : public framework::OpKernel<T> {
 public:
  struct Segment {
    int begin;
    int end;
    int type;
    bool operator==(const Segment& y) const {
      return begin == y.begin && end == y.end && type == y.type;
    }
  };

  void GetSegments(const int64_t* label, int length,
                   std::vector<Segment>* segments, int num_chunk_types,
                   int num_tag_types, int other_chunk_type, int tag_begin,
                   int tag_inside, int tag_end, int tag_single) const {
    segments->clear();
    segments->reserve(length);
    int chunk_start = 0;
    bool in_chunk = false;
    int tag = -1;
    int type = other_chunk_type;
    for (int i = 0; i < length; ++i) {
      int prev_tag = tag;
      int prev_type = type;
      PADDLE_ENFORCE_LE(label[i], num_chunk_types * num_tag_types);
      tag = label[i] % num_tag_types;
      type = label[i] / num_tag_types;
      if (in_chunk && ChunkEnd(prev_tag, prev_type, tag, type, other_chunk_type,
                               tag_begin, tag_inside, tag_end, tag_single)) {
        Segment segment{
            chunk_start,  // begin
            i - 1,        // end
            prev_type,
        };
        segments->push_back(segment);
        in_chunk = false;
      }
      if (ChunkBegin(prev_tag, prev_type, tag, type, other_chunk_type,
                     tag_begin, tag_inside, tag_end, tag_single)) {
        chunk_start = i;
        in_chunk = true;
      }
    }
    if (in_chunk) {
      Segment segment{
          chunk_start,  // begin
          length - 1,   // end
          type,
      };
      segments->push_back(segment);
    }
  }

  bool ChunkEnd(int prev_tag, int prev_type, int tag, int type,
                int other_chunk_type, int tag_begin, int tag_inside,
                int tag_end, int tag_single) const {
    if (prev_type == other_chunk_type) return false;
    if (type == other_chunk_type) return true;
    if (type != prev_type) return true;
    if (prev_tag == tag_begin) return tag == tag_begin || tag == tag_single;
    if (prev_tag == tag_inside) return tag == tag_begin || tag == tag_single;
    if (prev_tag == tag_end) return true;
    if (prev_tag == tag_single) return true;
    return false;
  }

  bool ChunkBegin(int prev_tag, int prev_type, int tag, int type,
                  int other_chunk_type, int tag_begin, int tag_inside,
                  int tag_end, int tag_single) const {
    if (prev_type == other_chunk_type) return type != other_chunk_type;
    if (type == other_chunk_type) return false;
    if (type != prev_type) return true;
    if (tag == tag_begin) return true;
    if (tag == tag_inside) return prev_tag == tag_end || prev_tag == tag_single;
    if (tag == tag_end) return prev_tag == tag_end || prev_tag == tag_single;
    if (tag == tag_single) return true;
    return false;
  }

  void Compute(const framework::ExecutionContext& context) const override {
    // initialize to parse configurations
    int num_chunk_types, num_tag_types;
    int other_chunk_type;
    int tag_begin, tag_inside, tag_end, tag_single;
    std::vector<Segment> label_segments;
    std::vector<Segment> output_segments;
    std::set<int> excluded_chunk_types;

    if (context.Attr<std::string>("chunk_scheme") == "IOB") {
      num_tag_types = 2;
      tag_begin = 0;
      tag_inside = 1;
      tag_end = -1;
      tag_single = -1;
    } else if (context.Attr<std::string>("chunk_scheme") == "IOE") {
      num_tag_types = 2;
      tag_begin = -1;
      tag_inside = 0;
      tag_end = 1;
      tag_single = -1;
    } else if (context.Attr<std::string>("chunk_scheme") == "IOBES") {
      num_tag_types = 4;
      tag_begin = 0;
      tag_inside = 1;
      tag_end = 2;
      tag_single = 3;
    } else if (context.Attr<std::string>("chunk_scheme") == "plain") {
      num_tag_types = 1;
      tag_begin = -1;
      tag_inside = -1;
      tag_end = -1;
      tag_single = -1;
    } else {
      PADDLE_THROW("Unknown chunk scheme.");
    }
    other_chunk_type = num_chunk_types = context.Attr<int>("num_chunk_types");
    excluded_chunk_types.insert(
        context.Attr<std::vector<int>>("excluded_chunk_types").begin(),
        context.Attr<std::vector<int>>("excluded_chunk_types").end());

    auto* inference = context.Input<LoDTensor>("Inference");
    auto place = inference->place();
    auto* label = context.Input<LoDTensor>("Label");
    auto* precision = context.Output<Tensor>("Precision");
    auto* recall = context.Output<Tensor>("Recall");
    auto* f1 = context.Output<Tensor>("F1-Score");
    auto* num_infer_chunks = context.Output<Tensor>("NumInferChunks");
    auto* num_label_chunks = context.Output<Tensor>("NumLabelChunks");
    auto* num_correct_chunks = context.Output<Tensor>("NumCorrectChunks");

    const int64_t* inference_data = inference->data<int64_t>();
    const int64_t* label_data = label->data<int64_t>();
    T* precision_data = precision->mutable_data<T>(place);
    T* racall_data = recall->mutable_data<T>(place);
    T* f1_data = f1->mutable_data<T>(place);
    int64_t* num_infer_chunks_data =
        num_infer_chunks->mutable_data<int64_t>(place);
    int64_t* num_label_chunks_data =
        num_label_chunks->mutable_data<int64_t>(place);
    int64_t* num_correct_chunks_data =
        num_correct_chunks->mutable_data<int64_t>(place);
    *num_infer_chunks_data = 0;
    *num_label_chunks_data = 0;
    *num_correct_chunks_data = 0;

    auto lod = label->lod();
    bool use_padding = lod.empty();
    int num_sequences = 0;

    if (use_padding) {
      auto dim1 = inference->dims()[1];
      auto* seq_length_t = context.Input<Tensor>("SeqLength");
      auto* seq_length_data = seq_length_t->data<int64_t>();
      num_sequences = seq_length_t->dims()[0];

      for (int i = 0; i < num_sequences; ++i) {
        int seq_length = seq_length_data[i];
        EvalOneSeq(inference_data + i * dim1, label_data + i * dim1, seq_length,
                   &output_segments, &label_segments, num_infer_chunks_data,
                   num_label_chunks_data, num_correct_chunks_data,
                   num_chunk_types, num_tag_types, other_chunk_type, tag_begin,
                   tag_inside, tag_end, tag_single, excluded_chunk_types);
      }
    } else {
      PADDLE_ENFORCE_EQ(lod.size(), 1UL,
                        "Only support one level sequence now.");
      PADDLE_ENFORCE(lod == inference->lod(),
                     "LoD must be same between Inference and Label.");
      num_sequences = lod[0].size() - 1;

      for (int i = 0; i < num_sequences; ++i) {
        int seq_length = lod[0][i + 1] - lod[0][i];
        EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i],
                   seq_length, &output_segments, &label_segments,
                   num_infer_chunks_data, num_label_chunks_data,
                   num_correct_chunks_data, num_chunk_types, num_tag_types,
                   other_chunk_type, tag_begin, tag_inside, tag_end, tag_single,
                   excluded_chunk_types);
      }
    }

    *precision_data = !(*num_infer_chunks_data)
                          ? 0
                          : static_cast<T>(*num_correct_chunks_data) /
                                (*num_infer_chunks_data);
    *racall_data = !(*num_label_chunks_data)
                       ? 0
                       : static_cast<T>(*num_correct_chunks_data) /
                             (*num_label_chunks_data);
    *f1_data = !(*num_correct_chunks_data)
                   ? 0
                   : 2 * (*precision_data) * (*racall_data) /
                         ((*precision_data) + (*racall_data));
  }

  void EvalOneSeq(const int64_t* output, const int64_t* label, int length,
                  std::vector<Segment>* output_segments,
                  std::vector<Segment>* label_segments,
                  int64_t* num_output_segments, int64_t* num_label_segments,
                  int64_t* num_correct, int num_chunk_types, int num_tag_types,
                  int other_chunk_type, int tag_begin, int tag_inside,
                  int tag_end, int tag_single,
                  const std::set<int>& excluded_chunk_types) const {
    GetSegments(output, length, output_segments, num_chunk_types, num_tag_types,
                other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
    GetSegments(label, length, label_segments, num_chunk_types, num_tag_types,
                other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
    size_t i = 0, j = 0;
    while (i < output_segments->size() && j < label_segments->size()) {
      if (output_segments->at(i) == label_segments->at(j) &&
          excluded_chunk_types.count(output_segments->at(i).type) != 1) {
        ++(*num_correct);
      }
      if (output_segments->at(i).end < label_segments->at(j).end) {
        ++i;
      } else if (output_segments->at(i).end > label_segments->at(j).end) {
        ++j;
      } else {
        ++i;
        ++j;
      }
    }
    for (auto& segment : (*label_segments)) {
      if (excluded_chunk_types.count(segment.type) != 1) {
        ++(*num_label_segments);
      }
    }
    for (auto& segment : (*output_segments)) {
      if (excluded_chunk_types.count(segment.type) != 1) {
        ++(*num_output_segments);
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle